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

蛋白质-配体结合亲和力是许多重要过程的主要决定因素，如细胞信号传导、基因调控、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。由于高昂的成本和与实验药物发现相关的延迟，学术界、制药和生物技术公司依赖于使用计算分子对接的虚拟筛选。通常，这涉及到成千上万的候选配体与靶蛋白受体对接。S结合位点，并使用合适的评分函数来评估每个候选物的结合亲和力，以确定最佳候选物作为先导或有希望的蛋白质抑制剂。由于评分函数（SF）用于对药物先导物进行评分、排序和识别，它预测候选配体对蛋白质的亲和力的保真度如何？S结合位点及其计算复杂度对虚拟筛选的准确性和通量有重要影响。然而，目前最先进的评分函数有许多不足之处，包括亲和力预测的准确度一般或吞吐量低，准确性不一致，准确度和吞吐量之间的权衡缺乏灵活性，以及仅依赖单一类别的评分函数。知识价值：准确预测大量不同蛋白质配体复合物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都有应用。我们试图通过开发有效的离散优化算法来解决这个问题，这些算法有助于：(1)设计精确，高通量的单和多SF方法，并具有给定蛋白质配体复合物数据集的可证明的最优性；(2)通过新的生化规则过滤器来确定生物化学相关的SFs，这些规则过滤器适当地约束了所选择的蛋白质-配体复合物的特征；(3)通过一种新颖的多SF方法，减少了仅依赖单一SF的准确度方差，从而实现了预测的稳健性；(4)通过一种新的集成动态多sf方法提供了精度-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)通过促进高效和具有成本效益的药物发现，从而有助于降低药物成本和提高可负担性，从而造福公共卫生；(2)对使用评分函数型方法的其他领域的影响；(3)对学生进行重要应用领域的跨学科培养；(4)传播项目期间开发的研究成果和软件工件；(5)弱势群体的参与和培训以及K-12外展。

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.