Vajda, Sandor - Boston University - Department of Biomedical

个人简介

Ph.D., Chemistry, Hungarian Academy of Science  M.Sc., Applied Mathematics, Éötvös Lorand University (Hungary)  M.Sc., Electrical Engineering, Gubkin Institute (Former USSR)

研究领域

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Computational methods have the potential for substantially improving the efficiency and reducing the costs of biomedical and pharmaceutical research. However, the capabilities of some molecular modeling tools are frequently overstated, reducing the credibility of the approach. Our goal is developing reliable and robust methods based on rigorous biophysical and engineering principles, and to implement them as web-based servers to be used and thereby validated by the broad biomedical community. We have developed methodologies for predicting the structure of protein complexes that have consistently been among the best performers in the worldwide protein docking experiment called CAPRI (see http://www.ebi.ac.uk/msd-srv/capri/). Some of the methods are implemented in our protein docking server ClusPro (http://cluspro.bu.edu/), which was the first automated server, and according to the CAPRI results yields fairly good results. ClusPro has almost 4000 registered users, and we run about 800 docking jobs each month. Structures of complexes, generated by the server, have been reported in over 190 research papers. Docking requires large CPU times even on supercomputers, and we continuously improve the algorithms to increase both speed and accuracy for applications to biomedical research and biotechnology. For the analysis of interactions between proteins and small ligands our main contribution has been the development of the FTMap algorithm (http://ftmap.bu.edu/) for computational solvent mapping. The method places molecular probes – small molecules and functional groups – on a protein surface for the identification and characterization of binding sites. Since mapping provides very useful information for drug design, we have been developing and testing modeling and design tools for the pharmaceutical industry, in collaboration with academic labs and companies. Our main goal is to build an in silico methodology that will substantially reduce the very high costs of early stage drug discovery.