LEARNING DRUG SPECIFICITY FROM PROTEIN FAMILIES

从蛋白质家族中了解药物特异性

• 批准号: 6143503
• 负责人: RICHARD Masten FINE
• 金额: $ 9.79万
• 依托单位: STRUCTURAL PROTEOMICS, INC.
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2002-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6143503
• 关键词: binding proteins; conformation; ligands; method development; protein binding; protein sequence; protein structure; structural biology

We propose to develop and apply methods for docking ligand fragments into families of proteins, for two specific purposes. The first of these is to develop methods to identify patterns which are unique to a specific family member, to avoid cross-reactivity in the design of highly specific drugs. The second is to develop methods to identify patterns of similarity, which can be used when multiple alleles of a target protein are known to help avoid the development of resistant strains of infectious disease organisms. The work is an extension of and suggested by our current efforts, funded by a separate Phase II SBIR, to develop methods for comparing surface features of proteins within and across families. The work proposed entails the development of (a) new docking methods which can directly use feature comparisons within families; (b) novel data segmentation tools based on unsupervised learning methods to assess similarity and differences obtained from docking results on individual family members; and (c) the development of new classification methods based on Vector Support Machines and other methods for optimizing separation functions to distinguish correctly docked from incorrectly docked structures within the families. While the work is exploratory, if successful it can provide a focussed set of powerful drug-discovery tools to take advantage of the increasingly rich amount of information on protein sequence and structure emerging from genomics and structural genomics efforts. Software developed will be offered for sale, and applied to available crystal structures to develop specialized databases which can be of immediate use in drug discovery. PROPOSED COMMERCIAL APPLICATIONS: The goals of the grant are to address key issues in current drug discovery, namely, specificity, cross-reactivity, and avoiding resistance in infectious disease organisms to new drugs. The methods are strongly enabled by the accumulation of knowledge from genomics and structural genomics efforts. We believe that these and similar methods will be necessary to reap the commercial and sociological benefit promised by this extraordinary accumulation of knowledge.

我们建议开发和应用将配体片段对接到蛋白质家族中的方法，用于两个特定目的。其中第一个是开发方法，以确定特定家庭成员特有的模式，以避免在设计高度特异性药物时发生交叉反应。第二是开发识别相似模式的方法，当已知目标蛋白的多个等位基因时，可以使用这种方法，以帮助避免出现传染病生物体的抗药性菌株。这项工作是我们目前努力的延伸和建议，由单独的第二阶段SBIR资助，开发用于比较家族内和家族间蛋白质表面特征的方法。建议的工作包括：(A)开发新的对接方法，可以直接使用家庭内部的特征比较；(B)基于无监督学习方法的新数据分割工具，以评估对接结果对单个家庭成员的相似性和差异性；以及(C)开发基于向量支持机的新分类方法，以及用于优化分离函数的其他方法，以区分正确对接和错误对接的家庭内部结构。虽然这项工作是探索性的，但如果成功，它可以提供一套集中的强大的药物发现工具，以利用基因组学和结构基因组学努力产生的关于蛋白质序列和结构的日益丰富的信息量。开发的软件将出售，并应用于现有的晶体结构，以开发专门的数据库，可以立即用于药物发现。拟议的商业应用：赠款的目标是解决当前药物发现中的关键问题，即特异性、交叉反应性和避免传染病生物对新药的耐药性。从基因组学和结构基因组学工作中积累的知识有力地使这些方法成为可能。我们认为，这些和类似的方法将是必要的，以获得这种非凡的知识积累所承诺的商业和社会学利益。