In-silico prediction of protein-peptide interactions.

蛋白质-肽相互作用的计算机预测。

• 批准号: 10259801
• 负责人: MICHEL F. SANNER
• 金额: $ 39.94万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10259801
• 关键词: Amino Acids; Area; Artificial Intelligence; Automobile Driving; Award; Benchmarking; Binding; Binding Proteins; Biological; Biological Availability; Biological Process; Biomedical Research; Cell physiology; Cells; Chemicals; Collaborations; Communities; Complex; Computational Biology; Computer software; Cyclic Peptides; Data Set; Development; Disease; Docking; Documentation; Drug Design; Educational workshop; Feedback; Free Energy; Funding; Goals; Half-Life; Influenza; Insulin; Libraries; Licensing; Ligands; Machine Learning; Malignant Neoplasms; Mathematics; Mediating; Metabolic Diseases; Methods; Modeling; Modernization; Mutate; Pathway interactions; Peptides; Performance; Peripheral; Permeability; Pharmaceutical Preparations; Play; Process; Production; Property; Proteins; Renaissance; Research; Role; Scientist; Signal Pathway; Software Engineering; Software Framework; Software Tools; Specificity; Structure; Study models; Techniques; Testing; Therapeutic; Thrombosis; Toxic effect; Training; Update; Validation; Work; base; combinatorial; computerized tools; computing resources; design; flexibility; globular protein; graphical user interface; improved; improved functioning; in silico; insight; interest; interoperability; novel; open source; peptide drug; predictive tools; programs; protein protein interaction; receptor; screening; small molecule; success; symposium; therapeutic target; tool; translational study; virtual screening

IN-SILICO PREDICTION OF PROTEIN-PEPTIDE INTERACTIONS Automated docking methods are used extensively for gaining a mechanistic understanding of the molecular interactions underpinning cellular processes. While these tools work well for small molecules they perform poorly for peptides and cannot handle Intrinsically Disordered Proteins (IDPs) which play very important roles in these processes. The goal of this project is the development of an efficient and practical peptide docking software, useful for designing therapeutic peptides and gaining insight into IDPs binding ordered proteins. The proposed software supports biomedical applications ranging from investigating chemical pathways to designing and optimizing therapeutic molecules for diseases such as cancer and metabolic disorders. Under the previous award we developed and released a new method for docking fully-flexible peptides with up to 20 standard amino acids: AutoDock CrankPep (ADCP). We showed that it outperforms current state-of-the-art docking methods. For the next award, we propose to: 1) further develop ADCP to support docking IPDs with up to 70 amino acids and improve support for therapeutic peptides containing modified amino acids and complex macrocycles; 2) develop peptide-specific scoring functions to increase docking success rates and methods for predicting the free energy of binding of peptides. This will be done by exploiting the latest advances in statistical potentials for docking, as well as applying machine-learning techniques; 3) test and validate the software on our datasets, community benchmarks, and through our collaborations with outstanding biologists working on biomedical applications spanning from designing drugs for thrombosis and influenza, to modeling IDPs interacting with globular proteins; and 4) document the software and release it under an open source license on a regular basis along with datasets we compile and update on regularly. The proposed research will occur in the context of collaborations with experimental biologists working on highly relevant biomedical projects and providing experimental feedback and validation. In addition, this project will benefit from various collaborations with experts in the fields of computational biology, applied mathematics and artificial intelligence. This docking software tool will be developed by applying best practices in software engineering and be implemented as a modular, extensible, component-based software framework for peptide docking. This docking engine will be part of the widely used AutoDock software suite. The ability to model complexes formed by proteins and fully-flexible peptides or IDPs is in high demand and will greatly extend the range of peptide-based therapeutic approaches for which automated docking can be successfully applied. It will also support gaining insights into interactions of IDPs with proteins. As such, it will impact the research of many medicinal chemists and biologist and extend the use of computational tools to a wider community of scientists, thereby supporting the advancement of biomedical research.

蛋白质-肽相互作用的计算机预测 自动对接方法被广泛用于获得分子的机械理解， 相互作用支撑细胞过程。虽然这些工具对小分子很有效， 对于肽而言，其作用较差，并且不能处理起着非常重要作用的内分泌紊乱蛋白（IDP 在这些过程中。本项目的目标是开发一种高效实用的肽对接方法 软件，用于设计治疗肽和深入了解IDP结合有序蛋白。 拟议的软件支持生物医学应用，从调查化学途径， 设计和优化用于癌症和代谢紊乱等疾病的治疗分子。下 我们开发并发布了一种新的方法，用于对接全柔性肽， 标准氨基酸：AutoDock CrankPep（ADCP）。我们证明了它的性能优于目前最先进的 对接方法对于下一个奖项，我们建议：1）进一步开发ADCP，以支持对接IPD与up 并改善对含有修饰的氨基酸和复合物的治疗肽的支持 大环化合物; 2）开发肽特异性评分功能以增加对接成功率和用于 预测肽的结合自由能。这将通过利用最新的进展， 对接的统计潜力，以及应用机器学习技术; 3）测试和验证 软件对我们的数据集，社区基准，并通过我们的合作与杰出的生物学家 致力于生物医学应用，从设计血栓形成和流感药物，到建模， IDP与球状蛋白质相互作用;以及4）记录软件并在开放源代码下发布 沿着我们定期编译和更新的数据集。 这项拟议的研究将在与实验生物学家合作的背景下进行， 高度相关的生物医学项目，并提供实验反馈和验证。另外这款 该项目将受益于与计算生物学、应用生物学和生物学领域专家的各种合作。 数学和人工智能。这一对接软件工具将采用最佳做法加以开发 并作为模块化、可扩展、基于组件的软件框架来实现 用于肽对接。该对接引擎将成为广泛使用的AutoDock软件套件的一部分。的能力 对由蛋白质和完全柔性肽或IDP形成的复合物进行建模的需求很高， 扩展了基于肽的治疗方法的范围， 应用。它还将有助于深入了解国内流离失所者与蛋白质的相互作用。因此，它将影响 许多药物化学家和生物学家的研究，并将计算工具的使用扩展到更广泛的 科学家社区，从而支持生物医学研究的进步。