The proposal requests the renewal of the grant "Computational Mapping of Proteins for the Binding of Ligands". Mapping globally samples the surface of target proteins using molecular probes - small molecules or functional groups - to identify potentially favorable binding positions. The method is based on X-ray and NMR screening studies showing that the binding sites of proteins also bind a large variety of fragment-sized molecules. We have developed the multi-stage mapping algorithm FTMAP (available as a server at based on the fast Fourier transform (FFT) correlation approach, mapped a large number of proteins, and established criteria for druggability. The general goals of this renewal are extending the method toward predicting preferences for specific functional groups and further improving the robustness of the predictions. The first goal will be achieved by developing large probe libraries that include each important functional group in many different probes, a special ("functional") clustering algorithm to identify the overlapping functional groups that occur in probes binding at the same location, and an iterative mapping algorithm which enhances the probe set in probes containing the functional groups identified in the previous round. Once functional groups are found, their preference for the particular site will be validated using stochastic roadmap simulations to assure that they have reduced tendency to escape from the site. The method will account for ligand and protein flexibility directly within the FFT-based mapping algorithm. This will further improve the reliability of site prediction in challenging problems, will allow for the use of more complex probes, and will enable us to consider flexibility in our mapping server which at present assumes a rigid protein. As an important part of the proposal, we will integrate computational and X-ray crystallographic mapping methods by pre-selecting the compounds for the X-ray based screening by computational mapping. The approach will be validated by mapping model systems and systems of current pharmaceutical interests. The comparison of predicted and observed interactions will help us to better understand the principles that govern the binding of fragment-sized molecules to functional sites of proteins, and to further improve the mapping. We will also study a number of interesting molecular recognition problems, including the identification of druggable sites in protein-protein interfaces, the identification of allosteric sites;extension of mapping to membrane proteins;and determining the important functional groups of macrocyclic compounds.

Public Health Relevance

Mapping methods place molecular probes - small molecules or functional groups - on the surface of proteins in order to identify the most favorable binding positions, and provide information on the druggability of such sites. Here we propose extending the algorithm to determining binding specificity, comparing the results to experimental data, and applying the method to important biological problems.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Macromolecular Structure and Function D Study Section (MSFD)
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Preusch, Peter C
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Boston University
Engineering (All Types)
Schools of Engineering
United States
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