Computational mapping of proteins for the binding of ligands

 描述(由申请者提供)：该提案要求续期“用于配体结合的蛋白质计算图谱”的资助。使用片段大小的分子探针在全球范围内对目标蛋白质的表面进行采样。作图的总体目标是确定结合热点，即蛋白质中主要贡献结合自由能的区域，并识别优先与这些热点结合的片段。研究热点的主要优点是它们比结合位点更保守。我们追求四个目标。首先，通过在全局映射算法中直接执行侧链搜索来提高柔性映射的效率，并将该算法扩展到具有柔性环的模型和同源模型。该方法将用于大规模的测绘计算，以回答有趣的生物学问题，例如在动态组中是否存在可用药的隐蔽位置。其次，我们开发了一种计算和实验方法的有效组合，用于识别与给定热点结合的片段，并与合作者合作，试图在生成改进计算方法所需的片段结合数据的过程中，为一些药物靶标找到片段。第三，我们开发了一种虚拟片段筛选算法，以减少需要进行实验测试的片段数量。由此产生的方法将为基于片段的配体发现(FBLD)提供直接输入，从而降低该方法的高昂成本，并使其更容易为学术实验室所用。第四，我们将通过显式的溶剂化模拟来进一步提高映射的精度。这种方法的基础是将蛋白质分解成具有多个构象的柔性侧链，其余的蛋白质，两个拷贝 一种具有多种构象的探针，以及两个水分子。我们使用非常高效的快速傅立叶变换相关方法对所有对的所有可行的相对取向的相互作用能量进行采样，并将详细的能量网格存储在使用小波变换压缩的查找表中。由于两两相互作用能的可加性，可以通过将预先计算的内能分量与相互作用能相加来快速评估任何构象的能量，所有这些都是从查找表中提取的。能量表的使用将加快配分函数的计算，使用蒙特卡罗模拟来细化碎片位置，并通过随机路线图模拟来确定逃逸时间。所有新算法都将在我们的FTMap服务器(http://ftmap.bu.edu)，)中实现，该服务器已经拥有1200多名注册用户。

 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 fragment sized molecular probes. The general goals of mapping are determining binding hot spots, i.e., regions of proteins that are major contributors to the binding free energy, and identifying fragments with preferential binding to these hot spots. The main advantage of studying hot spots is that they are more conserved than binding sites are. We pursue four aims. First, we improve the efficiency of flexible mapping by performing side chain search directly within the global mapping algorithm, and extend the algorithm to models with flexible loops and to homology models. The method will be used for large scale mapping calculations to answer interesting biological questions such as the existence of druggable cryptic sites in the kinome. Second, we develop an effective combination of computational and experimental methods for the identification of fragments binding to a given hot spot, and working with collaborators attempt to find fragment hits for a number of drug targets, in the process generating fragment binding data needed for improving computational methods. Third, we develop an algorithm for virtual fragment screening in order to reduce the number of fragments that need to be experimentally tested. The resulting methods will provide direct input for fragment based ligand discovery (FBLD), thereby reducing the high costs of the approach and making it more accessible to academic laboratories. Fourth, we will further improve the accuracy of mapping by explicit modeling of solvation. The method is based on decomposing the protein into flexible side chains with multiple conformers, the rest of the protein, two copies of a probe in a number of conformations, and two water molecules. We sample the interaction energies for all feasible relative orientations of all pairs using very efficient fast Fourier tranform correlation methods, and store the detailed energy grids in lookup tables, compressed using wavelet transforms. Due to the additivity of pairwise interaction energies, the energy of any conformation can be quickly evaluated by adding pre-calculated internal energy components to interaction energies, all extracted from lookup tables. The use of energy tables will speed up the calculation of partition functions, refinement of fragment positions using Monte Carlo simulations, and determining escape times by stochastic roadmap simulation. All new algorithms will be implemented in our FTMap server (http://ftmap.bu.edu), which already has over 1200 registered users.