Development of a new computational method for predicting drug - target interactions using a TSR-based representation of 3-D structures

标题：开发一种新的计算方法来预测药物与靶点的相互作用 基于TSR的三维结构表示 项目总结： 蛋白质和药物的三维结构在药物设计和发现中起着至关重要的作用。同时，它也是 提取有意义的结构信息并将其转化为知识是非常具有挑战性的。在过去的四十年里， 自从开发出第一个自动结构方法以来，已经发表了大约200篇论文 使用不同的结构表示法发布。每一种都有其独特性和局限性。我们的项目 使用新的基于TSR(三角空间关系)的表示向现有知识库添加 蛋白质的三维结构采用C-α原子。三角形是以蛋白质的C-α原子为顶点构成的。 每个三角形都由一个整数表示，我们将其表示为“key”。密钥是使用 基于基于规则的公式的长度、角度和顶点标签，确保将相同的关键字分配给 蛋白质间的TSR完全相同。由于密钥是在三个残数之间构造的，因此它们被视为 残留物间密钥。我们的结果清楚地证明了与其相匹配的蛋白质成功地聚集在一起 在大多数情况下进行功能分类，并成功识别已知和新的结构基序。 尽管我们已经成功地使用了Cα，但有两个事实激励我们继续开发残基内部密钥 来表示侧链的结构。第一个事实是，当我们研究三联体丝氨酸蛋白酶时， 我们找到了一把钥匙代表了两种不同的胰凝乳酶。然而，其中只有一个是 当考虑侧链之间的相互作用时，这是真正的三轴运动。第二个事实是，毒品往往 与蛋白质侧链有密切的相互作用。因此，这项提案的总体目标是发展 一种为蛋白质和药物研究定制的有效三维结构表示方法 药物和蛋白质的相互作用。表示蛋白质和药物结构的方法，以及预测药物和 蛋白质的相互作用是创新的。我们已经为科学工作者提供了我们的计算工具 社区，并将继续这样做。我们的中心假设是复杂的3-D结构可以分为 一组三角形，捕捉形状的最简单的基本体。每个三角形都转换为一个整数，该整数 独一无二地捕捉到了它的本质特征。这意味着一个三维结构可以用一个 多个整数集(一包密钥)。这项建议的理由是根据我们的研究结果得出的 使用残基间键获得基于TSR的蛋白质结构表示。该方法基于 与现有的方法相比，这种TSR思想具有重要的优势。我们会追求五个具体目标： 基于TSR的氨基酸关键表示及其表示机制的研究进展 对于药物，整合残基间和残基内的键以识别药物结合部位，预测药物靶点 相互作用，以及计算计算与实验数据的整合。拟议的研究将 对比较蛋白质三维结构和加速药物领域的研究产生了重大影响 医药行业的发展。

Title: Development of a new computational method for predicting drug - target interactions using a TSR-based representation of 3-D structures Project Summary: Protein and drug 3-D structures play a pivotal role in drug design and discovery. At the same time, it is very challenging to extract meaningful structural information and convert it to knowledge. In the last forty years, since the development of the first automated structural method, approximately 200 papers have been published using different representations of structures. Each has its uniqueness and limitations. Our project adds to the existing knowledge base with a new TSR (Triangular Spatial Relationship)-based representation of protein 3-D structures using Cα atoms. Triangles are constructed with the Cα atoms of a protein as vertices. Every triangle is represented by an integer, which we denote as "key". A key is computed using the length, angle and vertex labels based on a rule-based formula, which ensures assignment of the same key to identical TSRs across proteins. Since the keys are constructed among three residues, they are considered inter-residue keys. Our results clearly demonstrate successful clustering of proteins that matches their functional classifications in most cases and successful identification of known and new structural motifs. Although we have been successful using Cα, two facts inspired us to continue developing intra-residue keys to represent structures of side chains. The first fact, which emerged when we studied triad of serine proteases, is that we found a key that represents two different triads of chymotrypsin. However, only one of them is the true triad, when the interactions between the side chains are considered. The second fact is that drugs often have close interactions with side chains of proteins. Thus, the overall objectives of this proposal are to develop an effective method for representing 3-D structures of proteins and drugs that is customized for the study of drug and protein interactions. The ways to represent protein and drug structures, and to predict drug and protein interactions, are innovative. We have made our computational tools available for the scientific community and will continue to do so. Our central hypothesis is that complex 3-D structures can be divided into a set of triangles, the simplest primitives to capture the shape. Each triangle is converted to an integer that uniquely captures its essential characteristics. It means that a 3-D structure can be represented by a multiset of integers (bag of keys). The rationale of this proposal is derived from the results of our studies that used inter-residue keys to obtain TSR-based representation of protein structures. The method built based on this TSR idea has important advantages over the existing methods. Five specific Aims will be pursued: development of TSR-based key representation of amino acids and corresponding representation mechanism for drugs, integration of inter- and intra-residue keys for identifying drug-binding sites, predicting drug – target interactions, and integration of computational calculations with experimental data. The proposed research will have significant impacts on research in the fields of comparing protein 3-D structures and accelerating drug development for pharmaceutical industries.