AF: Small: Accurate, Biochemically-Relevant, and Robust Scoring Functions for Protein-Ligand Binding Affinity Prediction

AF：小：用于蛋白质-配体结合亲和力预测的准确、生化相关且稳健的评分功能

• 批准号: 1117900
• 负责人: Nihar Mahapatra
• 金额: $ 32.6万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117900&HistoricalAwards=false
• 关键词: AF; Small; Accurate; Biochemically; Relevant

Protein-ligand binding affinity is the principal determinant of many vital processes, such as cellular signaling, gene regulation, metabolism, and immunity, that depend upon proteins binding to some substrate molecule. Consequently, it has a central role in drug design. Due to prohibitive costs and delays associated with experimental drug discovery, academia and pharmaceutical and biotechnology companies rely on virtual screening using computational molecular docking. Typically, this involves docking of tens of thousands to millions of ligand candidates into a target protein receptor?s binding site and using a suitable scoring function to evaluate the binding affinity of each candidate to identify the top candidates as leads or promising protein inhibitors. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein?s binding site and its computational complexity have a significant bearing on the accuracy and throughput of virtual screening. However, current state-of-the-art scoring functions have a number of deficiencies, including either mediocre accuracy for affinity prediction or low throughput, inconsistent accuracy, inflexibility in accuracy-throughput trade-off provided, and reliance on only a single category of scoring function.INTELLECTUAL MERIT: Accurately predicting the binding affinities of large sets of diverse protein-ligand complexes remains one of the most challenging problems in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. We seek to address this problem by developing efficient discrete optimization algorithms that facilitate: (1) the design of accurate, high-throughput single and multi SF methods with provable optimality for a given protein-ligand complex dataset; (2) determination of biochemically-relevant SFs through novel biochemical rule filters that suitably constrain the protein-ligand complex features selected; (3) prediction robustness through a novel multi-SF approach that reduces the variance in accuracy associated with relying on only a single SF; and (4) flexibility in accuracy-throughput tradeoff provided through a new integrated dynamic multi-SF approach. BROADER IMPACTS: This project will have a number of broader impacts: (1) public health benefits by facilitating efficient and cost-effective drug discovery, which in turn helps lower drug costs and improves affordability; (2) impact on other domains where scoring function type approaches are used; (3) interdisciplinary training of students in an important application area; (4) dissemination of research and software artifacts developed during the project; and (5) participation and training of underrepresented groups and K-12 outreach.

蛋白质-配体结合亲和力是许多重要过程的主要决定因素，如细胞信号、基因调控、新陈代谢和免疫，这些过程依赖于蛋白质与某些底物分子的结合。因此，它在药物设计中起着核心作用。由于与实验性药物发现相关的高昂成本和延误，学术界以及制药和生物技术公司依赖于使用计算分子对接进行虚拟筛选。通常，这涉及到将数万到数百万个候选配体对接到目标蛋白受体--S结合部位，并使用合适的评分函数来评估每个候选配体的结合亲和力，以确定排名靠前的候选配体是先导或有希望的蛋白抑制剂。由于评分函数(SF)被用来对药物先导进行评分、排序和识别，因此它预测候选配体与蛋白质S结合位点亲和力的保真度及其计算复杂性对虚拟筛选的准确性和吞吐量有重要影响。然而，目前最先进的评分函数存在许多不足，包括亲和力预测的准确性一般或吞吐量低、准确性不一致、提供的准确性与吞吐量之间的权衡不灵活以及仅依赖单一类别的评分函数。电子计算的优点：准确预测大量不同蛋白质-配体复合体的结合亲和力仍然是计算生物分子科学中最具挑战性的问题之一，在药物发现、化学生物学和结构生物学中的应用。我们试图通过开发高效的离散优化算法来解决这个问题，这些算法有助于：(1)针对给定的蛋白质-配体复杂数据集设计准确、高通量的单SF和多SF方法，并具有可证明的最优性；(2)通过适当限制所选蛋白质-配体复杂特征的新型生化规则过滤器来确定与生物化学相关的SF；(3)通过新的多SF方法减少与仅依赖单个SF相关的精度差异；以及(4)通过新的集成的动态多SF方法提供精度-吞吐量权衡的灵活性。更广泛的影响：该项目将产生一些更广泛的影响：(1)通过促进高效率和具有成本效益的药物发现对公共健康产生好处，这反过来又有助于降低药物成本和提高可负担性；(2)对使用计分功能类型办法的其他领域产生影响；(3)在一个重要的应用领域对学生进行跨学科培训；(4)传播在项目期间开发的研究成果和软件；(5)参与和培训代表性不足的群体和K-12外联活动。