Books

 

 

 

Projects

 

 

 

Citation

 

Editorships

Current Drug Targets

Frontiers Section Editor (Bioinformatics and Biophysics) for the Current Drug Targets ISSN: 1873-5592

Bentham Link

Current Medicinal Chemistry

Section Editor (Bioinformatics in Drug Design and Discovery) for the Current Medicinal Chemistry ISSN: 1875-533X

Bentham Link

Combinatorial Chemistry and High Throughput Screening

Section Editor (Combinatorial/Medicinal Chemistry) for the Combinatorial Chemistry and High Throughput Screening ISSN: 1875-5402

Bentham Link

Current Bioinformatics

Member of the Editorial Board for the Current Bioinformatics ISSN: 2212-392X (Online) ISSN: 1574-8936 (Print)

Bentham Link

Organic and Medicinal Chemistry International Journal

Member of the Editorial Board for the Organic and Medicinal Chemistry International Journal ISSN: 2474-7610

Bentham Link

Bioengineering International

Section Editor in Chief (Bioinformatics) for Bioengineering International. ISSN 2668-7119

Bioengineering International

 

Research

Summaries      Pages
Protein-Ligand Interactions

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In the study of intermolecular interactions involving protein and ligands, we expect to gain further insights into the structural basis for the specificity of small-molecule ligands against a specific protein target (de Azevedo, 2008). Analysis of protein-ligand interaction is a central problem in drug design. Knowledge of the key features responsible for the specificity of a ligand for a protein allows us to determine which physical-chemical parameters could improve the protein-ligand interaction (de Azevedo and Dias, 2008a). Furthermore, the development of a computational model to predict the binding affinity based on the atomic coordinates of a protein-ligand complex opens the possibility to apply virtual screening approaches to search small-molecule databases to identify a drug candidate (de Azevedo and Dias, 2008b, de Azevedo, 2010a; Bitencourt-Ferreira and de Azevedo, 2019a; da Silva et al., 2020). To study protein-ligand interactions, we make use of protein crystallography (Canduri and de Azevedo, 2008), nuclear magnetic resonance spectroscopy (Fadel et al., 2005), molecular docking (de Azevedo, 2010b), and molecular dynamics (de Azevedo, 2011; Bitencourt-Ferreira and de Azevedo, 2019b).

 

References

 

Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019a; 2053: 251–273. Pubmed Logo

 

Bitencourt-Ferreira G, de Azevedo WF Jr. Molecular Dynamics Simulations with NAMD2. Methods Mol Biol. 2019b; 2053: 109–124. Pubmed Logo

 

Canduri F, de Azevedo WF. Protein crystallography in drug discovery. Curr Drug Targets. 2008; 9(12):1048–1053. Pubmed Logo

 

Journal of Computational Chemistry da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69–73. Pubmed Logo

 

de Azevedo WF Jr. Protein-drug interactions. Curr Drug Targets. 2008; 9(12):1030. Pubmed Logo

 

de Azevedo WF Jr, Dias R. Experimental approaches to evaluate the thermodynamics of protein-drug interactions. Curr Drug Targets. 2008a; 9(12):1071–1076. Pubmed Logo

 

de Azevedo WF Jr, Dias R. Computational methods for calculation of ligand-binding affinity. Curr Drug Targets. 2008b; 9(12):1031–1039. Pubmed Logo

 

de Azevedo WF Jr. Structure-based virtual screening. Curr Drug Targets. 2010a; 11(3):261–263. Pubmed Logo

 

de Azevedo WF Jr. MolDock applied to structure-based virtual screening. Curr Drug Targets. 2010b; 11(3):327–334. Pubmed Logo

 

de Azevedo WF Jr. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem. 2011; 18(9):1353–1366. Pubmed Logo

 

Fadel V, Bettendorff P, Herrmann T, de Azevedo WF Jr, Oliveira EB, Yamane T, Wüthrich K. Automated NMR structure determination and disulfide bond identification of the myotoxin crotamine from Crotalus durissus terrificus. Toxicon. 2005; 46(7):759–767. Pubmed Logo

 

 

Keywords: Protein; ligand; interactions; protein-ligand interactions; drug design; drug discovery; protein crystallography; nuclear magnetic resonance spectroscopy; molecular docking; molecular dynamics; simulations; docking simulations; binding affinity.

LigPlot
Computational Systems Biology

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We have been working on the development of computational models for unraveling the molecular mechanisms underlying enzyme inhibition and protein-ligand interactions (Bitencourt-Ferreira and de Azevedo, 2019a; 2019b; 2019c; da Silva et al., 2020). We can use these computational models to predict the binding affinity of a potential inhibitor for an enzyme; such knowledge has the potential to speed up drug discovery and decrease the cost of development of new drugs (de Ávila et al., 2017; Pintro and de Azevedo, 2017). Furthermore, the availability of computational models to predict binding affinity based on the atomic coordinates of protein-ligand complexes adds flexibility to the process of drug discovery (Xavier et al., 2016; Heck et al., 2017). It allows us to computationally test different scenarios where a potential new drug may interact with a protein target. We developed the programs SAnDReS (Xavier et al., 2016) and Taba (da Silva et al., 2020) to create machine-learning models targeted to the biological system of interest. We have successfully employed SAnDReS to study coagulation factor Xa (Xavier et al., 2016), cyclin-dependent kinases (de Ávila et al., 2017; Levin et al., 2018), HIV-1 protease (Pintro and de Azevedo, 2017), estrogen receptor (Amaral et al., 2018), cannabinoid receptor 1 (Russo and de Azevedo, 2019), and 3-dehydroquinate dehydratase (de Ávila and de Azevedo, 2018). Also, we used SAnDReS to develop a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes (Bitencourt-Ferreira and de Azevedo Jr., 2018).

 

References

 

Amaral MEA, Nery LR, Leite CE, de Azevedo Junior WF, Campos MM. Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest New Drugs. 2018; 36(5): 782–796. Pubmed Logo

 

Bitencourt-Ferreira G, de Azevedo Jr. WF. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem. 2018; 240: 63–69. Pubmed Logo

 

Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019a; 2053: 251–273. Pubmed Logo

 

Bitencourt-Ferreira G, de Azevedo WF Jr. Exploring the Scoring Function Space. Methods Mol Biol. 2019b; 2053: 275–281. Pubmed Logo

 

Bitencourt-Ferreira G, de Azevedo WF Jr. How Docking Programs Work. Methods Mol Biol. 2019c; 2053: 35–50. Pubmed Logo

 

Pubmed Logo da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69–73.

 

de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem Biophys Res Commun. 2017; 494: 305–310. Pubmed Logo

 

de Ávila MB, de Azevedo WF Jr. Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des. 2018; 92: 1468–1474. Pubmed Logo

 

Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF. Supervised Machine Learning Methods Applied to Predict Ligand-Binding Affinity. Curr Med Chem. 2017; 24(23): 2459–2470. Pubmed Logo

 

Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8. Pubmed Logo

 

Pintro VO, Azevedo WF. Optimized Virtual Screening Workflow. Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease. Comb Chem High Throughput Screen. 2017; 20(9): 820–827. Pubmed Logo

 

Current Medicinal ChemistryRusso S, de Azevedo WF. Advances in the Understanding of the Cannabinoid Receptor 1 - Focusing on the Inverse Agonists Interactions. Curr Med Chem. 2019; 26(10): 1908–1919. Pubmed Logo

 

Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL, Azevedo WF Jr. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016; 19(10): 801–812. Pubmed Logo

 

 

Keywords: Computational systems biology; systems biology; systems approach; machine learning; protein; ligand; interactions; protein-ligand interactions; drug design; drug discovery; binding affinity.

Computational Systems Biology
Molecular Simulations

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Our molecular world challenges us on an everyday basis to establish the theoretical foundations to understand it. From biological molecules to the next generation of nanomaterials, the possibility of simulating them is compelling. The impact of our scientific and technological development inspires a new generation of scientists to create new computational models to simulate molecules. The success of quantum mechanics makes it clear that it is our ultimate goal to simulate molecules. On the other hand, it is evident that computer power only limits simulations of polymers. Therefore, the development and application of classical and hybrid methodologies still have a beneficial impact to assess the behavior of molecules (de Azevedo and Dias, 2008). In our research, we focus on the development of a new generation of force fields targeted to the molecular system of interest (de Azevedo, 2011). We have been working on the development of a new computational tool to study biomolecules (de Azevedo et al., 2001), nanomaterials, and the interaction between them (Bitencourt-Ferreira and de Azevedo, 2019).

 

References

 

Bitencourt-Ferreira G, de Azevedo WF Jr. Molecular Dynamics Simulations with NAMD2. Methods Mol Biol. 2019; 2053: 109–124. Pubmed Logo

 

de Azevedo WF Jr, Canduri F, Fadel V, Teodoro LG, Hial V, Gomes RA. Molecular model for the binary complex of uropepsin and pepstatin. Biochem Biophys Res Commun. 2001; 287(1): 277–281. Pubmed Logo

 

de Azevedo WF Jr, Dias R. Computational methods for calculation of ligand-binding affinity. Curr Drug Targets. 2008; 9(12): 1031–1039. Pubmed Logo

 

de Azevedo WF Jr. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem. 2011; 18(9): 1353–1366. Pubmed Logo

 

Keywords: Molecular simulations; computer simulations; drug design; drug discovery; molecular docking; molecular dynamics; docking simulations; binding affinity.

Molecular Simulations