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

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


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 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; Veit-Acosta and de Azevedo, 2021), 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).


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

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

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

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   

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):1071–1076.   PubMed    

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

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

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

De Azevedo WF Jr. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem. 2011; 18(9):1353–1366. 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):759–767.   PubMed  

Veit-Acosta M, de Azevedo Junior WF. The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity. Curr Med Chem. doi: 10.2174/0929867328666210210121320.   PubMed