Recent Publications

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Volkart PA et al. Cyclin-Dependent Kinase 2 in Cellular Senescence and Cancer. A Structural and Functional Review. Curr Drug Targets. 2018.

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de Ávila MB et al.. Structural Basis for Inhibition of Enoyl-[Acyl Carrier Protein] Reductase (InhA) from Mycobacterium tuberculosis. Curr Med Chem 2018.

 

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Russo and Azevedo. Advances in the Understanding of the Cannabinoid Receptor 1-Focusing on the Inverse Agonists Interactions. Curr Med Chem. 2018.

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Bitencourt-Ferreira... Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem. 2018; 240:63–69.

 

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Amaral MEA et al.. Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest New Drugs. 2018; 36(5):782–796.

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de Ávila... Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des. 2018; 92:1468–1474.

 

 

Editor for the Following Journals

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

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

 

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Member of the Editorial Board for the PeerJ ISSN: 2167-8359

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

 

 

Research

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Protein-Ligand Interacions

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Molecular Docking

 

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Bioinspired Computing

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Computational Systems Biology

 

Protein-Ligand Interactions  

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 the 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 be changed to improve the protein-ligand interaction (De Azevedo & Dias, 2008a). Furthermore, the development of a computational model to predict the binding affinity based on the atomic coordinates of a protein-ligand complex (De Azevedo & Dias, 2008b) opens the possibility to apply virtual screening approaches to search small-molecule databases to identify a drug candidate (De Azevedo, 2010a). To study protein-ligand interactions, we make use of protein crystallography (Canduri & De Azevedo, 2008), nuclear magnetic resonance spectroscopy (Fadel et al., 2005), molecular docking (De Azevedo, 2010b), and molecular dynamics (De Azevedo, 2011). 

References   

Canduri F, de Azevedo WF. Protein crystallography in drug discovery. Curr Drug Targets. 2008; 9(12):1048-53. PubMed

De Azevedo WF Jr. Protein-drug interactions. Curr Drug Targets. 2008; 9(12):1030. PubMed     

De Azevedo WF Jr, Dias R. Experimental approaches to evaluate the thermodynamics of protein-drug interactions. Curr Drug Targets. 2008a; 9(12):1071-6. PubMed    

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

De Azevedo WF Jr. Structure-based virtual screening. Curr Drug Targets. 2010a; 11(3):261-3. PubMed  

De Azevedo WF Jr. MolDock applied to structure-based virtual screening. Curr Drug Targets. 2010b; 11(3):327-34. PubMed 

De Azevedo WF Jr. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem. 2011; 18(9):1353-66. PubMed  

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):759-67.   PubMed