Structure based Prediction of the interactome

小分子与蛋白质的相互作用不仅在整个细胞过程中无处不在， 这对药物设计和疾病治疗也至关重要。就像蛋白质一样， 蛋白质-RNA相互作用，高通量（HTP）实验方法已经导致产生 大量的蛋白质-小分子和相关数据。然而，除了规模庞大， 这些数据的异质性程度，以及源自 在工业内部，对基础研究中共享和充分利用这些数据提出了重大挑战， 治疗发展该提案旨在开发新的数学方法，不仅可以解决 解释数据本身，以及研究人员工作的协作和生成过程： 新的加密工具可以实现行业之间前所未有的安全共享和协作 和公众，结构特征的深度学习可以减少研究人员对先验知识的依赖。 药物靶点相互作用（DTI）的重要预测因素的假设。 在上一个资助期内，我们成功开发了基于结构的预测和HTP方法， 蛋白质-蛋白质和蛋白质-RNA相互作用的数据分析，揭示新的生物学（例如，为 神经变性疾病）。在这次更新中，我们的目标是：1）为多方开发可扩展的方法 计算和差异隐私，以实现大型专有药物-靶标相互作用的安全共享 在工业和公共研究人员之间建立数据库; 2）开发新的综合机器学习方法 用于基于相互作用组、分子结构和化学基因组学数据鉴定药物-靶标相互作用（在 3）与工业界、学术界和 科学界推动在实践中使用和采用这些计算工具和技术 生物医学研究人员。 这些目标的成功完成将为公共和私人研究社区提供可扩展的 获得安全共享专利药物筛选数据的技术以及灵活、准确的工具 用于预测药物-靶标相互作用。所有开发的软件都将通过公开访问 开放源码软件许可证下的基于网络的门户。与研究伙伴的合作将验证 这些工具与人类健康和疾病的相关性，而传播目标将确保研究 社区方便和持续地获得这些创新。

The interactions of small molecules with proteins is not only omnipresent throughout cellular processes, but also of fundamental importance to drug design and disease treatment. Much like with protein-protein and protein-RNA interactions, high-throughput (HTP) experimental methods have led to the generation of enormous volumes of protein-small molecule and related data. However, in addition to sheer scale, high degrees of heterogeneity in these data, combined with proprietary ownership concerns when originating from within industry, present significant challenges to the sharing and full use of this data in basic research and therapeutic development. This proposal aims to develop new mathematical methods that can address not only interpreting the data itself, but also the collaborative and generative process through which researchers work: new cryptographic tools can enable unprecedented forms of secure sharing and collaboration between industry and the public, and deep learning of structural features can reduce the dependence of researchers on prior assumptions as to important predictors of drug-target interactions (DTI). In our previous granting period, we successfully developed methods for structure-based prediction and HTP data analysis of protein-protein and protein-RNA interactions, uncovering novel biology (e.g., for neurodegenerative diseases). In this renewal, we aim to: 1) develop scalable methods for multi-party computation and differential privacy to enable the secure sharing of large proprietary drug-target interaction databases among industry and public researchers; 2) develop novel integrative machine learning approaches for identifying drug-target interactions based on interactome, molecular structure and chemogenomic data (in collaboration with co-I Jian Peng); and 3) establish innovative collaborations with industry, academia and the scientific community to drive use and adoption of these computational tools and technologies among practicing biomedical researchers. Successful completion of these aims will provide both public and private research communities with scalable access to technologies for secure sharing of proprietary drug screening data as well as flexible, accurate tools for predicting drug-target interactions. All developed software will be made available via publicly accessible web-based portals under open source software licenses. Collaborations with research partners will validate the relevance of these tools to human health and disease, while the dissemination aim will ensure research communities convenient and ongoing access to these innovations.