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

开发一种新的计算方法，使用基于 TSR 的 3-D 结构表示来预测药物-靶点相互作用

基本信息

批准号：
10363369
负责人：
Wu Xu
金额：
$ 42.05万
依托单位：
UNIVERSITY OF LOUISIANA AT LAFAYETTE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2024-08-31
项目状态：
已结题

项目摘要

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.

标题：开发用于预测药物的新计算方法 - 使用基于TSR的3D结构表示项目摘要：蛋白质和药物3-D结构在药物设计和发现中起关键作用。同时，是提取有意义的结构信息并将其转换为知道的非常挑战。在过去的四十年中，自从第一个自动结构方法的开发以来，大约有200篇论文使用不同的结构表示。每个都有其独特性和局限性。我们的项目通过新的TSR（三角形空间关系）的基于新的TSR的表示基础使用Cα原子的蛋白质3-D结构。三角形由蛋白质的Cα原子作为顶点构建。每个三角形都由整数表示，我们将其表示为“钥匙”。使用键计算长度，角度和顶点标签基于基于规则的公式，该公式可确保分配相同的密钥跨蛋白质相同的TSR。由于钥匙是在三个保留中构建的，因此被认为占用键。我们的结果清楚地表明了与他们的蛋白质的成功聚类在大多数情况下，功能分类以及已知和新结构基序的成功识别。尽管我们已经成功使用了Cα，但两个事实激发了我们继续开发占用物内键代表侧链的结构。当我们研究串行蛋白酶三合会时出现的第一个事实，是我们找到了一个代表两个不同三合会的胰胆红蛋白三合会的钥匙。但是，其中只有一个是当考虑侧链之间的相互作用时，真正的三合会。第二个事实是毒品经常与蛋白质的侧链有密切的相互作用。这是该提议的总体目标是发展代表蛋白质和药物的3-D结构的有效方法，该方法是为研究而定制的药物和蛋白质相互作用。表示蛋白质和药物结构的方法，并预测药物和蛋白质相互作用，具有创新性。我们使我们的计算工具可用于科学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，是捕获形状的最简单原始图。每个三角形都转换为一个整数独特地捕获其基本特征。这意味着3D结构可以由整数（钥匙袋）的多组。该提案的基本原理来自我们的研究结果使用的用于获得基于TSR的蛋白质结构表示的固定键。基于这种TSR的想法比现有方法具有重要的优势。将追求五个具体目标：开发基于TSR的氨基酸的关键表示和相应的表示机制对于药物，整合用于鉴定药物结合位点的残留键和残留键，预测药物 - 靶标相互作用以及计算计算与实验数据的集成。拟议的研究将对比较蛋白质3-D结构和加速药物的研究产生重大影响制药行业的发展。