Structural Modeling


We focused not only in the individual characterization of molecular systems but as well in the development of new and crucial methodologies to tackle specific problems in the area of structural computational biology. In the past we have developed a new methodological approach to calculate the binding free energy upon alanine mutation of residues at a protein-protein interface – compASM (Computational Alanine Scanning Mutagenesis). It has been applied since then very successfully to interfaces in a variety of systems (e.g. MDM2/P53HEL/antibody FvD1.3, HEL/HyHEL-10ZipA/FtsZIgG1/Streptococcal Protein G) allowing to highlight key features of complex formation.  We compared our approach against Thermodynamic Integration (TI), and our results demonstrated that the much faster compASM protocol gives results at the same level of accuracy as the TI method, but at a fraction of the computational time required to run TI. We have also studied in detail the physio-chemical characteristics of the aromatic residues due to their high prevalence as HS at protein-protein interfaces. A new VMD plugin was built and is now available for the scientific community (CompASM). Several Solvent Accessible Surface Area (SASA) features were measured and their role in the determination of the binding Hot-Spots statistically evaluated. The combination of these features was also analyzed by a support vector machine learning (SVM) algorithm, which led to an accurate new model for predicting these crucial residues: SBHD (SASA-Based Hot-Spot Detection). More recently we launched a new web-server to predict HS with a very high accuracy and specificity (SPOTON).


We are also working in PPIs involving G-Protein Coupled Receptors (GPCRs). In our previous research we have contributed to the discovery of asymmetrical signaling through GPCR dimers and determined models for oligomeric GPCRs assemblies, produced comprehensive classifications of GPCR/G-protein interfaces that determine subtype selectivity, identified functionally critical region on arrestin structure that can be targeted with drugs or chemical tools for functional modulation and produced computational protocols to study GPCR function and mechanism. Based on this experience and the unprecedented progress in the determination of detailed molecular structures of GPCRs, G-proteins, and members of the Arr family, we are now involved in work that aims to illuminate the molecular mechanisms of signaling selectivity with powerful computational methods by exploring the dynamic properties of the complex systems in their natural environment. Moreover, the specific focus on catecholamine-binding GPCRs, and in particular the dopamine receptor type 2 (DR2) is poised to bring new understanding about a physiologically and pharmacologically important class of drug targets. The mechanistic and structural details that underlie DR2 mode of function are unknown, both at the receptor level and with regard to the functional selectivity between the two major binding partners (G-protein and Arr-s). We are working to decipher the structural and dynamic determinants of this receptor with well-established physiological roles in a variety of cognitive emotional and motor functions.


In the area of protein-small ligand interactions, we have been working on various systems: the PICK1 PDZ domain; the nitric oxide synthase isoforms; the TEM family of β-lactamases; the nicotinamide phosphoribosyltransferases and nicotinamidases; and the Dopamine 2 Receptor. All these studies result from an interchange of ideas with experimental groups with as a final goal the production of better and more specific small-molecules.