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A Structural Systems Biology Approach to Drug Discovery

药物发现的结构系统生物学方法

基本信息

批准号：
8798517
负责人：
Rommie E Amaro
金额：
$ 62万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-30 至 2016-10-31
项目状态：
已结题

项目摘要

DESCRIPTION (Provided by the applicant) Abstract: With petascale computing power on the immediate horizon, computational studies have the opportunity to make unprecedented contributions to drug discovery efforts. Although ligand flexibility is routinely accounted for in computer-aided drug design (CADD) methodologies, incorporating receptor flexibility and system complexity remains an important challenge. The next frontier in flexible receptor methodologies is the integration of cutting-edge physics-based computational methods into the CADD techniques, in conjunction with the use of more complex biological systems. The incorporation of powerful new predictive theoretical tools into flexible receptor methodologies for ligand discovery and design will provide an important shift to the CADD field, enabling the discovery of novel ligand-binding modes and expediting the estimation of more accurate ligand free energies of binding. My vision is to drive the computer-aided drug design field towards a systems biology approach, where multiple proteins, and the RNAs they bind, are targeted - thus challenging the "one-target, one- disease, one-drug" paradigm. The new approaches I envision will integrate multiple time and length scales and take explicit advantage of the new structural information yielded by these algorithms. These investigations will push important frontiers in our understanding of biology, ultimately opening new pathways to more effective therapeutics.

描述（由申请人提供）摘要：随着千万亿次计算能力的出现，计算研究有机会为药物发现工作做出前所未有的贡献。虽然配体的灵活性通常占计算机辅助药物设计（CADD）方法，结合受体的灵活性和系统的复杂性仍然是一个重要的挑战。灵活受体方法学的下一个前沿是将尖端的基于物理学的计算方法整合到CADD技术中，并结合使用更复杂的生物系统。将强大的新的预测理论工具结合到灵活的受体方法中，用于配体的发现和设计，将为CADD领域提供重要的转变，从而能够发现新的配体结合模式，并加快估计更准确的配体结合自由能。我的愿景是将计算机辅助药物设计领域推向系统生物学方法，其中多个蛋白质及其结合的RNA被靶向-从而挑战“一个靶标，一种疾病，一种药物”的范式。我设想的新方法将整合多个时间和长度尺度，并明确利用这些算法产生的新结构信息。这些研究将推动我们对生物学理解的重要前沿，最终为更有效的治疗开辟新途径。