The 5,10-methylenetetrahydrofolate (5,10-mTHF) is a cofactor essential for the synthesis of purines and thymidine, which are crucial for the cell viability.[1] The α-elimination of ʟ-serine, catalyzed by the serine hydroxymethyltransferase (SHMT), is the primary source of 5,10-mTHF in the cell. However, the catalytic mechanism behind the synthesis of 5,10-mTHF was unknown, and two divergent theories were proposed for the mechanism. Some authors suggested that the final steps of the 5,10-mTHF synthesis occur in the cytoplasm whereas other authors showed some evidence that the reaction must occur inside the SHMT. [2]

In this study, we addressed the entire catalytic mechanism of the PLP-dependent enzyme SHMT using a QM/MM approach and the mechanism of 5,10-mTHF synthesis in aqueous solution. The calculations were prepared and analyzed using molUP [3] for VMD and run on Gaussian09 and ORCA.

This work [4] resulted in the entire e detailed catalytic mechanism of SHMT. The results showed that both hypotheses for the synthesis of 5,10-mTHF shared the two first steps where the -OH group is transferred from the serine to the THF. These reactions occur inside the SHMT and have a ∆G^‡ of 18.0 and 2.0 kcal/mol. Then, the reaction can proceed inside the enzyme through 5 sequential steps or in the cytoplasm where only 3 steps are needed. The calculations showed that the mechanism is kinetic and thermodynamically favorable by 0.8 and 24.3 kcal/mol, respectively, when it takes place inside the SHMT. Although the reaction is not impossible in solution, it is very improbable that the THF intermediate might be released to the cytoplasm to overcome a set of reactions that are less favorable when compared to the ones that would occur in the SHMT.

Reference

[1] Froese, D. S.; et al., Structural basis for the regulation of human 5,10-methylenetetrahydrofolate reductase by phosphorylation and S-adenosylmethionine inhibition. Nature comm 2018, 9 (1), 2261-2261.

[2] Schirch, V.; et al, Serine hydroxymethyltransferase revisited. Curr Opin Chem Biol 2005, 9 (5), 482-7.

[3] Fernandes, H. S.; et al., molUP: A VMD plugin to handle QM and ONIOM calculations using the Gaussian software. J Comput Chem 2018, 39 (19), 1344-1353.

[4] Fernandes, H. S.; et al., Catalytic Mechanism of the Serine Hydroxymethyltransferase: A Computational ONIOM QM/MM Study. ACS Catalysis 2018, 10096-10110.

Acknowledgments

FCT (SFRH/BD/115396/2016, IF/01310/2013, IF/00052/2014 e PTDC/QUI-QFI/31689/2017)

Pyridoxal 5'-phosphate (PLP), the active form of the vitamin B6, is an essential cofactor required by more than 160 families of enzymes. Its role as an electron sink makes it imperative for the catalysis of a myriad of chemical reactions. Contrarily to microorganisms and plants, humans and other mammals are not able to synthesize PLP de novo, resorting to a "salvage pathway" that helps to maintain PLP homeostasis [1]. The correct functioning of this salvage pathway is crucial for the cell, as demonstrated by the correlation between low levels of PLP and the occurrence of severe neurological disorders [2]. It was found that the major culprit is pyridoxine/pyridoxamine 5'-phosphate oxidase (PNPOx), an FMN-dependent homodimeric enzyme responsible for the recycling of pyridoxine 5'-phosphate (PNP) and pyridoxamine 5'-phosphate (PMP) into PLP [3]. Therefore, in order to better understand its role in these disorders, it is of the utmost importance to unveil the catalytic mechanism of PNPOx. To do so we used computational means, namely QM/MM hybrid methodologies [4], to evaluate different mechanistic proposals related to PNPOx reactivity. Models were prepared and evaluated enabling important aspects related to the catalytic modelling of this enzyme to be validated. The results obtained in the present work provide important details about the catalytic mechanism of PNPOx, helping us to understand the importance of some key residues in the active site that can have implications in some PLP-deficiency disorders. More studies are required to fully understand the catalytic mechanism of this important enzyme.

Acknowledgments: We acknowledge FCT for financial support to the PhD grant SFRH/BD/136594/2018, the Starting grants IF/01310/2013 and IF/00052/2014, and to the Project PTDC/QUI-QFI/31689/2017.

References

[1] Di Salvo, M L, et al. Vitamin B6 Salvage Enzymes: Mechanism, Structure and Regulation. BBA-Proteins Proteomics 2011, 1814(11), 1597–1608

[2] Mills, P B, et al. Neonatal Epileptic Encephalopathy Caused by Mutations in the PNPO Gene Encoding Pyridox(Am)Ine 5′-Phosphate Oxidase. Hum Mol Genet 2005, 14(8), 1077–1086

[3] Di Salvo, M L, et al. Structure and Mechanism of Escherichia Coli Pyridoxine 5′-Phosphate Oxidase. BBA-Proteins Proteomics 2003, 1647(1–2), 76–82

[4] Chung, L. W, et al. The ONIOM Method and Its Applications. Chem Rev 2015, 115(12), 5678–5796

Molecular imprinted polymers (MIP) are used in very different fields such as solid-phase extraction, enantiomer separations, drug delivery, drug discovery, and so on. Due to this, different techniques have been investigated in the past few years. In this contest, sol-gel polycondensation technique is an interesting alternative since MIP produced with this technique has been proved to present several advantages such as physical robustness, long shelf life, simple preparation, great selectivity, etc. The most widely used precursors for preparing sol−gel materials have been silicon alkoxides, such as tetramethoxysilane (TMOS) or tetraethoxysilane (TEOS). In a recent paper for the first time, we simulated a complex sol-gel system aimed at preparing the (S)-naproxen-imprinted xerogel with an explicit representation of all the ionic species at pH 9¹. With that simulation we were able to undercover the molecular mechanism behind the imprinting process. However, the simulation ran for only 100ns and we were unable to simulate other important process such as the polymer formation. One possible solution is to move on to a coarse-grain (CG) simulation based on the Martini force field². The model uses a four-to-one mapping, i.e. on average four heavy atoms and associated hydrogens are represented by a single interaction center. One of the main advantages of this approach is that larger systems may be simulated for longer time. Due to this, the main aim of this study is the simulation of the molecular imprinting process using the Martini force field, in order to simulate all the relevant aspects occurring during the imprinting and polycondensation process.

1. Concu, R.; Perez, M.; Cordeiro, M. N.; Azenha, M., Molecular dynamics simulations of complex mixtures aimed at the preparation of naproxen-imprinted xerogels. J Chem Inf Model 2014, 54 (12), 3330-43.
2. Marrink, S. J.; Risselada, H. J.; Yefimov, S.; Tieleman, D. P.; de Vries, A. H., The MARTINI Force Field:  Coarse Grained Model for Biomolecular Simulations. The Journal of Physical Chemistry B 2007, 111 (27), 7812-7824.

One of the main challenges facing the development of effective anti HIV-1 medicines relates to the high mutation rate of essential enzymes, such as HIV-1 Protease (HIV1Pr).^[1] Pereira Vaz et al. ^[2] first reported a threonine insertion at position 35 (E35E_T), in the HIV1Pr coding region, among treatment-naïve subtype C infected individuals. Undetectable viral loads were attained after antiretroviral therapy in such individuals, with no associated major mutations, implying null contribution of E35E_T to viral resistance. Interestingly, a new study suggests a potential additive effect of position 35 insertions when in presence of major mutations – ultimately leading to resistance to HIV1Pr inhibitors in higher extent. ^[3]

In order to study the role of the E35E_T insertion in the structure and ligand-binding propensity of HIV1Pr, homology models were generated from subtype B and subtype C base sequences, using available X-ray structures corresponding to highest identity sequences as template. Fifty (50)-nanoseconds Molecular Dynamics (MD) simulations were then performed for unbound and bound (HIV1PR:darunavir complex) structures of the wild-type form and a single‑point major mutation variant of HIV1PR – in all cases in presence and absence of E35_T.

Combining simple measurements like the root mean square (RMS) deviations and fluctuations, applied to the whole protein and to its two functional flap regions, with principal component analysis (PCA) of the multiple MD trajectories, we herein contrast the behaviour of all systems in attempt to dissect the putative role of E35E_T in the resistance towards HIV1PR inhibitors.

In recent years, the use of drug delivery systems based on polymeric nanoparticles (NPs) has generated innovative strategies for several diseases^1,2. Polylactic acid (PLA) is one of the most commonly used polymers for the synthesis of NPs³, PLA-NPs conjugated with hydrophilic molecules like polyethylene glycol (PEG) presents improved blood circulation, biocompatibility, and less cytotoxicity⁴.

The current high prevalence of cardiovascular diseases (CVD) and the vast application of PEGylated NPs propose an excellent opportunity to develop novel therapeutic approaches for CVD.

In this work, we employed a combination of Molecular Dynamic simulations and Blind Docking techniques to understanding the structural and physiochemical properties that establish the association of cilostazol and adenosine 5'-monophosphate (AMP), both antiaggregant compounds, with PLA-NPs and to characterize the host-guest chemistry of complexes as novel nanosystem for CVD.

The results showed that cilostazol structure allows a better alignment with the PLA unit than AMP and presented the highest affinity to PLA core, which was consistent with logP values. However, Steered Molecular Dynamics showed a similar behavior of drug release.

The structural characterization in silico of polymers-drugs provides a comprehensive understanding of the factors that contribute to NP formation and drug loading of nanocarriers based on polymeric NPs. This approach represents an innovative strategy to evaluate the drug encapsulation of several antiplatelet drugs into PLA-NPs.

References

1. Bae, Y., Adv. Drug Deliv. Rev., 61, 768, 2009.
2. Avgoustakis, K., Curr. Drug Deliv., 1, 321, 2004.
3. Xiao, R. Z., Int. J. Nanomedicine, 5, 1057, 2010.
4. Gref, R., Protein Delivery, 167, 2002.

Acknowledgements: CONICYT-PCHA/Doctorado Nacional/2014-21140225. CV acknowledges the financial support of FONDECYT
Regular #1161438. UNAB Regular, DI-695-15/R.

Most of drugs interact with more than one molecular target. This fact typically would be seen like as an undesired feature of a pharmacological treatment, however, current trends in drug discovery has put hope and several efforts in the improved efficiency and efficacy that have been showed by some promiscuous drugs. Indeed, several approaches for predict the polypharmacological profile of drugs have been recently developed. In this line, the structure of proteins has gained special interest.

The structure of proteins is several times more conserved than their sequence. Moreover, even in those cases where a close evolutionary relationship exists between two proteins, it is possible that their global structures are not conserving, and only share partial three-dimensional (3D) patterns, which define in most cases, their biological functions. Interestingly, several tools have been developed for the identification of similar 3D patterns, however, usually demand a known query or only consider the observed data (e.g. orthosteric binding sites in PDB, annotated motif, known ligands, etc.). Nevertheless, some approaches shows that 3D amino acids conservation is a enough prove for consider these residues as part of an active site or a binding site of a protein structure, even when no prior knowledge of functional residues are available. Thus, considering all unknown or unobserved 3D patterns (e.g. allosteric binding sites), for the discovery, search and characterization of putative common binding sites between a set of protein structures, cold be more informative than explore only known sites.

Here, we present 3D-PP, a new free access web server to discover all conserved 3D amino acid patterns among a set of protein structures including those coming from both, X-ray crystallographic experiments and in silico comparative modelling. The preprocessing modules of 3D-PP were developed in Python and all data generated are processed and organized automatically in a scalable high-performance graph database.

References (1–5)

1. Anighoro A, Bajorath J, Rastelli G. Polypharmacology: Challenges and Opportunities in Drug Discovery. J Med Chem [Internet]. 2014;dx.doi.org/10.1021/jm5006463. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24946140

2. Konc J, Janežič D. Binding site comparison for function prediction and pharmaceutical discovery. Curr Opin Struct Biol [Internet]. 2014 Apr [cited 2014 Sep 3];25:34–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24878342

3. Nadzirin N, Gardiner EJ, Willett P, Artymiuk PJ, Firdaus-Raih M. SPRITE and ASSAM: Web servers for side chain 3D-motif searching in protein structures. Nucleic Acids Res. 2012;40(W1).

4. Martínez-bazan N, Muntés-mulero V, Gómez-villamor S. DEX : High-Performance Exploration on Large Graphs for Information Retrieval. Artif Intell [Internet]. 2007;573–82. Available from: http://portal.acm.org/citation.cfm?doid=1321440.1321521

5. Núñez-Vivanco G, Valdés-Jiménez A, Besoaín F, Reyes-Parada M. Geomfinder: A multi-feature identifier of similar three-dimensional protein patterns: A ligand-independent approach. J Cheminform. 2016;8(1).

Nowadays, the fingerprinting methodologies of olive oils are dominated. They consider the entire analytical signal, which is acquired and recorded by the analytical instrument, directly from olive oil or isoleted fraction, i,e chromatogram. The shape and intensity of the recorded signal the instrumental fingerprint from the whole olive oil adulteration. Therefore, the methodolygy is based on the chemical composition (Fatty acids and Triglycerides compositions). However, Fatty acids composition as an indicator of purity suggests that linolenic acid content could be used as a parameter for the detection of extra virgin olive oil fraud with 5% of soybean oil. The adulteration could also be detected by the increase of the trans-fatty acid contents with 3% of soybean oil, 2% of corn oil and 4% of sunflower oil. The use of the ∆ECN42 proved to be effective in the Chemlali extra-virgin olive oil adulteration even at low levels: 1% of sunflower oil, 3% of soybean oil and 3% of corn oil. Therefore, compared to classical methods PCA and new approach of using LDA application could represent an alternative and innovative tool for faster and cheaper evaluation of extra-virgin olive oil adulteration.

A new D-erythrose 1,3-dioxane 1,5-lactone derivative was synthetized and found to be a highly stereo-selective template as dipolarophile in 1,3-dipolar cycloadditions. Different regio-selectivities were obtained depending on the nature of the 1,3-dipole: complete, with alkyl azides and diazomethylbenzene, inexistent, with nitrile oxides. To understand the mechanisms of cycloadditions with the three types of 1,3-dipoles, computational studies were performed, giving full agreement with the experimental data.

The computational results showed that all the studied cycloadditions are concerted processes, involving exoenergonic, and small free activation energies. The stereoselectivity of the reactions is due to a combination of the steric effect endorsed by hydrogen H-8 and the hyper conjugative effect of the incoming 1,3-dipole with the lactone. The regioselectivity observed in alkyl azides and phenyldiazomethane is mostly dependent on the distortion effect during the cycloaddition process. This distortion effect is however higher in the alkyl azide compounds than in phenyldiazomethane. This distortion effect is absent from nitrile oxides. This study provides a specific example where apparent similar chemistry was found to proceed via different mechanisms, leading to different output results.

Outer membrane protein G (OmpG) is a monomeric protein of E. coli outer membrane that mediates a pH-dependent non-specific oligosaccharide transport [1]. Two X-ray structures have been determined at different pH values: a closed conformation at low pH that inhibits metabolite transport; and an open one at neutral pH that allows it [1,2]. The key structural difference behind the mechanism lies in the position of the external loops, that determine the open or closed conformation of the channel. Given the important function of this protein, a detailed description of the conformation changes that result from varying the pH has enormous biological relevance, possibly contributing in making it a better antibiotic target and more flexible biosensor [3]. Here, we will perform constant pH molecular dynamics (CpHMD) simulations of a membrane-embedded OmpG in order to characterize the conformational/protonation space of both end states (open and closed). pH-induced conformational transitions and metabolite transmembrane diffusion through the pore are examples of other objectives in the current project.

[1] Yildiz, Ö., Vinothkumar, K.R., Goswami, P., Kühlbrandt, W. EMBO J., 2006, 25, 3702

[2] Zhuang, T., Chisholm, C., Chen, M., Tamm, L.K. J.Am.Chem.Soc., 2013, 135, 15101

[3] Chen, M., Khalid, S., Sansom, M. S., Bayley, H. Proc. Natl. Acad. Sci. U.S.A., 2008, 105, 6272.

G-protein-coupled receptors (GPCRs) constitute a large family of structurally similar proteins that respond to diverse physiological and environmental stimulants and that includes many therapeutic targets. In fact, 40% of all modern medicinal drugs are thought to target G-protein-coupled receptors (GPCRs), making this large family of proteins a particular appealing target for drug discovery efforts [1, 2].

Protein-ligand docking is a computational method that tries to predict and rank the structure resulting from the association between a ligand and a target protein [3]. Virtual screening (VS) can use docking to evaluate databases with millions of compounds to identify promising new molecules that could bind to a specific target of pharmacological interest, including GPCRs [4]. This strategy if often used to limit the amount of molecules that has to be tested experimentally and to reduce the cost in the identification of new lead molecules for drug development.

This work reports a detailed comparison of the popular Autodock and Vina software programs in ligand/decoys discrimination against 5 GPCR proteins, (Adenosine 2a receptor, Beta-1 adrenergic receptor, Beta-2 adrenergic receptor, C-X-C chemokine receptor type 4 and Dopamine D3 receptor), for a total of 1480 ligands and 99763 decoys. The results show that AutoDock is more efficient in recovering real ligands among the top 1% solution than VINA, when applying virtual screening to GPCR receptors.

1. Lagerstrom, M.C. and H.B. Schioth, Structural diversity of G protein-coupled receptors and significance for drug discovery. Nature Reviews Drug Discovery, 2008. 7(4): p. 339-357.
2. Overington, J.P., B. Al-Lazikani, and A.L. Hopkins, Opinion - How many drug targets are there? Nature Reviews Drug Discovery, 2006. 5(12): p. 993-996.
3. Sousa, S.F., P.A. Fernandes, and M.J. Ramos, Protein-ligand docking: Current status and future challenges. Proteins-Structure Function and Bioinformatics, 2006. 65(1): p. 15-26.
4. Shoichet, B.K. and B.K. Kobilka, Structure-based drug screening for G-protein-coupled receptors. Trends in Pharmacological Sciences, 2012. 33(5): p. 268-272.