Books
Docking Screens for Drug Discovery. Editor: de Azevedo Jr., Walter Filgueira (Ed.)
Projects
SAnDReS: Statistical Analysis of Docking Results and Scoring functions
Taba: A Tool to Analyze the Binding Affinity
Citation
Editorships
Frontiers Section Editor (Bioinformatics and Biophysics) for the Current Drug Targets ISSN: 1873-5592
Section Editor (Bioinformatics in Drug Design and Discovery) for the Current Medicinal Chemistry ISSN: 1875-533X
Section Editor (Combinatorial/Medicinal Chemistry) for the Combinatorial Chemistry & High Throughput Screening ISSN: 1875-5402
Member of the Editorial Board for the Current Bioinformatics ISSN: 2212-392X (Online) ISSN: 1574-8936 (Print)
Member of the Editorial Board for the Organic & Medicinal Chemistry International Journal ISSN: 2474-7610
Section Editor in Chief (Bioinformatics) for Bioengineering International. ISSN 2668-7119
Research
Protein-Ligand Interacions
Molecular Docking
Bioinspired Computing
Computational Systems Biology
Research
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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), 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.
Bitencourt-Ferreira G, de Azevedo WF Jr. Molecular Dynamics Simulations with NAMD2. Methods Mol Biol. 2019b; 2053: 109–124.
Canduri F, de Azevedo WF. Protein crystallography in drug discovery. Curr Drug Targets. 2008; 9(12):1048–1053.
de Azevedo WF Jr. Protein-drug interactions. Curr Drug Targets. 2008; 9(12):1030.
de Azevedo WF Jr, Dias R. Experimental approaches to evaluate the thermodynamics of protein-drug interactions. Curr Drug Targets. 2008a; 9(12):1071–1076.
de Azevedo WF Jr, Dias R. Computational methods for calculation of ligand-binding affinity. Curr Drug Targets. 2008b; 9(12):1031–1039.
de Azevedo WF Jr. Structure-based virtual screening. Curr Drug Targets. 2010a; 11(3):261–263.
de Azevedo WF Jr. MolDock applied to structure-based virtual screening. Curr Drug Targets. 2010b; 11(3):327–334.
de Azevedo WF Jr. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem. 2011; 18(9):1353–1366.
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.
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. |
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Molecular Docking
The computational prediction of the position of a given ligand into the binding pocket of a protein target is called protein-ligand molecular docking (Dias and de Azevedo, 2008; de Azevedo, 2010; Heberlé and Azevedo, 2011). Our focus here is on the application of optimized molecular docking strategies to identify potential new inhibitors for enzymes that are targets for drug development. We are interested in the discovery of new inhibitors for cyclin-dependent kinases (de Azevedo, 2016; Levin et al., 2017; de Ávila et al., 2017), HIV-1 protease (Pintro and de Azevedo, 2017), and several others proteins targets (Heck et al., 2017). We also seek the development of integrated strategies for molecular docking simulations (Xavier et al., 2016). These docking strategies use applications of bio-inspired computing to carry out molecular docking simulations (Heberlé and de Azevedo, 2011). Furthermore, we are seeking the development of a targeted-scoring function for the biological system we are interested in (Heck et al., 2017; Bitencourt-Ferreira and de Azevedo, 2019a; 2019b; Russo and de Azevedo, 2019; da Silva et al., 2020).
Molecular docking simulation of roscovitine against the structure of CDK2.
References
Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019a; 2053: 251–273.
Bitencourt-Ferreira G, de Azevedo WF Jr. Exploring the Scoring Function Space. Methods Mol Biol. 2019b; 2053: 275–281.
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.
de Azevedo WF Jr. MolDock applied to structure-based virtual screening. Curr Drug Targets. 2010; 11(3):327–334.
de Azevedo Jr. WF. Opinion Paper: Targeting Multiple Cyclin-Dependent Kinases (CDKs): A New Strategy for Molecular Docking Studies. Curr Drug Targets. 2016;17(1):2.
Dias R, de Azevedo WF Jr. Molecular docking algorithms. Curr Drug Targets. 2008; 9(12):1040–1047.
Heberlé G, de Azevedo WF Jr. Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem. 2011; 18(9):1339–1352.
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.
Levin NM, Pintro VO, de Ávila MB, de Mattos BB, De Azevedo WF Jr. Understanding the Structural Basis for Inhibition of Cyclin-Dependent Kinases. New Pieces in the Molecular Puzzle. Curr Drug Targets. 2017; 18(9): 1104–1111.
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.
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.
Keywords: Protein-ligand interactions; drug design; drug discovery; molecular docking; simulations; docking simulations; binding affinity. |
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Bio-inspired Computing
Nature as a source of inspiration has been shown to have a beneficial impact on the development of new computational methodologies. Algorithms that mimic biological systems can create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. Biologically inspired algorithms (BIA) comprise a class of stochastic optimization and adaptation methodologies designed for global optimization. One of the most promising biologically inspired algorithms is the evolutionary algorithm. The evolutionary algorithms extract inspiration from the process of genetic evolution. In Darwinian evolution, species selection is based on their capacity for survival of the fittest in an ecosystem. Under this view, classes of evolutionary algorithms, known as genetic algorithms, genetic programming, and evolutionary programming, have been developed. All evolutionary algorithms share a large number of characteristics (Heberlé and de Azevedo, 2011). We have been working in the application and development of bio-inspired computation to assess the problem of protein-ligand interactions (Xavier et al., 2016; Heck et al., 2017; de Ávila et al., 2017; Pintro and de Azevedo, 2017; Bitencourt-Ferreira and de Azevedo, 2019; Russo and de Azevedo, 2019).
Schematic representation of the main operators present in a genetic algorithm. The Python code related to this figure is available here
References
Bitencourt-Ferreira G, de Azevedo WF Jr. How Docking Programs Work. Methods Mol Biol. 2019; 2053: 35–50.
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.
Heberlé G, de Azevedo WF Jr. Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem. 2011; 18(9):1339–1352.
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.
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.
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.
Keywords: Bio-inspired computing; bio-inspired algorithms; evolutionary computing; evolutionary algorithms; genetic algorithms; ant colony optimization; neural network; machine learning; protein-ligand interactions; molecular docking; simulations; docking simulations; binding affinity. |
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Computational Systems Biology
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.
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.
Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019a; 2053: 251–273.
Bitencourt-Ferreira G, de Azevedo WF Jr. Exploring the Scoring Function Space. Methods Mol Biol. 2019b; 2053: 275–281.
Bitencourt-Ferreira G, de Azevedo WF Jr. How Docking Programs Work. Methods Mol Biol. 2019c; 2053: 35–50.
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.
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.
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.
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.
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.
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.
Keywords: Computational systems biology; systems biology; systems approach; machine learning; protein; ligand; interactions; protein-ligand interactions; drug design; drug discovery; binding affinity. |
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Molecular Simulations
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.
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.
de Azevedo WF Jr, Dias R. Computational methods for calculation of ligand-binding affinity. Curr Drug Targets. 2008; 9(12): 1031–1039.
de Azevedo WF Jr. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr Med Chem. 2011; 18(9): 1353–1366.
Keywords: Molecular simulations; computer simulations; drug design; drug discovery; molecular docking; molecular dynamics; docking simulations; binding affinity. |
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Exploring the Scoring Function Space [>>]
We envisage protein-ligand interaction as a result of the relation between the protein space (Smith, 1970) and the chemical space (Bohacek et al., 1996; Dobson, 2004; Kirkpatrick & Ellis, 2004; Lipinski & Hopkins, 2004; Shoichet, 2004; Stockwell, 2004), and we propose to approach these sets as a unique complex system, where the application of computational methodologies could contribute to establishing the physical principles to understand the structural basis for the specificity of ligands for proteins. Such approaches have the potential to create novel semi-empirical free energy scoring function to predict binding affinity with superior predictive power when compared with standard methodologies. We propose to use the abstraction of a mathematical space composed of infinite computational models to predict ligand-binding affinity, named here as scoring function space (Heck et al., 2017; Bitencourt-Ferreira & de Azevedo Jr., 2019). By the use of supervised machine learning techniques, we can explore this scoring function space to build a computational model targeted to a specific biological system. For instance, we created targeted-scoring functions for HIV-1 Protease and Cyclin-Dependent Kinases. We developed the programs SAnDReS, SFSXplorer, and Taba (da Silva et al., 2020) to generate computational models to predict ligand-binding affinity. SAnDReS, SFSXplorer, and Taba are integrated computational tools to explore the scoring function space.
A view of the scoring function space as a way to develop a computational model to predict ligand-binding affinity. Structures of proteins available with the following PDB codes: 2OW4, 2OVU, 2IDZ, 2GSJ, 2G85, 2A4L, 1ZTB, 1Z99, 1WE2, 1M73, 1FLH, and 1FHJ.
If you have interest in the above described research lines, please feel free to contact Prof. Walter F. de Azevedo Jr. e-mail: walter@azevedolab.net
Address
Pontifical Catholic University of Rio Grande do Sul, PUCRS.
Ipiranga Avenue, 6681 Partenon - Porto Alegre/RS. Brazil. Zip Code: 90619-900.
This site was designed by Dr. Walter F. de Azevedo Jr.