The Bioinformatics Unit of the CBMSO was founded in 2002 by Dr.Ángel Ramirez Ortiz. After the sad and premature loss of Dr.Ortiz in may 2008, we have been recognized as an emerging group in April 2009 under the direction of Dr. Ugo Bastolla. Our line on drug design is directed by Dr. Antonio Morreale. We also provide Bioinformatics facility to the CBMSO.
Our group is active in the field of the computational structural biology of proteins. Our main research lines are the computational study of protein structures, stability and evolution, drug design by docking and virtual screening, and theoretical ecology, in particular applied to bacterial communities. We summarize these lines below.
Our main goal is the quantitative and, if possible, predictive understanding of how proteins change conformation during their biological activity and their evolution. This has applications for rationalizing the mechanism of protein action, improving homology-based protein structure prediction, and improving models of protein-ligand docking. To pursue these goals, we have developed a new model of normal mode analysis of protein equilibrium dynamics that uses torsion angles as degrees of freedom. This method allows to treat large systems much more rapidly and accurately than previous ones, producing physically realistic movements of up to several Å at once. We are currently working at a force field that allows us to predict conformation changes upon ligand binding in order to improve our drug design protocol and to refine homology models by predicting rearrangements of protein structure upon mutation.
Our second goal consists in automatically classifying protein domains using their structure similarity and analyzing the structural and functional changes in their evolution. This work lead us to recognize that protein domains can be classified on a tree only for large enough similarity, whereas for lower but significant similarity their relationships must be described as a network, both due to their evolutionary origin through fragment assembly even below the domain level and because of evolutionary accelerations upon function changes. We are currently using our normal modes model to quantitatively characterize function change and function conservation in the evolution of proteins.
Another goal of our research consists in improving the methods for predicting protein folding stability that we developed some time ago. This has application for protein structure prediction through threading, and for predicting protein-protein interactions through the analysis of correlated mutations. We have applied these methods to characterize selective pressures favoring stability against unfolding (positive design) and misfolding (negative design), and fast folding. In this way, we found an interesting relationship between folding stability, population size and mutation bias. Our mathematical model suggests that this interplay can explain why intracellular bacteria with reduced effective population size tend to evolve with a mutation bias favoring A+T nucleotides, hence more hydrophobic proteins. We are currently investigating the implications of these results for the evolution of bacterial genomes.
We are also interested in the properties of the hundreds of proteins that form the Centrosome. We have found that these proteins are predicted to be much more disordered, coiled-coil and phosphorylated than control proteins of the same organism. These properties confer structural and regulatory plasticity to the Centrosome and are enhanced for organisms with a larger number of cell type, and they arose in evolution mainly through large insertions of disordered fragments that happened more frequently in evolutionary branches where the number of cell types increased significantly. We are further investigating the relationship between disorder, phosphorylation, electric charge, and the size of centrosomal proteins.
Flowering plants and insects are very diverse groups, characterized by mutualistic interactions (advantageous for both partners) with nested structure, in the sense that specialist species tend to interact with generalist species. We developed and solved a mathematical model that predicts that nested mutualistic interactions decrease the effective competition of a species community and allow it to maintain a larger biodiversity.
We are applying these results for studying the interactions between bacterial taxons, which we predict from co-occurrence matrices derived from metagenomic studies. We have found that there are many more aggregations of taxons (“mutualism”) than exclusions (“competition”), and we see that such mutualistic interactions favor the cosmopolitanism of bacteria, i.e. their capacity of living in quite different environments. We have developed an algorithm for reconstructing bacterial communities that present significant aggregation starting from 16s RNA data.
The second main line of research of our group consists in structure-based drug design. The senior investigator who directs this line, Dr. Antonio Morreale, has a strong experience in the field since several years. During the last years we have developed an integrated computational platform to perform massive Virtual Screening (VS) experiments, which allow to extract from a large database of small molecules the most promising candidates able to interact with a protein of clinical interest. Our Virtual Screening Data Management on an Integrated Platform (VSDMIP) has been proved to be an useful tool for developing new methods, and it is giving several patents in collaboration with experimental groups.
Another area of great interest in the medicinal chemistry field is 3D-QSAR (3-Dimensional Quantitative Structure-Activity Relationships). In this regard, we have developed gCOMBINE, a Java-written graphical user interface (GUI) for performing COMparative BINding Energy (COMBINE) analysis on a set of ligand-receptor complexes that allows predicting QSAR, with the aim to understand the molecular basis of specificity of ligand-receptor binding affinities and to guide further molecular modifications that improve binding affinity and/or selectivity.
Finally, molecular modeling studies are being conducted in collaboration with different research groups within and outside the CBMSO, and mainly involve molecular mechanics and dynamics simulations followed by quantitative free energy calculations. Our interest for the future is to continue with these three research activities as well as to introduce new methodological advances, in particular modeling flexible receptors using the elastic network framework described in the first section in order to predict the conformation changes of the protein upon ligand binding.