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Data-Driven Approaches for Molecular Docking

数据驱动的分子对接方法

基本信息

批准号：
8511690
负责人：
AJAY N JAIN
金额：
$ 29.59万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2005
资助国家：
美国
起止时间：
2005-07-01 至 2015-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8511690
关键词：
Accounting Address Affinity Algorithms Binding Binding Sites Biological Biology Chemical Structure Chemistry Complex Coupling Covalent Interaction Data Data Set Development Docking Exercise Exhibits Grant HIV Protease Health Histamine Receptor Lead Learning Libraries Ligand Binding Ligands Machine Learning Methods Modeling Molecular Molecular Conformation Movement Patients Performance Pharmaceutical Preparations Pharmacologic Substance Positioning Attribute Process Protein Binding Protein Conformation Proteins Protocols documentation Quantitative Structure-Activity Relationship Relaxation Research Personnel Running Sampling Site Source Structural Protein Structure System Techniques Time Variant Work base drug discovery flexibility improved interest intermolecular interaction molecular dynamics novel novel strategies novel therapeutics predictive modeling protein structure screening simulation small molecule success virtual

项目摘要

DESCRIPTION (provided by applicant): Molecular docking approaches for drug discovery are used for predicting ligand binding modes, ligand binding affinities, and for computational screening to identify new specific ligands for biological targets of pharmaceutical relevance. Current approaches are characterized by high inter-target variability and sharp limitations in ability to make accurate predictions for new ligands that are structurally different from known ones. Approaches that model protein flexibility directly or approach simulation-level detail in complex molecular systems have shown some success. However, they are generally applicable only in low throughput and in cases where high-quality experimentally determined protein target structures are available. This is in part why "me-too" drugs dominate the pharmaceutical marketplace and development pipeline. Such drugs generally bring much less pharmacological novelty to patient treatment than structurally novel therapeutics. We propose an integrated set of methods for molecular docking that treats protein flexibility in a serious manner, is computationally efficient enough for wide use, and which offers the opportunity to effectively use docking in cases where few experimental structures exist for a biological target of interest. Our recent work has established an approach to treating protein flexibility in docking that addresses large protein movements by considering multiple experimental structures and small ligand-dependent movements by protein/ligand complex relaxation beginning from many putative ligand dockings. We have also established an approach for de novo protein pocket induction that constructs a binding site based solely on ligand binding data that is capable of making accurate predictions of binding geometry and binding affinity for structurally novel ligands. Our proposed work will combine these approaches. In cases where protein structural information is available, experimentally determined structures will undergo additional sampling, followed by refinement of the binding pockets based on ligand binding data in order to improve the predictions obtained from docking based upon our existing methods developed for de novo pocket construction. In addition to data-driven pocket refinement, the proposed effort requires improvement in our scoring functions for docking, taking into account vastly more data and explicit modeling of protein flexibility and of the unbound states of proteins and ligands. We will also put significant effort into algorithmic improvements that will result in typical run-times on common single-processor hardware of several minutes per ligand to yield predictions of binding geometry and affinity. We believe that a widely applicable, genuinely predictive, and computationally tractable modeling approach to docking will substantially improve drug discovery in practice. These methods will facilitate identification of novel lead compounds from directed lead optimization and computational screening exercises.

描述（由申请人提供）：用于药物发现的分子对接方法用于预测配体结合模式、配体结合亲和力，并用于计算筛选，以鉴定与药物相关的生物靶标的新特异性配体。目前的方法的特征在于高的靶间变异性和对结构上不同于已知配体的新配体进行准确预测的能力的急剧限制。直接模拟蛋白质柔性或在复杂分子系统中接近模拟水平细节的方法已经取得了一些成功。然而，它们通常仅适用于低通量和高质量实验确定的蛋白质靶结构可用的情况。这也是为什么“模仿”药物在制药市场和开发管道中占据主导地位的部分原因。这类药物给患者治疗带来的药理学新奇通常比结构新颖的治疗剂少得多。我们提出了一套完整的分子对接方法，以严肃的方式对待蛋白质的灵活性，计算效率足够广泛使用，并提供了机会，有效地使用对接的情况下，很少有实验结构存在的生物目标的兴趣。我们最近的工作已经建立了一种方法来处理蛋白质的灵活性对接，解决大蛋白质的运动，考虑多个实验结构和小配体依赖的运动蛋白质/配体复合物的松弛开始从许多假定的配体对接。我们还建立了一种从头蛋白口袋诱导的方法，该方法仅基于配体结合数据构建结合位点，能够准确预测结构新颖配体的结合几何形状和结合亲和力。我们提出的工作将联合收割机结合这些方法。在蛋白质结构信息可用的情况下，实验确定的结构将进行额外的采样，然后根据配体结合数据对结合口袋进行细化，以改善基于我们现有的从头口袋构建方法从对接中获得的预测。除了数据驱动的口袋细化，所提出的努力需要改进我们的评分功能对接，考虑到更多的数据和蛋白质的灵活性和蛋白质和配体的未结合状态的明确建模。我们还将投入大量的精力到算法的改进，这将导致在常见的单处理器硬件上每个配体几分钟的典型运行时间，以产生结合几何形状和亲和力的预测。我们相信，一个广泛适用的，真正的预测性，和计算上易于处理的对接建模方法将大大提高药物发现在实践中。这些方法将有助于识别新的先导化合物，直接铅优化和计算筛选练习。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Molecular shape and medicinal chemistry: a perspective.

DOI：
10.1021/jm900818s
发表时间：
2010-05-27
期刊：
Journal of medicinal chemistry
影响因子：
7.3
作者：
Nicholls A;McGaughey GB;Sheridan RP;Good AC;Warren G;Mathieu M;Muchmore SW;Brown SP;Grant JA;Haigh JA;Nevins N;Jain AN;Kelley B
通讯作者：
Kelley B

Iterative refinement of a binding pocket model: active computational steering of lead optimization.