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

• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10363369
• 关键词: Algorithms; Amino Acid Motifs; Amino Acid Sequence; Amino Acids; Area; Biological; Bypass; Characteristics; Chemicals; Classification; Communities; Complex; Computing Methodologies; Conserved Sequence; Custom; Data; Databases; Development; Disease; Drug Binding Site; Drug Design; Drug Industry; Drug Targeting; Ensure; Foundations; Goals; Knowledge; Label; Length; MAPK3 gene; Methods; Mutation; Paper; Peptide Hydrolases; Pharmaceutical Preparations; Phosphoric Monoester Hydrolases; Phosphotransferases; Play; Proteins; Publishing; Research; Research Activity; Role; Science; Serine Protease; Shapes; Side; Structural Protein; Structure; Techniques; Time; Toxic effect; Triad Acrylic Resin; base; chymotrypsin; computerized tools; database structure; design; drug development; drug discovery; drug structure; innovation; insight; interest; knowledge base; nitration; novel therapeutics; protein function; protein structure; protein structure function; public health relevance; receptor; simulation; spatial relationship; three dimensional structure; tool

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：开发一种新的计算方法，用于预测药物-靶标相互作用， 基于TSR的三维结构表示 项目概要： 蛋白质和药物的三维结构在药物设计和发现中起着关键作用。同时，它也 提取有意义的结构信息并将其转换为知识非常具有挑战性。在过去的四十年里， 自从第一个自动结构化方法的发展以来，已经有大约200篇论文被 使用不同的结构表示法发表。每一种都有其独特性和局限性。我们的项目 添加到现有的知识库与新的TSR（三角空间关系）为基础的表示， 使用Cα原子的蛋白质三维结构。以蛋白质的Cα原子为顶点构建三角形。 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 基于规则公式的长度、角度和顶点标签，确保将相同的键分配给 在蛋白质中有相同的TSRs。由于密钥是在三个残基之间构造的，因此考虑它们 残基间键。我们的结果清楚地表明，成功的蛋白质聚类， 在大多数情况下的功能分类和已知的和新的结构基序的成功鉴定。 虽然我们已经成功地使用Cα，但两个事实激励我们继续开发残基内密钥 来表示侧链的结构。第一个事实是，当我们研究丝氨酸蛋白酶三联体时， 我们发现了一个代表两个不同的胰凝乳蛋白酶三联体的密码。然而，其中只有一个是 当考虑侧链之间的相互作用时，是真正的三元组。第二个事实是，毒品往往 与蛋白质的侧链有密切的相互作用。因此，本提案的总体目标是： 一种有效的方法，用于表示蛋白质和药物的三维结构，为研究定制， 药物和蛋白质的相互作用蛋白质和药物结构的表示方法，以及预测药物和 蛋白质相互作用是创新的。我们已经使我们的计算工具可以用于科学研究， 社区，并将继续这样做。我们的中心假设是，复杂的三维结构可以分为 一组三角形，最简单的图元来捕捉形状。每个三角形都被转换为一个整数， 独特地捕捉其基本特征。这意味着三维结构可以用 多整数集（密钥袋）。这一建议的理由来自我们的研究结果， 使用残基间键来获得基于TSR的蛋白质结构表示。该方法建立在 该TSR思想相对于现有方法具有重要的优点。将追求五个具体目标： 一种基于TSR的氨基酸关键词表示方法及其表示机制 对于药物，整合残基间和残基内的键，用于鉴定药物结合位点，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 对蛋白质三维结构比较、药物加速等领域的研究具有重要影响 制药业的发展。