Computational mapping of proteins for the binding of ligands

描述（由申请人提供）：该提案要求延长“用于配体结合的蛋白质计算图谱”的资助。全局映射使用分子探针（小分子或官能团）对靶蛋白表面进行采样，以确定潜在的有利结合位置。该方法基于X射线和NMR筛选研究，显示蛋白质的结合位点也结合各种各样的片段大小的分子。我们已经开发了基于快速傅里叶变换（FFT）相关方法的多阶段映射算法FTMAP（可在http：//ftmap.bu.edu/作为服务器获得），映射了大量蛋白质，并建立了可药用性的标准。这种更新的总体目标是将该方法扩展到预测特定官能团的偏好，并进一步提高预测的鲁棒性。第一个目标将通过开发包括许多不同探针中的每个重要官能团的大型探针库、用于识别在相同位置结合的探针中出现的重叠官能团的特殊（“功能”）聚类算法以及迭代映射来实现 该算法增强了包含前一轮中鉴定的官能团的探针中的探针组。一旦发现官能团，将使用随机路线图模拟验证它们对特定位点的偏好，以确保它们具有降低的逃离位点的倾向。该方法将直接在基于FFT的映射算法中考虑配体和蛋白质的灵活性。这将进一步提高在具有挑战性的问题中的位点预测的可靠性，将允许使用更复杂的探针，并且将使我们能够考虑在我们的映射服务器中的灵活性，该映射服务器目前假设刚性蛋白质。作为该提案的一个重要组成部分，我们将整合计算和X射线晶体学映射方法，通过计算映射预先选择用于基于X射线筛选的化合物。该方法将通过映射模型系统和当前制药利益的系统进行验证。预测和观察到的相互作用的比较将有助于我们更好地理解片段大小的分子与蛋白质功能位点结合的原理，并进一步改进映射。我们还将研究一些有趣的分子识别问题，包括蛋白质-蛋白质界面中可药用位点的识别，变构位点的识别;将映射扩展到膜蛋白;以及确定大环化合物的重要官能团。

DESCRIPTION (provided by applicant): 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 http://ftmap.bu.edu/) 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.