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Timeline of Humbert Gonzalez Diaz

2018
Nov
09
Published new article




Article

MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochond...

Published: 09 November 2018 by American Chemical Society (ACS) in Journal of Chemical Information and Modeling

doi: 10.1021/acs.jcim.8b00631

Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and non-linear nano-quantitative structure-toxicity relationship-based artificial neural network (ANN-QSTR) models were setup using the fractal dimensions calculated from CNTs as source of structural complex-geometrical information to predict the potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays. The attained experimental data suggest that CNTs have high ability to inhibit the F0-ATPase active-binding site following the order: oxidizedCNT (CNTCOOH > CNTOH) > pristineCNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile the performance of the ANN-QSTR models was found to be improved by including different non-linear combinations of the calculated fractal Scanning Electron Microscopy (SEM) nano-descriptors, leading to models with excellent internal accuracy and predictivity on external data. Finally, the present study can contribute towards the rational-design of carbon nanomaterials and opens new opportunities towards mitochondrial nanotoxicology-based in silico models.

0 Reads | 0 Citations
2018
Sep
21
Published new article




Article

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

Published: 21 September 2018 by American Chemical Society (ACS) in ACS Combinatorial Science

doi: 10.1021/acscombsci.8b00090

Determining the target proteins of new anti-cancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (cj). In fact, ChEMBL database contains outcomes of 65534 different anticancer activity preclinical assays for 35565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of cj formed from >70 different biological activity parameters (c0), >300 different drug targets (c1), >230 cell lines (c2), and 5 organisms of assay (c3) and/or organisms of the target (c4). It include a total of 45833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex dataset with multiple Big Data features. This data is hard to be rationalized by researchers in order to extract useful relationships and predict new compounds. In this context, we propose to combine Perturbation Theory (PT) ideas and Machine Learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL dataset of preclinical assays of anti-cancer compounds. This is a simple linear model with only three variables. The model presented values of Area Under Receiver Operating Curve = AUROC = 0.872, Specificity = Sp(%) = 90.2, Sensitivity = Sn(%) = 70.6, and overall Accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multi-condition moving averages to capture all the complexity of the dataset. We also compared the model with non-linear Artificial Neural Network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anti-cancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anti-cancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.

0 Reads | 1 Citations
2018
Aug
23
Published new article




Article

Perturbation Theory–Machine Learning Study of Zeolite Materials Desilication

Published: 23 August 2018 by American Chemical Society (ACS) in Journal of Chemical Information and Modeling

doi: 10.1021/acs.jcim.8b00383

Zeolites are important materials for research and industrial applications. Mesopores are often introduced by desilication but other properties are also affected, making its optimization difficult. In this work, we demonstrate that Perturbation Theory and Machine Learning can be combined in a PTML multioutput model describing the effects of desilication. The PTML model achieves a notable accuracy (R2 = 0.98) in the external validation and can be useful for the rational design of novel materials.

1 Reads | 0 Citations
2018
May
23
Published new article




Article

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-P...

Published: 23 May 2018 by American Chemical Society (ACS) in ACS Chemical Neuroscience

doi: 10.1021/acschemneuro.8b00083

Predicting Drug-Protein Interactions (DPIs) for target proteins involved in Dopamine pathways is very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex Big Data sets of preclinical assays reported in public databases. This includes multiple conditions of assay like different experimental parameters, biological assays, target proteins, cell lines, organism of the target, organism of assay, etc. On the other hand, Perturbation Theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previous known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL dataset of preclinical assays of compounds targeting Dopamine pathway proteins. The best PTML model found predicts 50000 cases with Accuracy 70 - 91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple non-linear methods like ANN, Random Forest, Deep Learning, etc. Some of the non-linear methods outperform the linear model but at the cost of a notable increment on the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of PLG Peptido-mimetic compounds. In addition, we performed a molecular Docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC50, EC50, Ki, etc.) for compounds in assays involving different cell lines used, organisms of the protein target, and/or organism of assay, for proteins in Dopamine pathway.

5 Reads | 0 Citations
2018
Mar
02
Published new article






<strong>Precision Medicine: Carbon Nanotubes as Potential Treatment for Human Brain Disorders-Based Mitochondrial Dysfun...

Published: 02 March 2018 by MDPI AG in MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition

doi: 10.3390/mol2net-04-05130

<p>The study of key molecular mechanisms of mitochondrial dysfunctions, which are responsible for neurodegenerative diseases, is a critical step to assist for the diagnosis and therapy success. In this regard, we suggest an alternative of treatment on neurodegenerative disorders-based on Single-Walled Carbon Nanotubes (SWCNT) as potential mito protective -(Phe)-F0-ATPase targeting nanoparticles toward Precision Molecular Nanomedicine against pathological ATP-hydrolysis conditions. Herein, we used <em>ab initio</em> computational simulation to analyze the structural and electronic properties from SWCNT-family with zigzag topologies (n, m - Hamada indices n &gt; 0; m = 0) like: SWCNT-pristine, SWCNT-COOH, SWCNT-OH, SWCNT-monovacancy interacting with the critical (Phe)-residues of the mitochondrial F0-ATPase and using oligomycin A (specific Phe-F0-ATPase inhibitor) as reference control. Then, we show that the SWCNT-family can be potentially used to selectively inhibit the (Phe)-F0-ATPase activity liked to pathological mitochondrial ATP-hydrolysis associated to human neurodegenerative disorders by using DFT-<em>ab initio</em> simulation. The <em>in-silico</em> results suggest the formation of more stable complexes of interaction following the order: SWCNT-COOH/F0-ATPase complex (1.79 eV) &gt; SWCNT-OH/F0-ATPase complex (0.61 eV) &gt; SWCNT/F0-ATPase complex (0.45 eV) &gt; SWCNT-monovacancy/F0-ATPase complex (0.43 eV) based on the strength of the chemisorption interactions. These theoretical evidences open new horizons towards mito-target precision nanomedicine.</p>

54 Reads | 0 Citations
2017
Oct
16
Published new article




Article

Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channe...

Published: 16 October 2017 by Springer Nature in Scientific Reports

doi: 10.1038/s41598-017-13691-8

The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r2 > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73-98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available at https://doi.org/10.6084/m9.figshare.4802320.v2 .

5 Reads | 0 Citations
2017
May
31
Published new article




Article

A study of the Immune Epitope Database for some fungi species using network topological indices

Published: 31 May 2017 by Springer Nature in Molecular Diversity

doi: 10.1007/s11030-017-9749-4

In the last years, the encryption of system structure information with different network topological indices has been a very active field of research. In the present study, we assembled for the first time a complex network using data obtained from the Immune Epitope Database for fungi species, and we then considered the general topology, the node degree distribution, and the local structure of this network. We also calculated eight node centrality measures for the observed network and compared it with three theoretical models. In view of the results obtained, we may expect that the present approach can become a valuable tool to explore the complexity of this database, as well as for the storage, manipulation, comparison, and retrieval of information contained therein.

0 Reads | 0 Citations
2017
May
01
Published new article




Article

QSPR/QSAR-based Perturbation Theory approach and mechanistic electrochemical assays on carbon nanotubes with optimal pro...

Published: 01 May 2017 by Elsevier BV in Carbon

doi: 10.1016/j.carbon.2017.01.002

In the present study, different in vitro and electrochemical protocols were employed to determine the mitoprotective properties of carbon nanotubes family (pristine-CNT, oxidized-CNT) based on free radical scavenging ability against the most aggressive reactive oxygen species (ROS) as hydroxyl radical (·OH) formed by Fenton-Haber-Weiss reaction, which was experimentally induced on isolated rat-liver mitochondria through Fe2+ ions overload. The results suggest that the mitochondrial Fenton-inhibition response involves a significant reduction of (·OH) concentration linked to iron-complexing ability of CNT-family, following the order: carboxylated-CNT > pristine-CNT ∼ hydroxylated-CNT, without affecting the electrochemical mitochondrial membrane potential in Fe2+-overloaded mitochondria. Besides, a new in silico dose-response QSPR-model was applied suggesting reliability for the CNT-dose-effect series predictions towards the mitochondrial Fenton ROS-inhibition with excellent linear behavior on the training set (R2 = 0.901; R2(adj.) = 0.901; Q2(LOO-CV) = 0.901) and test set (Q2F1 = 0.9008; Q2F2 = 0.9008; Q2F3 = 0.9009; MAE = 21.213) for internal and external validation respectively, with p < 0.05 for all regression coefficient for > 70,000 data points. Lastly, these experimental and theoretical evidences open a gate to the rational design of novel carbon nanomaterials toward mitochondrial nanomedicine based redox-targeting as an alternative of treatment of several chronic diseases where pathological Fenton-reaction mechanisms have been directly involved.

8 Reads | 3 Citations
2017
Apr
25
Published new article




Article

Experimental–Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Le...

Published: 25 April 2017 by American Chemical Society (ACS) in Journal of Chemical Information and Modeling

doi: 10.1021/acs.jcim.6b00458

The study of selective toxicity of carbon nanotubes (CNT) on mitochondria (CNT-mitotoxicity) is of major interest for future biomedical applications. In the current work, the mitochondrial oxygen consumption (E3) is measured under three experimental conditions by exposure to pristine and oxidized CNTs (hydroxylated and carboxylated). Respiratory functional assays showed that the information of the CNT-Raman spectroscopy could be useful to predict structural parameters of mitotoxicity induced by CNTs. The in vitro functional assays show that the mitochondrial oxidative phosphorylation by ATP-synthase (or state V3 of respiration) was not perturbed in isolated rat-liver mitochondria. For the first time a Star Graph (SG) transform of the CNT Raman spectra is proposed in order to obtain the raw information for a nano-QSPR model. Box-Jenkins and Perturbation Theory Operators are used for the SG Shannon entropies. A modified RRegrs methodology is employed to test four regression methods such as Multiple Linear regression (LM), Partial Least Squares Regression (PLS), Neural Networks regression (NN), and Random Forest (RF). RF provides the best modelsto predict the mitochondrial oxygen consumption in the presence of specific CNTs with R2 of 0.998 - 0.999 and RMSE of 0.0068 - 0.0133 (training and test subsets). This work is aimed at demonstrating that the SG transform of Raman spectra is useful to encode CNT information, similarly to the SG transform of the blood proteome spectra in cancer or electroencephalogram in epilepsy and also as a prospective chemoinformatics tool for nano-risk assessment. All data files and R object models are available at https://dx.doi.org/10.6084/m9.figshare.3472349.

5 Reads | 3 Citations
2017
Apr
01
Published new article




Article

Experimental study and Random Forest prediction model of microbiome cell surface hydrophobicity

Published: 01 April 2017 by Elsevier BV in Expert Systems with Applications

doi: 10.1016/j.eswa.2016.10.058

Highlights•Experimental study and prediction model of microbiome cell surface hydrophobicity.•Expected Measurement Moving Average – Machine Learning model to predict CSH.•Random Forest prediction model with 12 features and test R-squared of 0.992. AbstractThe cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation and pathogenesis. For the present in vitro fermentation experiment, the CSH of ruminal mixed microbes was considered, along with other data records of pH, ammonia-nitrogen concentration, and neutral detergent fibre digestibility, conditions of surface tension and specific surface area in two different time scales. A dataset of 170,707 perturbations of input variables, grouped into two blocks of data, was constructed. Next, Expected Measurement Moving Average – Machine Learning (EMMA-ML) models were developed in order to predict CSH after perturbations of all input variables. EMMA-ML is a Perturbation Theory method that combines the ideas of Expected Measurement, Box-Jenkins Operators/Moving Average, and Time Series Analysis. Seven regression methods have been tested: Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, Elastic Net regression, Neural Networks regression, and Random Forests (RF). The best regression performance has been obtained with RF (EMMA-RF model) with an R-squared of 0.992. The model analysis has shown that CSH values were highly dependent on the in vitro fermentation parameters of detergent fibre digestibility, ammonia – nitrogen concentration, and the expected values of cell surface hydrophobicity in the first time scale.

2 Reads | 3 Citations
2016
Dec
14
Published new article




Article

Data Analysis in Chemistry and Bio-Medical Sciences

Published: 14 December 2016 by MDPI in International Journal of Molecular Sciences

doi: 10.3390/ijms17122105

Note: In lieu of an abstract, this is an excerpt from the first page.Excerpt There is an increasing necessity for multidisciplinary collaborations in molecular science between experimentalists and theoretical scientists, as well as among theoretical scientists from different fields.

6 Reads | 2 Citations
2016
Nov
23
Published new article




Article

Chiral Brønsted Acid-Catalyzed Enantioselective α-Amidoalkylation Reactions: A Joint Experimental and Predictive Study

Published: 23 November 2016 by Wiley in ChemistryOpen

doi: 10.1002/open.201600120

Enamides with a free NH group have been evaluated as nucleophiles in chiral Brønsted acid-catalyzed enantioselective α-amidoalkylation reactions of bicyclic hydroxylactams for the generation of quaternary stereocenters. A quantitative structure–reactivity relationship (QSRR) method has been developed to find a useful tool to rationalize the enantioselectivity in this and related processes and to orient the catalyst choice. This correlative perturbation theory (PT)-QSRR approach has been used to predict the effect of the structure of the substrate, nucleophile, and catalyst, as well as the experimental conditions, on the enantioselectivity. In this way, trends to improve the experimental results could be found without engaging in a long-term empirical investigation.

5 Reads | 1 Citations
2016
Nov
15
Published new article






Editorial: MOL2NET 2016, International Conference Series on Multidisciplinary Sciences.

Published: 15 November 2016 by MDPI AG in MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition

doi: 10.3390/mol2net-02-00001

<p>Welcome Message</p> <p>We are glad to invite all colleagues worldwide to participate on a new International Conference Series. The official title of this conference series is MOL2NET International Conference Series on Multidisciplinary Sciences. MOL2NET (the conference running title) is the acronym of the lemma of the conference: From Molecules to Networks. This running title is inspired by the possibility of multidisciplinary collaborations in science between experimentalists and theoretical scientists.</p> <p>This is an International Conference Series to Foster Interdisciplinary Collaborations in Sciences with emphasis on Experimental Chemistry (all branches), Materials Science, Nanotechnology, Life Sciences, Medicine, and Healthcare, along with Data Analysis, Computer Sciences, Bioinformatics, Systems Biology, and Complex Networks Sciences.</p> <p>The Scientific Headquarters (HQs) of this conference series are in the Faculty of Science and Technology, University of Basque Country (UPV/EHU), Biscay. However, the idea of this multidisciplinary conference emerged from the melting pot formed as the result of multiple collaborations of professors from many centers worldwide.</p> <p>Locally, the founders and strongest supporters of the conference are professors endowed by IKERBASQUE, Basque Foundation for Sciences, professors from the two departments&nbsp;Department of Organic Chemistry I and&nbsp;Department of Organic Chemistry II of the University of Basque Country (UPV/EHU), and professors from the Department of Computer Sciences of the University of Coru&ntilde;a (UDC).</p> <p>In addition, professors / researchers from the&nbsp;Center for the Study of Biological Complexity of the Virginia Commonwealth University (VCU), USA, the Natural Resources Research Institute, of the&nbsp;University of Minnesota, USA,&nbsp;and many other institutions are also founders and supporters of this conference, please see full committees lists.</p> <p>The publication of communications will be online via the platform SciForum of the Editorial Molecular Diversity Preservation International (MDPI), with HQ in Basel, Switzerland, and Beijing -Wuhan, China. This year the second edition of MOL2NET is planed to be held from <strong>2016</strong>-Dec-05 to <strong>2017</strong>-Jan-25<span><strong></strong></span><strong></strong> (including interactive discussions, posts, comments, questions, and answers&nbsp;about papers in the online platform Sciforum). However, the online submission platform is open and the publication of communications will be asap upon acceptance, all the year. For more details, see Schedule &amp; Program&nbsp;page and to submit a communication use the Submission&nbsp;link. Remember, these are the dates for the online conference and not for the face-to-face workshops associated to the conference.</p> <p><span>MOL2NET Past Edition</span></p> <p>MOL2NET-01, the first edition of this conference series, was held in Dec 2015. This first conference attracted more than 100 papers and 300+ authors and/or committee members representing 30+ universities of 20+ countries. Some of the world top universities and centers represented in the lists of committee members and/or authors were: <strong>Harvard</strong> Medical School, Boston, USA; <strong>Stanford</strong> School of Medicine, USA; <strong>Virginia</strong> Commonwealth University (VCU), USA; University of <strong>Minnesota Duluth</strong>, MN, USA; Conservatoire National des Arts et M&eacute;tiers, <strong>CNAM Par&iacute;s</strong>, France; University of <strong>Pennsylvania</strong>, USA; <span>Miller School of Medicine, <em></em></span>University of <strong>Miami</strong>, USA; <span><strong>EMBL-EBI</strong> European Bioinformatics Institute</span>, Cambridge, UK; <strong>CAS</strong> Chinese Academy of Science, China; <strong>ZJU</strong> Zhejiang University, China.</p> <p><span>‪</span><span>Face-to-Face Associated Workshops (In person participation)<br /></span></p> <p>MOL2NET International Conferences Series on Multidisciplinary Sciences is a scientific conference that runs totally online at the SciForum platform promoted by the editorial of the Molecular Diversity Preservation Institute (MDPI), Basel, Switzerland. https://sciforum.net/conference/mol2net-02. Consequently, no physical presence is needed saving traveling costs. However multiple workshops associated to the conference run in person (face-to-face) at their organizing universities. This year our conference is the online host of many of these workshops:</p> <p>SRI-08 The 8th Annual Undergraduate Summer Research Symposium of Saint Thomas University, Miami, USA, Sept, 2016. Symposium of the Summer Research Institute (SRI), HQ Saint Thomas University (STU), Miami, FL, USA. Workshop supported by&nbsp;STE-TRAC&nbsp;Miami Dade College (MDC) grant, Chairperson&nbsp;Prof. David Quesada and Advisory Chairperson&nbsp;Prof. Humberto Gonzalez-Diaz (Online Publication).</p> <p><span>‪</span>IWMEDIC-04,&nbsp;IV International Workshop on Medical Imaging, Medical Coding, and Clinical Data Analysis of University of Coru&ntilde;a (UDC). The IWMEDIC-04 workshop will be held presentially at the&nbsp;University Hospital Complex of A Coru&ntilde;a (June, 20, 2016),&nbsp;Hospital M&eacute;dico Quir&uacute;rgico San Rafael (June, 21,2016),&nbsp;and Faculty of Computer Sciences,&nbsp;UDC (June, 20-22, 2016). The chairman of this workshop is the&nbsp;Chair Professor and Director of Department of Computer Sciences, UDC, Coru&ntilde;a, Spain Prof. Alejandro Pazos and Advisory Chairperson&nbsp;Prof. Humberto Gonzalez-Diaz (Online Publication).</p> <p><span>‪</span><span>UFIQOSYC-01‬</span> , 1st Young Scientist Workshop hold at the Department of Organic Chemistry II, University of Basque Country UPV/EHU.&nbsp;&nbsp;<span>This workshop&nbsp;have&nbsp;brought together early career researchers (postdocs and graduate students) from the area of organic chemistry and catalysis across the UFI QOSYC to exchange information and practice presenting their research work in a supportive scientific environment. Chairpersons&nbsp;Prof. Esther Lete, </span>Prof. Esther Dom&iacute;nguez P&eacute;rez,<span> and </span>Prof. Jose Luis Vicario</p> <p><span>‪</span>SUIWCS-01, Soochow University International Workshop Series on Computer Sciences. The SUIWCS-01&nbsp;workshop will be held presentially at the&nbsp;&nbsp;the School of Computer Science and Technology&nbsp;of Soochow University, PCR, China (Summer, 2016). The chairman of this workshop is the Chief of Department of Software Engineering and Professor of Computer Sciences, School of Computer Sciences and Technology, Soochow University (SUDA), Suzhou, China, Prof. Quan Liu .</p> <p>BMEICB-02&nbsp;Second Bioinformatics Meeting of The School of Bioinformatics Engineering, University of Talca, Talca, Chile, (Oct, 13-14, 2016). Advisory chair and connection with&nbsp;MOL2NET conference Prof. Julio Caballero</p> <p><span>‪</span><span>CIESABIO-01‬</span> , 2016, the Workshop Series on Biotechnology and Zoonotic Diseases of the CIESA, Center for Invetigations and Advanced Studies on Animal Health of the FMVZ Faculty of Medical Veterinary and Zootechnique, of the UAEMEX Autonomous University of the State of Mexico. The Chairperson of this workshop is the Prof. Esvieta Tenorio.</p> <p><span>MDPI JCR Journals Special Issues<br /></span><br />In parallel, the members of committees and/or authors are encouraged to edit special issues for different journals of the editorial MDPI (http://www.mdpi.com/). See, as example,&nbsp;the special issue on the International Journal of Molecular Sciences (IJMS),&nbsp;IF = 3.257), with 18 papers in total including papers from the conference, link: Special Issue on Data Analysis in Molecular Sciences. In order to send a proposal of&nbsp;associated workshop and/or special issue contact the chairperson of the conference and UPV/EHU Ikerbasque Professor Prof. H. Gonz&aacute;lez-D&iacute;az .</p> <p><span>MOL2NET People, Media Channels, and Social Networks</span></p> <p>We are uploading flyers&nbsp;and promotional videos (in different languages) to the MOL2NET accounts in different social networks such as: GOOGLE+ account with +50000 viewers; FACEBOOK group with +10000 followers; and&nbsp;TWITTER account @mol2net. In addition, we have uploaded topic-specific pages with lists of contacts of people related to the conference. In this page you can find people with research interests focused on one specific area such as Organic Chemistry, Computational Chemistry, Materials or Nanoscience, etc. In this sense, to contact people related to all areas of Chemistry you may visit&nbsp;&nbsp;[Section 01], but to contact people related to Organic Chemistry &amp; Medicinal Chemistry specifically (organic synthesis, catalysis, drug discovery, <em>etc</em>.) you can visit also the page Organic Chemistry People, as well as [Section 08].</p> <p><strong>NOTES for participants</strong><br /><br /> The MOL2NET conference is Totally Online; no physical presence is needed saving traveling costs. We accept experimental works, theoretical works, or experimental-theoretic works in the areas mentioned. Proceedings will be Published Online, Open Access, and <strong>Totally Free of Charges</strong> (no cost). Please, see the following instructions: (1) Read call for papers [Link], (2) Read&nbsp;[Instructions to Authors] and download template .doc file&nbsp;MOL2NET 2016 Microsoft Word template file, (3) Submit short communications (2-3 pages), reviews, papers, or videos: [Link]. For details about in person (face-to-face) participation on associated workshops contact the respective members of the local committees.</p> <p><!--AUTOGENERATED_COMMITTEE_START--> Conference Chairman <strong>Prof. Gonz&aacute;lez-D&iacute;az, H.</strong></p> <p>IKERBASQUE Prof., Ph.D., Pharm.Lic.,&nbsp;</p> <p>(1) Department of Organic Chemistry II,</p> <p>University of Basque Country (UPV/EHU),</p> <p>Campus Bizkaia, Basque Country, Spain.</p> <p>(2) IKERBASQUE, Basque Foundation for Science,</p> <p>Bilbao, Bizkaia, Basque Country, Spain.</p> <p>humberto.gonzalezdiaz@ehu.eus</p>

13 Reads | 0 Citations
2016
Oct
09
Published new article






<span>SRI-08: The 8th Annual Undergraduate Summer Research Symposium of Saint Thomas University</span>

Published: 09 October 2016 by MDPI AG in MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition

doi: 10.3390/mol2net-02-07001

<p><span>Dear&nbsp;colleagues we welcome you&nbsp;to the symposium of the Summer Research Institute (SRI)&nbsp;with Head Quarters (HQ) at Saint Thomas University (STU), and supported by Miami Dade College (MDC), Miami, Downtown, FL, USA . This is face-to-face (in person) workshop associated to and hosted online by MOL2NET-2, International Conference of Multidisciplinary Sciences, 2016, MDPI, Sciforum, Basel, Switzerland, with HQs, University of The Basque Country (UPV/EHU), and supported by IKERBASQUE, Basque Foundation for Science, Basque Country, Bilbao, Spain</span>. </p>

6 Reads | 0 Citations
2016
Oct
09
Published new article






<strong></strong><strong>IWMEDIC04: International Workshop in Medical Informatics and Integration of Clinical Data, Coru...

Published: 09 October 2016 by MDPI AG in MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition

doi: 10.3390/mol2net-02-09001

<p>This year the MOL2NET is the online host conference&nbsp;for IWMEDIC-04&nbsp;(see details on&nbsp;Section I). IWMEDIC-04 is the IV International Workshop on Medical Imaging, Medical Coding, and Clinical Data Analysis of University of Coru&ntilde;a (UDC). The IWMEDIC-04 workshop will be held presentially at the&nbsp;University Hospital Complex of A Coru&ntilde;a (June, 20, 2016),&nbsp;Hospital M&eacute;dico Quir&uacute;rgico San Rafael (June, 21,2016),&nbsp;and Faculty of Computer Sciences,&nbsp;UDC (June, 20-22, 2016). The chairman of this workshop is Prof. Alejandro Pazos; Ph.D., M.D., Chair and Director of Department of Computer Sciences, UDC, Coru&ntilde;a, Spain. The topics include, but are not limited to, Medical Imaging Processing, Medical Informatics, Medical Coding, Bioinformatics, Computer Aided Drug Desing, Data Analysis, <em>etc</em>. English will be the official language for online publication and presentations, as per MDPI rules, presential lectures may be in English, Spanish, or Galician.</p>

5 Reads | 0 Citations
2016
Oct
04
Published new article






<strong>Artificial Neural Network Schedulers for Food Webs</strong>

Published: 04 October 2016 by MDPI AG in MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition

doi: 10.3390/mol2net-02-05002

<p><em>In this work, we introduce by the first time a new type of algorithm aimed to predict the more promising topology of one ANN to be trained in order to model a given dataset of complex system. In so doing, we can quantify topological (connectivity) information of both the complex networks under study and a set of ANNs trained using Shannon measures. Using information parameters as inputs, we developed one scheduler for 338050 outputs of 10 different ANNs for the respective 33805 pair of nodes in 73 Biological Networks. The overall accuracy of the SANN-HPC schedulers found was of &gt;72% for Biological Networks; in training and validation series. </em></p>

8 Reads | 0 Citations
2016
Aug
05
Published new article




Article

Experimental-Theoretic Approach to Drug-Lymphocyte Interactome Networks with Flow Cytometry and Spectral Moments Perturb...

Published: 05 August 2016 in Current Pharmaceutical Design

doi:

We can combine experimental techniques like Flow Cytometry Analysis (FCA) with Chemoinformatics methods to predict the complex networks of interactions between organic compounds and targets in the immune system. In this work, we determined experimentally the values of EC50 = 17.82 μg/mL and Cytotoxicity = 20.6 % for the anti-microbial / anti-parasite drug Dermofural over Balb/C CD9 lymphocytes using flow cytometry. After that, we developed a new Perturbation-theory model for Drug-Cell Target Interactome in Lymphocytes based on dispersion-polarization moments of drug structure. The models correctly classifies 34591 out of 42715 (Accuracy = 80.9%) cases of perturbations in assay endpoints of 11492 drugs (including both train and validation series). Each endpoint correspond to one out of 2616 assays, 38 molecular and cellular targets, 77 standard type measures, in four possible (human and rodents).

8 Reads | 0 Citations
2016
Jul
27
Published new article




Article

Gastrointestinal Spatiotemporal mRNA Expression of Ghrelin vs Growth Hormone Receptor and New Growth Yield Machine Learn...

Published: 27 July 2016 by Springer Nature in Scientific Reports

doi: 10.1038/srep30174

The management of ruminant growth yield has economic importance. The current work presents a study of the spatiotemporal dynamic expression of Ghrelin and GHR at mRNA levels throughout the gastrointestinal tract (GIT) of kid goats under housing and grazing systems. The experiments show that the feeding system and age affected the expression of either Ghrelin or GHR with different mechanisms. Furthermore, the experimental data are used to build new Machine Learning models based on the Perturbation Theory, which can predict the effects of perturbations of Ghrelin and GHR mRNA expression on the growth yield. The models consider eight longitudinal GIT segments (rumen, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum), seven time points (0, 7, 14, 28, 42, 56 and 70 d) and two feeding systems (Supplemental and Grazing feeding) as perturbations from the expected values of the growth yield. The best regression model was obtained using Random Forest, with the coefficient of determination R(2) of 0.781 for the test subset. The current results indicate that the non-linear regression model can accurately predict the growth yield and the key nodes during gastrointestinal development, which is helpful to optimize the feeding management strategies in ruminant production system.

3 Reads | 2 Citations
2016
May
01
Published new article




Article

Experimental and chemometric studies of cell membrane permeability

Published: 01 May 2016 by Elsevier BV in Chemometrics and Intelligent Laboratory Systems

doi: 10.1016/j.chemolab.2016.03.010

Highlights•A model obtained to predict the changes on cell permeability when there are variations (perturbations) in many input variables over the two time scales.•Time Scale Analysis, Box-Jenkins Operators (BJO) and Covariance Perturbation Theory Operators (CPTOs) were considered.•A simulation of ternary phase diagram with predicted values of cell permeability at different experimental conditions were developed. AbstractCell membrane permeability (P) governs the molecules or ions to transport through the cell membrane. In this study, we measured P of ruminal microbes in different initial levels of surface tension (ST) and specific surface area (SSA). Data of P in present study and published data of pH, Ammonia concentration, digestibility of neutral detergent fibre and gas production in two time scales (tk and ˈtk) as input variables Vq(tk) were took into consideration for developing a predictive model. The ideas of Box-Jenkins Operators and Covariance Perturbation Theory Operators were used by the first time to establish a model to predict the variations of cellular permeability. The best model presented sensitivity, specificity and accuracy > 0.89, and MCC > 0.78 for 77,781 cases (Training + Validation series). In addition, we also reported a simulation of ternary phase diagram with predicted values of cell permeability at various experimental conditions.

3 Reads | 2 Citations
2016
Apr
19
Published new article




Article

ADMET-Multi-Output Cheminformatics Models for Drug Delivery, Interactomics, and Nanotoxicology.

Published: 19 April 2016 in Current Drug Delivery

doi:

ADMET Chemoinformatics multi-output models are useful for a parallel prediction of multiple experimental parameters related the absorption (A), distribution (D), metabolism (M), excretion (E), and toxicity (T) process of drugs, pollutants, and NPs with a single QSPR model. Here we present one state-of-art review about the different applications of multi-output QSPR models for ADMET process. Some of the models reviewed predict changes in ADMET properties for >3000 assays of and/or >30000 interactions between drugs and >100 targets (metabolizing enzymes, drug transporters, or organisms). Other models predict the self-aggregation of NP micelles of drugs and surfactants with implications in drug delivery and ADMET process. We also included a review of multi-output models for the cytotoxicity or ecotoxicity of NPs related to ADEMT and cell or environment delivery processes.

5 Reads | 0 Citations
2016
Apr
01
Published new article




Article

Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives

Published: 01 April 2016 by Elsevier BV in Neuropharmacology

doi: 10.1016/j.neuropharm.2015.12.019

The use of Cheminformatics tools is gaining importance in the field of translational research from Medicinal Chemistry to Neuropharmacology. In particular, we need it for the analysis of chemical information on large datasets of bioactive compounds. These compounds form large multi-target complex networks (drug-target interactome network) resulting in a very challenging data analysis problem. Artificial Neural Network (ANN) algorithms may help us predict the interactions of drugs and targets in CNS interactome. In this work, we trained different ANN models able to predict a large number of drug-target interactions. These models predict a dataset of thousands of interactions of central nervous system (CNS) drugs characterized by > 30 different experimental measures in >400 different experimental protocols for >150 molecular and cellular targets present in 11 different organisms (including human). The model was able to classify cases of non-interacting vs. interacting drug-target pairs with satisfactory performance. A second aim focus on two main directions: the synthesis and assay of new derivatives of TVP1022 (S-analogues of rasagiline) and the comparison with other rasagiline derivatives recently reported. Finally, we used the best of our models to predict drug-target interactions for the best new synthesized compound against a large number of CNS protein targets.

4 Reads | 12 Citations
2016
Mar
19
Published new article




Article

QSPR-Perturbation Models for the Prediction of B-Epitopes from Immune Epitope Database: A Potentially Valuable Route for...

Published: 19 March 2016 by Springer Nature in International Journal of Peptide Research and Therapeutics

doi: 10.1007/s10989-016-9524-x

5 Reads | 1 Citations
2016
Feb
01
Published new article




Article

Chemometric approach to fatty acid metabolism-distribution networks and methane production in ruminal microbiome

Published: 01 February 2016 by Elsevier BV in Chemometrics and Intelligent Laboratory Systems

doi: 10.1016/j.chemolab.2015.11.008

Highlights•Methane emission has attracted more and more attention by nutrition and environmental scientists.•We can develop a Chemometric methodology to integrate different parallel laboratory experiments.•We can use Chemometrics method to study perturbations in experimental data of methane production and fatty acid distribution networks due to changes in experimental conditions.•We combined Perturbation Theory (PT), Linear Free-Energy Relationships (LFER), Linear Discriminant Analysis (LDA), to develop non-linear PT-LFER models of perturbations in Methane production – fatty acid distribution networks.•We can use Artificial Neural Networks (ANNs) to develop non-linear PT-NLFER models of perturbations in Methane production – fatty acid distribution network. AbstractMethane emission has been attracting more and more attention. Unfortunately, a lot of factors influence the methane emission (chemical structure of metabolites, time, methane, gas pressure, microbiome composition, diet, etc.). We propose a new chemometric methodology to integrate different laboratory experiments in this field. Firstly, we report (1) new laboratory experiments to measure by separating (1a) methane production (gas phase), (1b) volatile fatty acid (VFA) distribution (liquid phase) and (1c) fatty acid (FA) distribution in rumen microbiome. Next, we also report the new (2) chemometric methodology that integrates all the data in a single theoretical model. The laboratory work includes two experimental sections (a) to measure the methane production, pH, gas pressure, temperature and (b) FA distribution. The section (b) include two different experimental parts chromatographic determination of internal peak areas (IPA%) of (b.1) long chain fatty acids (LCFA) and (b.2) VFA. In all studies, we can use different treatments, distribution phase (media, bacteria, or protozoan microbiome), cis/trans patterns, experimental protocols, etc. Next, we combined Perturbation Theory (PT), Linear Free-Energy Relationships (LFER), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANNs) to develop linear and non-linear models of perturbations in methane production – fatty acid distribution network. The best PT-LFER model found presented values of sensitivity, specificity, and accuracy > 0.94, and Matthews correlation coefficient (MCC) > 0.894 for 545,695 cases of perturbations in experimental data. This methodology may be useful to quantify the effect of perturbations due to the changes in experimental conditions in the study of fatty acid distribution when we need to carry out parallel experiments in different phases.

4 Reads | 0 Citations
2016
Jan
01
Published new article




Article

Perturbation theory model of reactivity and enantioselectivity of palladium-catalyzed Heck–Heck cascade reactions

Published: 01 January 2016 by Royal Society of Chemistry (RSC) in RSC Advances

doi: 10.1039/C6RA08751E

A new multi-output PT-QSRR model to correlate and predict the enantioselectivity and yield of Heck–Heck cascade reactions has been developed. Enantioselective intramolecular Heck–Heck cascade reactions have emerged as an excellent tool for the construction of polycyclic frameworks, such as lycorane alkaloids, xestoquinone and analogues. However, it is particularly difficult to rationalize the effect of simultaneous changes in both the structure of many molecular entities and experimental conditions (temperature, time, solvent, ligand, catalyst loading, etc. ) on reactivity and enantioselectivity. In this work, a computational model to predict the enantiomeric excess and the yield of Heck–Heck cascade reactions has been developed. The model combines Perturbation Theory (PT) and Quantitative Structure-Reactivity Relationships (QSRR) ideas for the prediction of two different outputs with the same equation (% ee and % yield). This model predicted 520 experimental outcomes with a correlation coefficient of R = 0.89, standard error of estimates of SEE = 1.19%, and a cross-validation correlation coefficient of q2 = 0.79. The use of the model has been illustrated with a case study, the Heck–Heck cascade reaction of a 2,3-dialkenyl pyrrole using Pd(dba) 2 and ( R )-BINAP. For the first time, a 2000-points simulation in ternary phase diagrams shows the effect of the concentration of the catalyst, the base, and ligand on the enantioselectivity of this reaction. The QSRR model also predicts trends in structural outcomes, such as halides vs. triflates, or the ligand structure. Therefore, the model opens the door to the design of new chiral ligands and helps to find trends to improve the experimental results in enantioselective polyene cyclisations.

0 Reads | 2 Citations
2016
Jan
01
Published new article




Article

Predicting the binding properties of single walled carbon nanotubes (SWCNT) with an ADP/ATP mitochondrial carrier using ...

Published: 01 January 2016 by Royal Society of Chemistry (RSC) in RSC Advances

doi: 10.1039/C6RA08883J

Interactions between single walled carbon nanotubes (SWCNT) family with mitochondrial ADP/ATP carrier (ANT-1) were evaluated using constitutional and electronic nanodescriptors defined by ( n , m )-Hamada indexes (armchair, zig-zag and chiral). Interactions between the single walled carbon nanotube (SWCNT) family and a mitochondrial ADP/ATP carrier (ANT-1) were evaluated using constitutional (functional groups, number of carbon atoms, etc. ) and electronic nanodescriptors defined by ( n , m )-Hamada indexes (armchair, zig-zag and chiral). The Free Energy of Binding (FEB) was determined by molecular docking simulation and the results showed that FEB was statistically more negative ( p < 0.05), following the order SWCNT-COOH > SWCNT-OH > SWCNT, suggesting that polar groups favor the anchorage to ANT-1. In this regard, it was showed that key ANT-1 amino acids (Arg 79, Asn 87, Lys 91, Arg 187, Arg 234 and Arg 279) responsible for ADP-transport were conserved in ANT-1 from different species examined to predict SWCNT interactions, including shrimp Litopenaeus vannamei and fish Danio rerio commonly employed in ecotoxicology. The SWCNT-ANT-1 inter-atomic distances for the key ANT-1 amino acids were similar to that with carboxyatractyloside, a classical inhibitor of ANT-1. Significant linear relationships between FEB and n -Hamada index were found for zig-zag SWCNT and SWCNT-COOH ( R2 = 0.95 in both cases). A Perturbation Theory-Nano-Quantitative Structure-Binding Relationship (PT-NQSBR) model was fitted that was able to distinguish between strong (FEB < −14.7 kcal mol −1 ) and weak (FEB ≥ −14.7 kcal mol −1 ) SWCNT–ANT-1 interactions. A simple ANT-1-inhibition respiratory assay employing mitochondria suspension from L. vannamei , showed good accordance with the predicted model. These results indicate that this methodology can be employed in massive virtual screenings and used for making regulatory decisions in nanotoxicology.

3 Reads | 4 Citations
2016
Jan
01
Published new article




Article

Multi-output Model with Box-Jenkins Operators of Quadratic Indices for Prediction of Malaria and Cancer Inhibitors Targe...

Published: 01 January 2016 in Current Protein & Peptide Science

doi:

The ubiquitin-proteasome pathway (UPP) is the primary degradation system of short-lived regulatory proteins. Cellular processes such as the cell cycle, signal transduction, gene expression, DNA repair and apoptosis are regulated by this UPP and dysfunctions in this system have important implications in the development of cancer, neurodegenerative, cardiac and other human pathologies. UPP seems also to be very important in the function of eukaryote cells of the human parasites like Plasmodium falciparum, the causal agent of the neglected disease Malaria. Hence, the UPP could be considered as an attractive target for the development of compounds with Anti-Malarial or Anti-cancer properties. Recent online databases like ChEMBL contains a larger quantity of information in terms of pharmacological assay protocols and compounds tested as UPP inhibitors under many different conditions. This large amount of data give new openings for the computer-aided identification of UPP inhibitors, but the intrinsic data diversity is an obstacle for the development of successful classifiers. To solve this problem here we used the Bob-Jenkins moving average operators and the atom-based quadratic molecular indices calculated with the software TOMOCOMD-CARDD (TC) to develop a quantitative model for the prediction of the multiple outputs in this complex dataset. Our multi-target model can predict results for drugs against 22 molecular or cellular targets of different organisms with accuracies above 70% in both training and validation sets.

2 Reads | 0 Citations
2015
Dec
05
Published new article






Editorial: MOL2NET 2015, International Conference on Multidisciplinary Sciences.

Published: 05 December 2015 by MDPI AG in MOL2NET, International Conference on Multidisciplinary Sciences

doi: 10.3390/MOL2NET-1-00001

<p>The full title of this conference is the MOL2NET International Conference on Multidisciplinary Sciences. This is an International Conference to Foster Interdisciplinary Collaborations in Experimental Chemistry (all branches), Medicine, Nanotechnology, Data Analysis, Bioinformatics, and Networks Sciences. MOL2NET (the conference's running title) is the acronym of the lemma of the conference: <strong>From Molecules to Networks</strong>. This running title is inspired by the possibility of multidisciplinary collaborations in science between experimentalists and theoretical scientists; represented disciplines will encompass the molecular and biomedical sciences, social networks analysis, and beyond. More specifically, this conference aims to promote scientific synergies between groups of experimental molecular and bio-medical scientists. Relevant fields include Chemistry, all areas (Inorganic Chemistry, Organic Chemistry, Medicinal Chemistry, Analytical Chemistry, Chemical Engineering), Pharmaceutical Sciences, Pharmacology, Cancer Research, OMICS, Neurosciences, Nanosciences, Materials Science, Medicine, and Biomedical Engineering, Cancer Research. Moreover, the conference welcomes computational and social sciences experts from different areas, such as Computational Chemistry, Bioinformatics, Networks Science, Social Networks analysis, Data and Computer Sciences, Predictive analytics, Biostatistics, <em>etc</em>. The Scientific Headquarters (HQs) are in the Faculty of Science and Technology, University of Basque Country (UPV/EHU), Bizkaia. The participation and publication of communications is online via the platform SciForum of the Editorial Molecular Diversity Preservation International (MDPI), with HQ in Basel, Switzerland, and Beijing - Wuhan, China.</p> <p>The conference per se is the result of the synergy between the Department of Organic Chemistry II, UPV/EHU, and IKERBASQUE, Basque Foundation for Sciences, with the Faculty of Informatics, University of Coru&ntilde;a (UDC). MDPI Sciforum platform (https://sciforum.net/) will publish accepted communications online. In parallel, we are editing one special issue for International Journal of Molecular Sciences (IJMS) journal of editorial MDPI (http://www.mdpi.com/). The revision process is totally independent, please contact the editorial if you are interested.</p> <p>We also invite all colleagues to share the conference website through social media. We are uploading flyers, conferences, and promotional videos in different languages to the MOL2NET accounts in different social networks.</p> <p>- GOOGLE+ account with +30000 viewers: http://bit.do/gmol2net<br /> - FACEBOOK group with +8000 followers: http://bit.do/fbmol2net<br /> - TWITTER account: @mol2net</p> <p>Sincerely yours,</p> <p>Conference Chair</p> <p>Prof. Humberto Gonzalez-Diaz, PhD., Pharm.Lic.<br /><br />IKERBASQUE Professor of Department of Organic Chemistry II, <br />University of Basque Country UPV/EHU, Campus Bizkaia<br />Chair Endowed by Basque Government/Eusko Jaurlaritza foundation<br />IKERBASQUE, Basque Foundation for Science, Bilbao, Bizkaia</p> <p></p>

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2015
Dec
04
Published new article






<strong>Bio-AIMS Chemoinformatics Web tools for proteins</strong>

Published: 04 December 2015 by MDPI AG in MOL2NET, International Conference on Multidisciplinary Sciences

doi: 10.3390/MOL2NET-1-F004

<p>The peptide biological screening represents a difficult task due to the complexity of the amino-acid sequences. One solution is the encoding of the molecular information using complex networks or graphs of the peptides into QSAR-like models in Web tools. Bio-AIMS contains free Web tools on an Artificial Intelligence Model Server in Biosciences: http://bio-aims.udc.es/TargetPred.php. These in silico peptide screening tools are implementing models to predict different protein activities, drug &ndash; protein and protein &ndash; protein interactions. The inputs are using 3D protein structures or 1D peptide amino acid sequences and the SMILES formulas for drugs, and the classification models are based on Machine Learning techniques. The Web tools are implemented using Python, PHP and XHTML programming languages.</p>

5 Reads | 0 Citations
2015
Dec
04
Published new article






<strong>Perturbation Theory Modeling of Intramolecular Carbolithiation Reactions</strong>

Published: 04 December 2015 by MDPI AG in MOL2NET, International Conference on Multidisciplinary Sciences

doi: 10.3390/MOL2NET-1-a013

<p>PT-QSRR models are Quantitative Structure-Reactivity Relationship (QSRR) models based on Perturbation Theory (PT) that may be useful for multi-objective optimization in organic synthesis. In this communication, we summarize some of the more important results and conclusions obtained in our previous research / review paper about PT-QSRR models published in <em>Curr. Top. Med. Chem., </em><strong>2013</strong>, <em>13</em>, (5), 1713-1741. I this previous work, firstly we reviewed general aspects and applications of both perturbation theory and QSPR models. Secondly, we formulate a general-purpose perturbation theory for multiple-boundary QSPR problems. In this previous work, we developed a new QSPR-Perturbation theory model that classify correctly &gt;100,000 pairs of intra-molecular carbolithiations with 75-95% of Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp). The model predicts probabilities of variations in the yield and enantiomeric excess of reactions due to at least one perturbation in boundary conditions (solvent, temperature, temperature of addition, or time of reaction). The model also account for changes in chemical structure (connectivity structure and/or chirality patterns in substrate, product, electrophile agent, organolithium, and ligand of the asymmetric catalyst).</p>

9 Reads | 0 Citations
2015
Nov
13
Published new article






<strong><span>Multi Activity QSAR Models for Anti- Parasite Drugs Using Markov Entropy Indices</span></strong>

Published: 13 November 2015 by MDPI AG in 2nd International Electronic Conference on Entropy and Its Applications

doi: 10.3390/ecea-2-B010

<p>There are many parasite species with very different antiparasite drugs susceptibility. Computational methods in biology and chemistry prediction of the biological activity based on Quantitative Structure-Activity Relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multi-species QSAR classification model (ms-QSAR). In so doing, we use Markov Chains theory to calculate new multi-target spectral moments to fit a QSAR model that predict by the first time a ms-QSAR model for 500 drugs tested in the literature against 16 parasite species and other 207 drugs no tested in the literature using entropy type indices. The data was processed by Artificial Neural Network (ANN) classifying drugs as active or non-active against the different tested parasite species. The best ANN found was MLP 23:23-18-1:1. Overall model classification accuracy was 85.65% (211/244 cases) in training. Validation of the model was carried out by means of external predicting series. In this serie, the model classified correctly 81.85% (275/357 cases).</p>

6 Reads | 0 Citations
2015
Nov
01
Published new article




Article

Multi-Target Mining of Alzheimer Disease Proteome with Hansch's QSBR-Perturbation Theory and Experimental-Theoretic Stud...

Published: 01 November 2015 in Curr Drug Targets

doi:

Hansch's model is a classic approach to Quantitative Structure-Binding Relationships (QSBR) problems in Pharmacology and Medicinal Chemistry. Hansch QSAR equations use as input parameters of electronic structure and lipophilicity. In this work, we perform a review on Hansch's analysis. We also developed a new type of PT-QSBR Hansch's model based on Perturbation Theory (PT) and QSBR approach for a large number of drugs reported in CheMBL. The targets are proteins expressed by the Hippocampus region of the brain of Alzheimer Disease (AD) patients. The model predicted correctly 49312 out of 53783 negative perturbations (Specificity = 91.7%) and 16197 out of 21245 positive perturbations (Sensitivity = 76.2%) in training series. The model also predicted correctly 49312/53783 (91.7%) and 16197/21245 (76.2%) negative or positive perturbations in external validation series. We applied our model in theoretical-experimental studies of organic synthesis, pharmacological assay, and prediction of unmeasured results for series of compounds similar to Rasagiline (compound of reference) with potential neuroprotection effect.

0 Reads | 1 Citations
2015
Oct
29
Published new article




Article

Self-Assembled Binary Nanoscale Systems: Multioutput Model with LFER-Covariance Perturbation Theory and an Experimental–...

Published: 29 October 2015 by American Chemical Society (ACS) in Langmuir

doi: 10.1021/acs.langmuir.5b03074

Studies of the self-aggregation of binary systems are of both theoretical and practical importance. They provide an opportunity to investigate the influence of the molecular structure of the hydrophobe on the nonideality of mixing. On the other hand, linear free energy relationship (LFER) models, such as Hansch’s equations, may be used to predict the properties of chemical compounds such as drugs or surfactants. However, the task becomes more difficult once we want to predict simultaneaously the effect over multiple output properties of binary systems of perturbations under multiple input experimental boundary conditions (bj). As a consequence, we need computational chemistry or chemoinformatics models that may help us to predict different properties of the autoaggregation process of mixed surfactants under multiple conditions. In this work, we have developed the first model that combines perturbation theory (PT) and LFER ideas. The model uses as input covariance PT operators (CPTOs). CPTOs are calculated as the difference between covariance ΔCov(iμk) functions before and after multiple perturbations in the binary system. In turn, covariances calculated as the product of two Box–Jenkins operators (BJO) operators. BJOs are used to measure the deviation of the structure of different chemical compounds from a set of molecules measured under a given subset of experimental conditions. The best CPT-LFER model found predicted the effects of 25 000 perturbations over 9 different properties of binary systems. We also reported experimental studies of different experimental properties of the binary system formed by sodium glycodeoxycholate and didodecyldimethylammonium bromide (NaGDC-DDAB). Last, we used our CPT-LFER model to carry out a 1000 data point simulation of the properties of the NaGDC-DDAB system under different conditions not studied experimentally.

2 Reads | 3 Citations
2015
Jun
01
Published new article




Article

Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and s...

Published: 01 June 2015 by Elsevier BV in Biosystems

doi: 10.1016/j.biosystems.2015.04.007

Using computational algorithms to design tailored drug cocktails for highly active antiretroviral therapy (HAART) on specific populations is a goal of major importance for both pharmaceutical industry and public health policy institutions. New combinations of compounds need to be predicted in order to design HAART cocktails. On the one hand, there are the biomolecular factors related to the drugs in the cocktail (experimental measure, chemical structure, drug target, assay organisms, etc.); on the other hand, there are the socioeconomic factors of the specific population (income inequalities, employment levels, fiscal pressure, education, migration, population structure, etc.) to study the relationship between the socioeconomic status and the disease. In this context, machine learning algorithms, able to seek models for problems with multi-source data, have to be used. In this work, the first artificial neural network (ANN) model is proposed for the prediction of HAART cocktails, to halt AIDS on epidemic networks of U.S. counties using information indices that codify both biomolecular and several socioeconomic factors. The data was obtained from at least three major sources. The first dataset included assays of anti-HIV chemical compounds released to ChEMBL. The second dataset is the AIDSVu database of Emory University. AIDSVu compiled AIDS prevalence for >2300 U.S. counties. The third data set included socioeconomic data from the U.S. Census Bureau. Three scales or levels were employed to group the counties according to the location or population structure codes: state, rural urban continuum code (RUCC) and urban influence code (UIC). An analysis of >130,000 pairs (network links) was performed, corresponding to AIDS prevalence in 2310 counties in U.S. vs. drug cocktails made up of combinations of ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4856 protocols, and 10 possible experimental measures. The best model found with the original data was a linear neural network (LNN) with AUROC>0.80 and accuracy, specificity, and sensitivity≈77% in training and external validation series. The change of the spatial and population structure scale (State, UIC, or RUCC codes) does not affect the quality of the model. Unbalance was detected in all the models found comparing positive/negative cases and linear/non-linear model accuracy ratios. Using synthetic minority over-sampling technique (SMOTE), data pre-processing and machine-learning algorithms implemented into the WEKA software, more balanced models were found. In particular, a multilayer perceptron (MLP) with AUROC=97.4% and precision, recall, and F-measure >90% was found.

1 Reads | 3 Citations
2015
Mar
10
Published new article




Article

Multi-output model with Box–Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin–proteaso...

Published: 10 March 2015 by Springer Nature in Molecular Diversity

doi: 10.1007/s11030-015-9571-9

The ubiquitin–proteasome pathway (UPP) plays an important role in the degradation of cellular proteins and regulation of different cellular processes that include cell cycle control, proliferation, differentiation, and apoptosis. In this sense, the disruption of proteasome activity leads to different pathological states linked to clinical disorders such as inflammation, neurodegeneration, and cancer. The use of UPP inhibitors is one of the proposed approaches to manage these alterations. On other hand, the ChEMBL database contains >5,000 experimental outcomes for >2,000 compounds tested as possible proteasome inhibitors using a large number of pharmacological assay protocols. All these assays report a large number of experimental parameters of biological activity like \(EC_{50}, IC_{50}\) , percent of inhibition, and many others that have been determined under many different conditions, targets, organisms, etc. Although this large amount of data offers new opportunities for the computational discovery of proteasome inhibitors, the complexity of these data represents a bottleneck for the development of predictive models. In this work, we used linear molecular indices calculated with the software TOMOCOMD-CARDD and Box–Jenkins moving average operators to develop a multi-output model that can predict outcomes for 20 experimental parameters in >450 assays carried out under different conditions. This generated multi-output model showed values of accuracy, sensitivity, and specificity above 70 % for training and validation series. Finally, this model is considered multi-target and multi-scale, because it predicts the inhibition of the UPP for drugs against 22 molecular or cellular targets of different organisms contained in the ChEMBL database.

2 Reads | 2 Citations
2015
Jan
01
Published new article




Article

Experimental and computational studies of fatty acid distribution networks

Published: 01 January 2015 by Royal Society of Chemistry (RSC) in Molecular BioSystems

doi: 10.1039/C5MB00325C

A new PT-LFER model is useful for predicting a distribution network in terms of specific fatty acid distribution. Unbalanced uptake of Omega 6/Omega 3 (ω-6/ω-3) ratios could increase chronic disease occurrences, such as inflammation, atherosclerosis, or tumor proliferation, and methylation methods for measuring the ruminal microbiome fatty acid (FA) composition/distribution play a vital role in discovering the contribution of food components to ruminant products ( e.g. , meat and milk) when pursuing a healthy diet. Hansch's models based on Linear Free Energy Relationships (LFERs) using physicochemical parameters, such as partition coefficients, molar refractivity, and polarizability, as input variables ( Vk ) are advocated. In this work, a new combined experimental and theoretical strategy was proposed to study the effect of ω-6/ω-3 ratios, FA chemical structure, and other factors over FA distribution networks in the ruminal microbiome. In step 1, experiments were carried out to measure long chain fatty acid (LCFA) profiles in the rumen microbiome (bacterial and protozoan), and volatile fatty acids (VFAs) in fermentation media. In step 2, the proportions and physicochemical parameter values of LCFAs and VFAs were calculated under different boundary conditions ( cj ) like c1 = acid and/or base methylation treatments, c2 = with/without fermentation, c3 = FA distribution phase (media, bacterial, or protozoan microbiome), etc. In step 3, Perturbation Theory (PT) and LFER ideas were combined to develop a PT-LFER model of a FA distribution network using physicochemical parameters ( Vk ), the corresponding Box–Jenkins (Δ Vkj ) and PT operators (ΔΔ Vkj ) in statistical analysis. The best PT-LFER model found predicted the effects of perturbations over the FA distribution network with sensitivity, specificity, and accuracy > 80% for 407 655 cases in training + external validation series. In step 4, alternative PT-LFER and PT-NLFER models were tested for training Linear and Non-Linear Artificial Neural Networks (ANNs). PT-NLFER models based on ANNs presented better performance but are more complicated than the PT-LFER model. Last, in step 5, the PT-LFER model based on LDA was used to reconstruct the complex networks of perturbations in the FA distribution and compared the giant components of the observed and predicted networks with random Erdős–Rényi network models. In short, our new PT-LFER model is a useful tool for predicting a distribution network in terms of specific fatty acid distribution.

0 Reads | 1 Citations
2015
Jan
01
Published new article




Article

Mitoprotective activity of oxidized carbon nanotubes against mitochondrial swelling induced in multiple experimental con...

Published: 01 January 2015 by Royal Society of Chemistry (RSC) in RSC Advances

doi: 10.1039/C5RA14435C

Mitochondrial Permeability Transition Pore (MPTP) is involved in neurodegeneration, hepatotoxicity, cardiac necrosis, nervous and muscular dystrophies. Mitochondrial Permeability Transition Pore (MPTP) is involved in neurodegeneration, hepatotoxicity, cardiac necrosis, nervous and muscular dystrophies. We used different experimental protocols to determine the mitoprotective activity (% P ) of different carbon nanotubes (CNT) against mitochondrial swelling in multiple boundary conditions ( bj ). The experimental boundary conditions explored included different sub-sets of combinations of the following factors b0 = three different mitochondrial swelling assays using the MPT-inductor (Ca 2+ , Fe 3+ , H 2 O 2 ) combined or not with a second MPT-inductor and swelling control assays using MPT-inhibitor (CsA, RR, EGTA), b1 = exposure time (0–600 s), and b2 = CNT concentrations (0–5 μg ml −1 ). Other boundary conditions ( bk ) changed were the CNT structural parameters b3 = CNT type (SW, SW + DW, MW), b4 = CNT functionalization type (H, OH, COOH). We also changed different of CNT like b5 = molecular weight/functionalization ratio ( minW / maxW ) or b6 = maximal and minimal diameter ( Dmin / Dmax ) as physic-chemical properties ( Vk ). Next, we employed chemoinformatics ideas to develop a new Perturbation Theory (PT) model able to predict the % P of CNT in multiple experimental conditions. We investigated different output functions of the absorbance ′ f ( εij ) used in PL4/PL5 methods like ( εij , 1/ εij , 1/ εij2 , or −log εij ) as alternative outputs of the model. The inputs are in the form an additive functions with linear/non-linear terms. The first term is a function 0f (〈 εij 〉) of the average absorbance 〈 εij 〉 (expected value) in different assays ( bj ). The concentration dependent terms are linear functions of concentration, or hill-shaped curves similar to PL4/PL5 functions (used in dose–response analysis). The CNT structure perturbation terms are linear/non-linear functions of Box–Jenkins operators (Δ Vkj ). The Δ Vkj are moving averages (deviations) of the Vk of the CNT with respect to their expected values 〈 Vkj 〉. The best model found predicted the values of absorbance (measure of mitoprotective activity vs. mitochondrial swelling) with regression coefficient R2 = 0.997 for >6000 experimental data points ( q2 = 0.994). Last, we used the model to carry out a simulation of the changes on mitoprotective activity for CNT family after one increase of 1–10% of the minWi and maxDi of CNT.

2 Reads | 2 Citations
2014
Dec
01
Published new article




Article

Computational ecotoxicology: Simultaneous prediction of ecotoxic effects of nanoparticles under different experimental c...

Published: 01 December 2014 by Elsevier BV in Environment International

doi: 10.1016/j.envint.2014.08.009

Nanotechnology has brought great advances to many fields of modern science. A manifold of applications of nanoparticles have been found due to their interesting optical, electrical, and biological/chemical properties. However, the potential toxic effects of nanoparticles to different ecosystems are of special concern nowadays. Despite the efforts of the scientific community, the mechanisms of toxicity of nanoparticles are still poorly understood. Quantitative-structure activity/toxicity relationships (QSAR/QSTR) models have just started being useful computational tools for the assessment of toxic effects of nanomaterials. But most QSAR/QSTR models have been applied so far to predict ecotoxicity against only one organism/bio-indicator such as Daphnia magna. This prevents having a deeper knowledge about the real ecotoxic effects of nanoparticles, and consequently, there is no possibility to establish an efficient risk assessment of nanomaterials in the environment. In this work, a perturbation model for nano-QSAR problems is introduced with the aim of simultaneously predicting the ecotoxicity of different nanoparticles against several assay organisms (bio-indicators), by considering also multiple measures of ecotoxicity, as well as the chemical compositions, sizes, conditions under which the sizes were measured, shapes, and the time during which the diverse assay organisms were exposed to nanoparticles. The QSAR-perturbation model was derived from a database containing 5520 cases (nanoparticle-nanoparticle pairs), and it was shown to exhibit accuracies of ca. 99% in both training and prediction sets. In order to demonstrate the practical applicability of our model, three different nickel-based nanoparticles (Ni) with experimental values reported in the literature were predicted. The predictions were found to be in very good agreement with the experimental evidences, confirming that Ni-nanoparticles are not ecotoxic when compared with other nanoparticles. The results of this study thus provide a single valuable tool toward an efficient prediction of the ecotoxicity of nanoparticles under multiple experimental conditions.

2 Reads | 17 Citations
2014
Nov
21
Published new article




Article

Computational Tool for Risk Assessment of Nanomaterials: Novel QSTR-Perturbation Model for Simultaneous Prediction of Ec...

Published: 21 November 2014 by American Chemical Society (ACS) in Environmental Science & Technology

doi: 10.1021/es503861x

Nanomaterials have revolutionized modern science and technology due to their multiple applications in engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of nanoparticles (NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and cytotoxicity assays are of special interest in order to determine the potential harmful effects of NPs. Processes based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense, alternative approaches such as quantitative structure-activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for the better understanding of the biological behavior of NPs that may be responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity separately against only one organism (bioindicator species or cell line) and have not reported information regarding the quantitative influence of characteristics other than composition or size. In this work, we developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents. The model was created from 36488 cases (NP-NP pairs) and exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict toxicities of several NPs that were not included in the original data set. The results of the predictions suggest that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.

4 Reads | 26 Citations
2014
Nov
01
Published new article




Article

Mapping networks of anti-HIV drug cocktails vs. AIDS epidemiology in the US counties

Published: 01 November 2014 by Elsevier BV in Chemometrics and Intelligent Laboratory Systems

doi: 10.1016/j.chemolab.2014.08.006

1 Reads | 0 Citations
2014
Oct
31
Published new article






Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

Published: 31 October 2014 by MDPI AG in The 18th International Electronic Conference on Synthetic Organic Chemistry

doi: 10.3390/ecsoc-18-e012

Alzheimer's Disease (AD), Parkinson, and other neurodegenerative diseases are a major health problem nowadays. In this sense, the discovery of new drugs for neurodiseases treatment is a goal of the major importance. Public databases, like ChEMBL, contain a large amount of data about multiplexing assays of inhibitors of a group of enzymes with special relevance in central nervous system. Mono Amino Oxidases (MAOs), Acetyl Cholinesterase (AChE), Glycogen Synthase Kinase-3 (GSK-3), AChE (AChE), and 5α-reductases (5αRs). This data conform an important information source for the application of multi-target computational models. However, almost all the computational models known focus in only one target. In this work, we developed mt-QSAR for inhibitors of 8 different enzymes promising in the treatment of different neurodiseases. In so doing, we combined by the first time the software DRAGON with Moving Average parameters with this objective. The best DRAGON model found predict with very high accuracy, specificity, and sensitivity &gt;90% a very large data set &gt;10000 cases in training and validation series. We also report experimental results about the assay of several 7H-benzo[e]perimidin-7-one derivatives as possible MAO-A inhibitors. Last, we used these compounds in the model to predict the activity of against other targets

7 Reads | 0 Citations
2014
Sep
24
Published new article




Article

Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoreti...

Published: 24 September 2014 by MDPI in International Journal of Molecular Sciences

doi: 10.3390/ijms150917035

In a multi-target complex network, the links (L(ij)) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K(i), K(m), IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

2 Reads | 7 Citations
2014
Jun
25
Published new article




Article

Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a ...

Published: 25 June 2014 by Royal Society of Chemistry (RSC) in Nanoscale

doi: 10.1039/c4nr01285b

Nowadays, the interest in the search for new nanomaterials with improved electrical, optical, catalytic and biological properties has increased. Despite the potential benefits that can be gathered from the use of nanoparticles, only little attention has been paid to their possible toxic effects that may affect human health. In this context, several assays have been carried out to evaluate the cytotoxicity of nanoparticles in mammalian cells. Owing to the cost in both resources and time involved in such toxicological assays, there has been a considerable increase in the interest towards alternative computational methods, like the application of quantitative structure–activity/toxicity relationship (QSAR/QSTR) models for risk assessment of nanoparticles. However, most QSAR/QSTR models developed so far have predicted cytotoxicity against only one cell line, and they did not provide information regarding the influence of important factors rather than composition or size. This work reports a QSTR-perturbation model aiming at simultaneously predicting the cytotoxicity of different nanoparticles against several mammalian cell lines, and also considering different times of exposure of the cell lines, as well as the chemical composition of nanoparticles, size, conditions under which the size was measured, and shape. The derived QSTR-perturbation model, using a dataset of 1681 cases (nanoparticle–nanoparticle pairs), exhibited an accuracy higher than 93% for both training and prediction sets. In order to demonstrate the practical applicability of our model, the cytotoxicity of different silica (SiO2), nickel (Ni), and nickel(II) oxide (NiO) nanoparticles were predicted and found to be in very good agreement with experimental reports. To the best of our knowledge, this is the first attempt to simultaneously predict the cytotoxicity of nanoparticles under multiple experimental conditions by applying a single unique QSTR model.

1 Reads | 25 Citations
2014
May
01
Published new article




Article

Markov mean properties for cell death-related protein classification

Published: 01 May 2014 by Elsevier BV in Journal of Theoretical Biology

doi: 10.1016/j.jtbi.2014.01.033

The cell death (CD) is a dynamic biological function involved in physiological and pathological processes. Due to the complexity of CD, there is a demand for fast theoretical methods that can help to find new CD molecular targets. The current work presents the first classification model to predict CD-related proteins based on Markov Mean Properties. These protein descriptors have been calculated with the MInD-Prot tool using the topological information of the amino acid contact networks of the 2423 protein chains, five atom physicochemical properties and the protein 3D regions. The Machine Learning algorithms from Weka were used to find the best classification model for CD-related protein chains using all 20 attributes. The most accurate algorithm to solve this problem was K*. After several feature subset methods, the best model found is based on only 11 variables and is characterized by the Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.992 and the true positive rate (TP Rate) of 88.2% (validation set). 7409 protein chains labeled with "unknown function" in the PDB Databank were analyzed with the best model in order to predict the CD-related biological activity. Thus, several proteins have been predicted to have CD-related function in Homo sapiens: 3DRX-involved in virus-host interaction biological process, protein homooligomerization; 4DWF-involved in cell differentiation, chromatin modification, DNA damage response, protein stabilization; 1IUR-involved in ATP binding, chaperone binding; 1J7D-involved in DNA double-strand break processing, histone ubiquitination, nucleotide-binding oligomerization; 1UTU-linked with DNA repair, regulation of transcription; 3EEC-participating to the cellular membrane organization, egress of virus within host cell, class mediator resulting in cell cycle arrest, negative regulation of ubiquitin-protein ligase activity involved in mitotic cell cycle and apoptotic process. Other proteins from bacteria predicted as CD-related are 2G3V - a CAG pathogenicity island protein 13 from Helicobacter pylori, 4G5A - a hypothetical protein in Bacteroides thetaiotaomicron, 1YLK-involved in the nitrogen metabolism of Mycobacterium tuberculosis, and 1XSV - with possible DNA/RNA binding domains. The results demonstrated the possibility to predict CD-related proteins using molecular information encoded into the protein 3D structure. Thus, the current work demonstrated the possibility to predict new molecular targets involved in cell-death processes.

0 Reads | 3 Citations
2014
Mar
18
Published new article




Article

LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as C...

Published: 18 March 2014 by Wiley in Molecular Informatics

doi: 10.1002/minf.201300027

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non‐annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH‐INSIDE software to calculate the Markov‐Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200 Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure‐Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio‐aims.udc.es/LECTINPred.php. This web server showed the following goodness‐of‐fit statistics: Sensitivity=96.7 % (for Ls), Specificity=87.6 % (non‐active proteins), and Accuracy=92.5 % (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top‐scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure‐prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design.

0 Reads | 1 Citations
2014
Feb
21
Published new article




Article

ANN Multiscale Model of Anti-HIV Drugs Activity vs AIDS Prevalence in the US at County Level Based on Information Indice...

Published: 21 February 2014 by American Chemical Society (ACS) in Journal of Chemical Information and Modeling

doi: 10.1021/ci400716y

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.

0 Reads | 17 Citations
2014
Feb
01
Published new article




Article

A QSPR-like model for multilocus genotype networks of Fasciola hepatica in Northwest Spain

Published: 01 February 2014 by Elsevier BV in Journal of Theoretical Biology

doi: 10.1016/j.jtbi.2013.11.005

Fasciola hepatica is a parasitic trematode that infects wild and domesticated mammals, particularly cattle and sheep, and causes significant economic losses to global livestock production. In the present study, we used codominant genetic markers to define and build, for the first time, complex genotype networks for F. hepatica isolated from cattle and sheep in NW Spain. We generated three types of random networks with a number of nodes and edges as close as possible to the observed networks, and we then calculated 14 node centrality measures for both observed and random networks. Finally, using Linear Discriminant Analysis (LDA) and these measures as inputs, we constructed a quantitative structure-property relationship (QSPR)-like model able to predict the propensity of a specific genotype of F. hepatica to infect different infrapopulations, farms and/or host species. The accuracy, sensitivity and specificity of the model were >90% for both training and cross-validation series. We also assessed the applicability domain of the model. This type of QSPR model is a potentially powerful tool for epidemiological studies and could be used to manage and prevent the spread of fasciolosis.

2 Reads | 2 Citations
2014
Jan
01
Published new article




Article

Editorial: Chemoinformatics in metabolomics, modeling chemical reactivity and ADMET processes part 1.

Published: 01 January 2014 in Curr. Drug Metab.

doi:

4 Reads | 0 Citations
2014
Jan
01
Published new article




Article

QSPR and flow cytometry analysis (QSPR-FCA): review and new findings on parallel study of multiple interactions of chemi...

Published: 01 January 2014 in Curr. Drug Metab.

doi:

The immune system helps to halt the infections caused by pathogenic microbial and parasitic agents. The ChEMBL database lists very large datasets of cytotoxicity of organic compounds but notably, a large number of compounds have unknown effects over molecular and cellular targets in the immune system. Flow Cytometry Analysis (FCA) is a very important technique to determine the effect of organic compounds over these molecular and cellular targets in the immune system. In addition, multi-target Quantitative Structure- Property Relationship (mt-QSPR) models can predict drug-target interactions, networks. The objectives of this paper are the following. Firstly, we carried out a review of general aspects and some examples of applications of FCA to study the effect of drugs over different cellular targets. However, we focused more on methods, materials, and experimental results obtained in previous works reported by our group in the study of the drug Dermofural. We also reviewed different mt-QSPR models useful to predict the immunotoxicity and/or the effects of drugs over immune system targets including immune cell lineages or proteins. Secondly, we included new results not published before. Initially, we used ChEMBL data to train and validate a new model but with emphasis in the effect of drugs over lymphocytes. Lastly, we report unpublished results of the computational and FCA study of a new nitro-vinyl-furan compound over thymic lymphocytes T helpers (CD4+) and T cytotoxic (CD8+) population.

4 Reads | 0 Citations
2014
Jan
01
Published new article




Article

Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic rea...

Published: 01 January 2014 in Curr. Drug Metab.

doi:

The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the effects of in-out perturbations in intra-molecular carbolithiations, drug ADME process, or self-aggregation of micelle nanoparticles of drugs or surfactants. The Accuracy (Ac), Sensitivity (Sn), and Specificity (Sp) of these models were >90% in all cases. The first model predicts variations in the yield or enantiomeric excess due to structural variations or changes in the solvent, temperature, temperature of addition, or time of reaction. The second model predicts changes in >18 parameters of biological effects for >3000 assays of ADME properties and/or interactions between 31,723 drugs and 100 targets (metabolizing enzymes, drug transporters, or organisms). The third model predicts perturbations due to changes in temperature, solvent, salt concentration, and/or structure of anions or cations in the self-aggregation of micelle nanoparticles of drugs and surfactants.

7 Reads | 0 Citations
2014
Jan
01
Published new article




Article

Nanocarriers & drug delivery: rational design and applications.

Published: 01 January 2014 in Current Topics in Medicinal Chemistry

doi:

3 Reads | 0 Citations
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