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Limitations of the recognition elements in terms of synthesis, cost, availability, and stability have impaired the translation of biosensors into practical use. Inspired by nature to mimic the molecular recognition of the anti-SARS-CoV-2 S protein antibody (Ab

Diabetes is a metabolic disorder that presents hyperglycemia and vascular complications due to the non-production of insulin or its inappropriate use by the body. One of the strategies to treat diabetes is the inhibition of dipeptidyl peptidase-4 (DPP-4) and it is interesting to conduct virtual screening studies to search for new inhibitors of the DPP-4 enzyme. This study involves a virtual screening using the crystallographic structure of DPP-4 and a compound subset from the ZINC database. To filter this compound subset, we used some physicochemical properties, positioning at the three DPP-4 binding sites, molecular interactions, and ADME-Tox properties. The conformations of ligands obtained from AutoDock Vina were analyzed using a consensus with other algorithms (AutoDock and GOLD). The compounds selected from virtual screening were submitted to biological assays using the "DPPIV-Glo™ protease assay". Cytotoxicity tests were also performed. One promising compound (ZINC1572309) established interactions with important residues at the binding site. The results of the ADME-Tox prediction for ZINC1572309 were compared with a reference drug (sitagliptin). The cytotoxicity of sitagliptin and ZINC1572309 were evaluated using the XTT short-term cytotoxic assay, including normal and tumor cell lines to observe the cellular response to inhibitor treatment at different genetic bases. Both compounds (ZINC1572309 and the reference drug - sitagliptin) also inhibited DPP-4 activity, suggesting interesting biological effects of the selected compound at non-cytotoxic concentrations. Therefore, from in silico and in vitro studies, a potential hit as DPP-4 inhibitor was discovered and it can be structurally optimized to achieve suitable activity and pharmacokinetic profiles.

The main protease of SARS-CoV-2 (called Mpro or 3CLpro) is essential for processing polyproteins encoded by viral RNA. Macromolecules adopt several favored conformations in solution depending on their structure and shape, determining their dynamics and function. Integrated methods combining the lowest-frequency movements obtained by Normal Mode Analysis (NMA), and the faster movements from Molecular Dynamics (MD), and data from biophysical techniques, are necessary to establish the correlation between complex structural dynamics of macromolecules and their function. In this article, we used a hybrid simulation method to sample the conformational space to characterize the structural dynamics and global motions of WT SARS-CoV-2 Mpro and 48 mutants, including several mutations that appear in P.1, B.1.1.7, B.1.351, B.1.525 and B.1.429+B.1.427 variants. Integrated Hybrid methods combining NMA and MD have been useful to study the correlation between the complex structural dynamics of macromolecules and their functioning mechanisms. Here, we applied this hybrid approach to elucidate the effects of mutation in the structural dynamics of SARS-CoV-2 Mpro, considering their flexibility, solvent accessible surface area analyses, global movements, and catalytic dyad distance. Furthermore, some mutants showed significant changes in their structural dynamics and conformation, which could lead to distinct functional properties.HighlightsSingle surface mutations lead to changes in Mpro structural dynamics.Mutants can be more stable than WT according to the structural dynamics properties.Mpromutants can present a distinct functionality in relation to the wild-type.Potential viral markers for more pathogenic or transmissible SARS-CoV-2 variants.

The phenomenon of protein aggregation has been associated with several neurodegenerative diseases, such as Parkinson's and Alzheimer's. Computational tools have been used to predict regions prone to aggregate in proteins with relative success. We have developed a tool called MAGRE for such predictions, based on the machine learning and sliding window techniques. We have applied the Support Vector Machine algorithm to generate classification models. In order to accomplish classification training, we adopted information of primary structure - protein sequence - from the Amyloid Data Bank. We have implemented two predictor categories according to protein structural information: General and Folding Class. We have selected the best performances of the sliding windows method and considered the folding class in order to develop the predictor. We conducted testing with randomly selected protein sequences from the PDB data bank - MAGRE's performance was compared with two predictors from literature: Aggrescan and Zyggregator, being considered satisfactory.

Made available in DSpace on 2019-10-06T17:14:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Vanillic acid is a widely used food additive (flavouring agent, JECFA number: 959) with many reported beneficial biological effects. The same is true for its ester derivative (methyl vanillate, JECFA number: 159). Based on the increasing evidence that diapocynin, the dimer of apocynin (NADPH oxidase inhibitor), has some improved pharmacological properties compared to its monomer, here the dimer of methyl vanillate (MV), i.e., methyl divanillate (dimer of methyl vanillate, DMV) was synthesized and studied in the context of its redox properties and binding affinity with human serum albumin (HSA). We found that the antioxidant potency of DMV was significantly increased compared to MV. In this regard, the reduction of 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical by DMV was 30-fold more effective compared to MV. Ferric ion reduction was 4-fold higher and peroxyl radical reduction was 2.7-fold higher. The interaction with HSA was significantly improved (Stern-Vomer constants, 3.8 × 105 mol-1 L and 2.3 × 104 mol-1 L, for DMV and MV, respectively). The complexation between DMV and HSA was also evidenced by induced circular dichroism (ICD) signal generation in the former due to its fixation in the asymmetric protein pocket. Density-functional calculations (TD-DFT) showed that the ICD spectrum was related to a DMV conformation bearing a dihedral angle of approximately -60°. Similar dihedral angles were obtained in the lowest and most populated DMV cluster poses obtained by molecular docking simulations. The computational studies and experimental displacement studies revealed that DMV binds preferentially at site I. In conclusion, besides being a powerful antioxidant, DMV is also a strong ligand of HSA. This is the first study on the chemical and biophysical properties of DMV, a compound with potential beneficial biological effects. Department of Chemistry Faculty of Sciences UNESP-São Paulo State University Laboratory of Computational Biology and Bioinformatics UFABC-Federal University of ABC Department of Physical Chemistry Institute of Chemistry Campinas State University (UNICAMP) Department of Chemistry Faculty of Sciences UNESP-São Paulo State University FAPESP: 2014/50926-0 FAPESP: 2016/20549-5 FAPESP: 2016/22014-1 CNPq: 302793/2016-0 CNPq: 305541/2017-0

Made available in DSpace on 2019-10-06T16:52:48Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-02-05 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Due to the high sensitivity to alterations in microenvironment polarity of macromolecules, pyrene and its derivatives have long been applied in biosciences. Human serum albumin (HSA), besides its numerous physiological functions, is the main responsible by transport of endogenous and exogenous compounds in the circulatory system. Here, a comprehensive study was carry out to understand the interaction between HSA and the pyrene derivative 1-pyrenesulfonic acid (PMS), which showed a singular behaviour when bound to this protein. The complexation of PMS with HSA was studied by steady state, time-resolved and anisotropy fluorescence, induction of circular dichroism (ICD) and molecular docking. The fluorescence quenching of PMS by HSA was abnormal, being stronger at lower concentration of the quencher. Similar behaviour was obtained by measuring the ICD signal and fluorescence lifetime of PMS complexed in HSA. The displacement of PMS by site-specific drugs showed that this probe occupied both sites, but with higher affinity for site II. The movement of PMS between these main binding sites was responsible by the abnormal effect. Using the holo (PDB: ID 1A06) and apo (PDB: ID 1E7A) HSA structures, the experimental results were corroborated by molecular docking simulation. The abnormal spectroscopic behaviour of PMS is related to its binding in different regions in the protein. The movement of PMS into the protein can be traced by alteration in the spectroscopic signals. These findings bring a new point of view about the use of fluorescence quenching to characterize the interaction between albumin and ligands. Department of Chemistry Faculty of Sciences UNESP - São Paulo State University Laboratory of Computational Biology and Bioinformatics UFABC – Federal University of ABC Department of Chemistry Faculty of Sciences UNESP - São Paulo State University FAPESP: 2014/50926-0 FAPESP: 2016/20549-5 FAPESP: 2016/22014-1 CNPq: 302793/2016-0

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

We describe a new method and a software, called GANM (Genetic Algorithm with Normal Modes), that combines genetic algorithm and normal mode analysis in order to achieve protein–ligand docking simulations that take into account global and local conformational adjustments. The energetical and structural optimization during the ligand binding process is achieved with GA that uses a rotamer library for changing the side chain conformations within and around the binding site. The methodology uses normal modes to account for the global conformational changes of the protein. This software is implemented in distinct versions using different levels of approximations. In this article, we describe the methodology and discuss the results and the performance obtained with the first two versions of GANM. The protein–ligand complexes considered for the test calculations are: (1) Dihydrofolate Reductase (DHFR) and MTX as ligands, (2) HIV-1 Aspartyl Protease with ritonavir, nelfinavir and DMP323 as ligands, and (3) CDK-2 and DT-5 as ligands.

Thioredoxins are multifunctional oxidoreductase proteins implicated in the antioxidant cellular apparatus and oxidative stress. They are involved in several pathologies and are promising anticancer targets. Identification of noncatalytic binding sites is of great interest for designing new allosteric inhibitors of thioredoxin. In a recent work, we predicted normal mode motions of human thioredoxin 1 and identified two major putative hydrophobic binding sites. In this work we investigated noncovalent interactions of human thioredoxin 1 with three phenotiazinic drugs acting as prooxidant compounds by using molecular docking and circular dichroism spectrometry to probe ligand binding into the previously predicted allosteric hydrophobic pockets. Our in silico and CD spectrometry experiments suggested one preferred allosteric binding site involving helix 3 and adopting the best druggable conformation identified by NMA. The CD spectra showed binding of thioridazine into thioredoxin 1 and suggested partial helix unfolding, which most probably concerns helix 3. Taken together, these data support the strategy to design thioredoxin inhibitors targeting a druggable allosteric binding site.

The Thioredoxin (Trx) system plays important roles in several diseases (e.g. cancer, viral infections, cardiovascular and neurodegenerative diseases). Therefore, there is a therapeutic interest in the design of modulators of this system. In this work, we used normal mode analysis to identify putative binding site regions for Human Trx1 that arise from global motions. We identified three possible inhibitor’s binding regions that corroborate previous experimental findings. We show that intrinsic motions of the protein are related to the exposure of hydrophobic regions and non-active site cysteines that could constitute new binding sites for inhibitors.