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.

蛋白质结合亲和力是许多重要过程的主要决定因素，例如细胞信号传导，基因调节，代谢和免疫，依赖于与某些底物分子结合的蛋白质。因此，它在药物设计中具有核心作用。由于与实验药物发现有关的成本和延误，学术界和制药和生物技术公司依赖使用计算分子对接的虚拟筛查。通常，这涉及将数万到数百万至数百万的配体候选物停靠到靶蛋白受体的结合位点中，并使用合适的评分功能来评估每个候选者的结合亲和力，以识别顶级候选者作为铅或有希望的蛋白质抑制剂。由于评分函数（SF）用于评分，排名和识别药物铅，因此它可以预测配体候选蛋白质结合位点的亲和力及其计算复杂性具有显着的依赖，这对虚拟筛选的准确性和吞吐量具有重要意义。但是，当前的最新评分功能具有许多缺陷，包括相关性预测或低吞吐量或低吞吐量的中等准确性，准确性不一致，提供的准确性折衷权的不灵活性，以及​​仅依赖于得分功能的单个类别的能力。生物分子科学，在药物发现，化学生物学和结构生物学中应用。我们寻求通过开发有助于促进的有效离散优化算法来解决此问题：（1）精确，高通量单和多SF方法的设计具有可在给定蛋白质配体复杂数据集的可证明最佳性的设计； （2）通过新型的生化规则过滤器确定生化与生化相关的SF，从而适当限制所选择的蛋白质配体复合物的特征； （3）通过一种新型的多SF方法预测鲁棒性，该方法降低了仅依靠单个SF相关的准确性方差； （4）通过新的集成动态多SF方法提供的精确度量交易的灵活性。更广泛的影响：该项目将产生更广泛的影响：（1）通过促进有效且具有成本效益的药物发现，公共卫生益处，这反过来又有助于降低药品成本并提高负担能力； （2）对使用评分函数类型方法的其他域的影响； （3）在重要的应用领域对学生的跨学科培训； （4）在项目期间开发的研究和软件工件的传播； （5）参与和培训代表性不足的群体和K-12外展。