In-silico prediction of protein-peptide interactions.

蛋白质-多肽相互作用的电子计算机预测 自动对接方法被广泛用于获得对分子的机械理解 支撑细胞过程的相互作用。虽然这些工具对小分子很有效，但它们执行 多肽含量低，不能处理起重要作用的固有无序蛋白(IDPs) 在这些过程中。本项目的目标是开发一种高效实用的多肽对接。 该软件可用于设计治疗性多肽和深入了解IDPs结合的有序蛋白。 拟议的软件支持生物医学应用，范围从研究化学途径到 设计和优化治疗癌症和代谢紊乱等疾病的治疗分子。在.之下 在之前的奖项中，我们开发并发布了一种新的方法，用于对接多达20个完全柔性的多肽 标准氨基酸：AutoDock CrankPep(ADCP)。我们表明，它的性能超过了当前最先进的产品 对接方法。对于下一届奖项，我们建议：1)进一步开发ADCP，以支持IPDS与UP的对接 至70个氨基酸，并改进了含有修饰氨基酸和复合体的治疗多肽的载体 大循环；2)开发特定于多肽的评分函数，以提高对接成功率和方法 预测了多肽结合的自由能。这将通过利用以下方面的最新进展来实现 对接的统计潜力，以及应用机器学习技术；3)测试和验证 软件基于我们的数据集、社区基准，并通过我们与杰出生物学家的合作 致力于生物医学应用，从设计治疗血栓和流感的药物，到建模 与球状蛋白相互作用的IDP；以及4)记录该软件并在开放源码下发布 与我们定期编译和更新的数据集一起，定期获得许可。 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。此外，这一点 该项目将受益于与计算生物学领域的专家的各种合作，应用 数学和人工智能。该对接软件工具将通过应用最佳实践进行开发 并作为模块化的、可扩展的、基于组件的软件框架实现 用于多肽对接。该对接引擎将成为广泛使用的AutoDock软件套件的一部分。一种能力 对蛋白质和全柔性多肽或IDPs形成的复合体进行建模的需求很高，而且将大大 扩大可成功实现自动对接的基于多肽的治疗方法的范围 已申请。它还将支持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。

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.