"Computational scientists solve tomorrow's problems with yesterday's computers; computer scientists seem to do it the other way around."--anonymous.

Laboratory of Computational Systems Biology 

Welcome to 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 computer models to simulate natural systems. Our research interests are interdisciplinary with five major emphases:     

-Protein-Ligand Interactions

-Molecular Docking,

-Bio-inspired Computing

-Computational Systems Biology, and 

-Computational Life Sciences.

Our main goal is the development of research projects focused on 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. These computational models can be used to predict 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. 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, With such methodologies, we adopt the principles described in the seminal work of Stephen WolframA New Kind of Science, where computer simulations of complex systems play a pivotal role to bring deeper insights into the functioning of the nature. 

We envisage protein-ligand interaction as a result of the intersection between the protein space and the chemical space, 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 new semi-empirical force fields to predict binding affinity with superior predictive power when compared with classical methodologies.                        

Here you find overall information about our research projects, publications, and teaching material.    


Research Projects     

The availability of more than 120,000 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 specific scoring functions to predict ligand-binding affinity.  We are working in the development of computational tools to analyze protein-ligand interactions to carry out protein-ligand docking simulations and to predict three-dimensional structures of proteins.         

Re-dock result for the structure of cyclin-dependent kinase 2A4L. Scatter plot and ROC Curve generated by SAnDReS.

A small sample of our research projects running right now at azevedolab.net.   

   -SAnDReS   PubMed   
   -Taba: A Tool for Analysis of  Binding Affinitty   PubMed
   -Fine Tuning of Scoring Functions   PubMed  
   -Application of Molecular Docking to Nanobiotechnology   PubMed    
   -Protein-Ligand Docking Simulations   PubMed   
   -Machine Learning Algorithms Applied to Systems Biology   PubMed     
   -Machine Learning for Development of Scoring Functions   PubMed    
   -Development of Scoring Functions for CDK   PubMed   
   -Machine Learning Algorithms Applied to Bioinformatics   PubMed   




Contact Us

If you have any question, please feel free to contact Prof. Walter F. de Azevedo Jr. e-mail: walter@azevedolab.net

Join Us

If you are interested in computational systems biology please contact us (e-mail: walter@azevedolab.net).