The use of plastics for packaging across many industries has become a standard, however, in recent years there has been an increase in demand for more eco-friendly packaging alternatives. In this vein, the use of plant proteins in film formation has been studied extensively.^1-2 Due to the strong mechanical properties obtained through the use of plant-based proteins, the biodegradability of such materials, and their economic efficiency, these proteins are of substantial interest in the effort to replace current synthetic packaging films.^{3 -5}

In our work, we focus on combining both experimental and computational techniques in order to develop protein-based films for food packaging applications. By combing the two techniques, we aim to better understand the interactions of plant-based proteins with selected plasticizing modifiers in order to develop films with optimal mechanical properties. In our work we compare the interactions of selected modifiers with soy protein and zein protein from corn. In this regard, we apply various computational techniques, including protein-ligand docking^6-7 and molecular modeling methods^8-9 to assess the interactions and then compare our finding with experimental data^1,3,10-11. By modeling how choice plasticizing modifiers interact differently with each protein, we aim to better formulate our films in order to achieve mechanical properties that compete with those of current synthetic packaging systems.

References

1. Calva-Estrada, S. J.; Jiménez-Fernández, M.; Lugo-Cervantes, E. Protein-Based Films: Advances in the Development of Biomaterials Applicable to Food Packaging. Food Engineering Reviews2019, 11(2), 78–92.
2. Park, H. J.; Bunn, J. M.; Weller, C. L.; Vergano, P. J.; Testin, R. F. Water Vapor Permeability and Mechanical Properties of Grain Protein-Based Films as Affected by Mixtures of Polyethylene Glycol and Glycerin Plasticizers. Transactions of the ASAE1994, 37(4), 1281–1285.
3. Ghanbarzadeh, B.; Oromiehi, A. Biodegradable Biocomposite Films Based on Whey Protein and Zein: Barrier, Mechanical Properties and AFM Analysis. International Journal of Biological Macromolecules2008, 43(2), 209–215.
4. Reddy, N.; Yang, Y. Thermoplastic Films from Plant Proteins. Journal of Applied Polymer Science2013, 130(2), 729–738.
5. Shukla, P.; Bhise, S.; Thind, S. S. Roles of Biodegradable Edible Films and Coatings in Food Industry. ACTA Scientific Nutritional Health2019, 3(5), 138–147.
6. Yilmaz, H., Ahmed, L., Rasulev, B., Leszczynski, J. Application of ligand- and receptor-based approaches for prediction of the HIV-RT inhibitory activity of fullerene derivatives, Journal of Nanoparticle Research, 2016, 18(5):123
7. Ahmed L., Rasulev B., Turabekova M., Leszczynska D., Leszczynski J. Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations, Organic & Biomolecular Chemistry, 2013, 11, 5798–5808
8. Turabekova M.A., Rasulev B.F., Levkovich M.G., Abdullaev N.D. and Leszczynski J. Aconitum and Delphinium sp. Alkaloids as Antagonist Modulators of Voltage-Gated Na+ Channels. AM1/DFT Electronic Structure Investigations and QSAR Studies. Computational Biology and Chemistry, 2008, 32, 88-101
9. Simsek, T., Simsek, S., Mayer, C., Rasulev, B. Combined Computational and Experimental Study on the Inclusion Complexes of β-Cyclodextrin with Selected Food Phenolic Compounds, Structural Chemistry, 2019, 30(4), 1395–1406
10. Demchuk, Z.; Kohut, A.; Voronov, S.; Voronov, A. Versatile Platform for Controlling Properties of Plant Oil-Based Latex Polymer Networks. ACS Sustainable Chemistry & Engineering2018, 6(2), 2780–2786.
11. Masamba, K.; Li, Y.; Zhong, F. Effect of Homogenization Stirring Speed on Mechanical and Water Barrier Properties of Gallic Acid Treated Zein-Oleic Acid Composite Films. Food Packaging and Shelf Life2016, 10, 97–105.
12. Kashiri, M.; Cerisuelo, J. P.; Domínguez, I.; López-Carballo, G.; Hernández-Muñoz, P.; Gavara, R. Novel Antimicrobial Zein Film for Controlled Release of Lauroyl Arginate (LAE). Food Hydrocolloids2016, 61, 547–554.

Breast cancer is a heterogeneous disease that can be classified into different clinical, histopathological and molecular subtypes.‘Triple-negative’ breast cancer (TNBC) comprises about 15% of all breast cancer cases and are devoid of the estrogen receptor (ER), progesterone receptor (PR) and the human epidermal growth factor receptor 2 (HER2) is overexpressed. Triple Negative Breast cancer is differentiated by Brenton et al 2005 on the basis of prognosis and response to the therapy. The treatment that was possible till date for TNBC was chemotherapy as these can breast cancer cells typically lack ER, PR and express ex HER2 Hence the patients are unable to receive any conventional endocrine therapies .It is quite intriguing for for scientific and translational researcher, as TN phenotype initially appears as a potential surrogate for basal-like breast cancers. ER and HER2 mRNA expression are found in basal/ myoepithelial cells of the normal breast, but one of the ‘intrinsic gene’ subtypes of the disease TNBC is characterized by the lack of these ER and HER2 mRNA expression. According, to Perou et al 2000 TNBC are lacking ER and PR expression and HER2 over-expression /HER2 gene amplification.

TNBCs usually exhibit quite high belligerent behaviour and is predominantly found in young women of Hispanic and African descent and a considerable link with BRCA1 germline mutations has also been detected. These TNBCs exhibit quite high metastasis which culminates in the death of the patient within 5 years after diagnosis. However, TNBC is vastly heterogeneous and best considered as an umbrella term which comprises of different entities with characteristic genetic, histological,transcriptional, and clinical attributes. Till date, no effective treatment for metastatic TNBC is available following surgery, radiation and chemotherapy

Among the different types of nanoparticles, gum acacia nanoparticles have the potential for controlled drug delivery to cancer cells. We have synthesized gum acacia nanoparticles by nanoprecipitation method. Physical characterization of the synthesized nanoparticles was done by DLS(Dynamic Light Scattering) and Zeta Potential. Hydrodynamic size of Gum Acacia NPs as measured by DLS was 413.3nm and surface charge of the same was found to be -0.348mV.For the possible biomedical application,we have used curcumin, a natural chemotherapeutic agent.Despite its potential anti-cancer activity, solubility of curcumin is very low rendering its limit in application. Several efforts have been made to solubilise curcumin to enhance its bioavailability. We have used gum acacia nanoparticles where curcumin can be loaded comfortably and thereby increases its bioavailabilty. The cytotoxicity of curcumin-gum acacia NP complex was evaluated on breast cancer cell lines with respect to pre-determined LD50 doses. To investigate the mechanisms, various biochemical assays were also performed. Further, we have explored the fact that the uptake of curcumin by the cells is increased when the curcumin is loaded with gum acacia NPs by utilising the fluorescence property of curcumin. The curcumin-Gum Acacia NP complex was found to induce apoptosis by perturbing the mitochondrial membrane potential of the cancer cells.

The ongoing rapid spread of COVID-19 disease from its first detection in Wuhan, China in late 2019 was declared a pandemic by World Health Organization on 11th March, 2020. It is believed that to combat this deadly virus, now designated as SARS-CoV-2, designing and developing a proper vaccine is the best solution. For developing a sustainable vaccine against this virus, one should have a proper understanding of the mutational changes occurring constantly in its genome and also about the variations that may arise in different communities. Here, we report an algorithm to identify and characterize the mutational changes in the COVID-19 sequences isolated from different countries. The patterns in mutation along with the demographic analysis shown here can be very effective for community specific vaccine designing in the future.

Glass transition temperature T_g is one of the most important thermophysical properties of amorphous polymers. The substantial change in polymer dynamics at glass-transition temperature causes a major change in physical properties, including mechanical modulus, density, specific heat, damping characteristics, dielectric properties of the polymer[1-5]. The cheminformatics approach based on machine learning algorithms is often applied to predict the quantitative relationships between key molecular descriptors and the glass transition temperature T_g of investigated polymers. In this work, we discussing an innovative modeling framework by integrating cheminformatics and coarse-grained molecular dynamics simulations to predict T_g of diverse set of more than hundred polymers[6-10]. This synergistic approach provides valuable insights into the roles of key molecular features (i.e., cohesive interactions, chain stiffness, and topology) influencing the of polymers, paving the way to establish a materials-by-design framework for polymeric materials [11-12]. By harnessing the power of this unprecedented computational efficiency provided by this novel framework, we can successfully predict properties of not only the polymeric materials, but also other classes of organic and inorganic materials

MOL2NET Conference highlights fragments of abstracts published in special issues of journals associated to the conference. This is a fragment of the abstract of the original article that belongs to: Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models), Biomolecules 2020, 10(4), 503; https://doi.org/10.3390/biom10040503.

Fragment: Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures. Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

This is a fragment of the original article that belongs to: Special Issues (Associated to Mol2Net): Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models, https://www.mdpi.com/journal/biomolecules/special_issues/big_data_analysis_biomolecular

Reference (Read Full Paper Free): Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signaturesby Hila Dagan ,Efrat Flashner-Abramson ,Swetha Vasudevan ,Maria R. Jubran ,Ehud Cohen andNataly Kravchenko-BalashaBiomolecules 2020, 10(4), 503; https://doi.org/10.3390/biom10040503 - 26 Mar 2020,

MOL2NET Conference highlights fragments of abstracts published in special issues if journals associated to the conference. This is a fragment of the abstract of the original article that belongs to:

Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models), Biomolecules 2020, 10(1), 26; https://doi.org/10.3390/biom10010026 - 23 Dec 2019

Fragment: Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies...

(This article belongs to the Special Issue Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models)

References

Reference (Read Full Paper Free): Biomolecules 2020, 10(1), 26; https://doi.org/10.3390/biom10010026

MOL2NET Conference highlights fragments of abstracts published in special issues if journals associated to the conference. This is a fragment of the abstract of the original article that belongs to: A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Studyby Shuaibing He ,Xuelian Zhang ,Shan Lu ,Ting Zhu ,Guibo Sun andXiaobo SunBiomolecules 2019, 9(10), 577; https://doi.org/10.3390/biom9100577 - 07 Oct 2019.

In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the [...] Read more. (This article belongs to the Special Issue Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models)

Geoid heights are important for converting terrain elevation from one reference system to another. Different space agencies have developed digital elevation model (DEM) products, which are commercially as well as accessible openly for the earth or its regions in different vertical datums. These DEMs are commonly available in either EGM96 or WGS84 datum. The shape of the geoid(s) developed over time have been derived using approximation of spherical harmonics. Geoid height plays an important role during comparison, validation and utilization of these DEMs. In this study, geoid heights (N) were calculated for EGM84, EGM96 and EGM 2008 using GeographicLib online service at locations of ground control points (GCPs) and analyzed. The mean geoid undulation for the three sites at Kendrapara, Orissa; Jaipur, Rajasthan and Dehradun, Uttarakhand are -62.92m, -50.24m and -44.02m respectively. Whereas the standard deviation for the three sites at Kendrapara, Orissa; Jaipur, Rajasthan and Dehradun, Uttarakhand are 0.27m, 0.46m and 1.22m respectively. The negative values of geoid heights in all the three experimental sites depicts negative gravity anomaly i.e. mass deficit, at these sites and thus indicating that in these regions the surface of the geoid is lower than the reference ellipsoid (WGS84). The resulting standard deviations also depict the increasing roughness of the experimental sites in the order: Kendrapara site, Jaipur site to maximum at Dehradun site.

Maritime surveillance has been an essential requirement since ancient times. However, in the absence of technology, it could not be done so effectively at that time as is being done in today's era of science and technology. The advancement in remote sensing technology has made maritime surveillance quicker and more precise. Ship detection and identification are playing a crucial role in the field of maritime surveillance in order to dealing with sea border activity, illegal fishery, maritime traffic, illegal migration of humans, navy movements or oil spill detection and monitoring. The information provided by imaging radar is fundamentally different from sensors that operate in infrared and visible portions of electromagnetic spectrum. SAR images are found to be very much suitable for the identification of sea objects because of very bright appearance of sea objects in SAR images against dark sea surface in background. In this work RISAT-1 SAR image of Mumbai offshore region (acquired on September 15, 2016) and Radarsat-2 SGF W2 mode of Vancouver, Canada (acquired on August 14, 2008) has been used for the rapid detection of ship objects using CFAR (Constant False Alarm Rate) algorithm technique provided by SNAP (Sentinel Application Platform) software. Presence of detected ship objects has also been demonstrated using kurtosis graph generated from azimuth and range FFT (Fast Fourier Transform) images of final ship detection resulted images of each study area.