Laboratory of Computational Systems Biology

This is the website of the Computational Systems Biology research group of Prof. Walter F. de Azevedo Jr.       

We are a research group working on the development of computer models to assess intermolecular interactions involving biomolecules and potential ligands. We have been working on the creation of novel computational models for unraveling the molecular mechanisms underlying enzyme inhibition and protein-ligand interactions. We 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 new drugs. 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, since it allows us to computationally test different scenarios where a potential new drug may interact with a protein target.

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.

Research [>>]

The availability of more than 150,000 experimentally determined macromolecule structures paved the way to investigate computer models to predict three-dimensional structures of proteins and to simulate protein-ligand interactions. Furthermore, integrating protein structures and binding affinity information creates a favorable environment for the development of specific scoring functions to predict ligand-binding affinity.  We are working on the development of computational tools to analyze protein-ligand interactions.  Our research interests are interdisciplinary with five major emphases:   

• Protein-Ligand Interactions (Wiki, Google Scholar)   
• Molecular Docking (Wiki, Google Scholar)   
• Bio-inspired Computing (Wiki, Google Scholar)   
• Computational Systems Biology (Wiki, Google Scholar)   
• Molecular Simulations (Wikibook, Google Scholar)   

Additionally, we have interests in following projects: electrostatics potential energy function for protein-ligand complexes, development of quantum mechanics models to address protein-ligand interactions, development of hybrid computational models involving quantum computing and artificial intelligence, creation of new computational models to simulate action potentials in neurons, creation of targeted  scoring functions, and development of deep learning applications to predict the behavior of complex systems.

If you have interest in the above described projects, 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.