Collaborative Research: Identification and Structural Modeling of Intrinsically Disordered Protein-Protein and Protein-Nucleic Acids Interactions

合作研究：本质无序的蛋白质-蛋白质和蛋白质-核酸相互作用的识别和结构建模

• 批准号: 2146026
• 负责人: Daisuke Kihara
• 金额: $ 24.88万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146026&HistoricalAwards=false
• 关键词: Collaborative; Research; Identification; Structural; Modeling

Many key cellular processes rely on the protein-protein and protein-nucleic acid interactions. A large and functionally important portion of these interactions is carried out by intrinsically disordered regions (IDRs) in proteins. Proteins with IDRs are involved in the pathogenesis of numerous human diseases and are considered as attractive and potent drug targets. IDRs lack a stable structure under physiological conditions and as such are particularly challenging to analyze and work with. This project addresses this challenge by developing a full suite of advanced computational tools and databases for predicting and modeling functions and structures IDR-protein and IDR-nucleic acids interactions. The knowledge of the interacting IDRs, their binding partners, and modeled 3D structures of interactions will guide building hypotheses for experiment design and interpretation of experimental data. The project will train Ph.D. students of different backgrounds through interdisciplinary coursework and mentoring at Purdue University and Virginia Commonwealth University (VCU). High school students will be recruited through outreach activities and programs that the PIs are involved in. Altogether, this project focuses on the interdisciplinary computational life science education and research efforts at Purdue and VCU.Three interlocked computational methods will be developed for studying molecular interactions of IDRs at the 1 dimensional (1D), 2D, and 3D levels, significantly advancing over the conventional solutions that are limited to 1D/sequence predictions. The corresponding aims are: (1) high-accuracy prediction of protein and nucleotide binding regions within IDR sequences using cutting-edge multi-task deep learning models (1D level); (2) integrative identification of the partner molecules (proteins and nucleic acids) for these binding regions (2D level); and (3) structure modeling by innovative docking between IDRs and the partner proteins and nucleotides (3D level). The developed tools and results will be provided to the research community through a web-based database and open source repositories. Overall, this work significantly advances structural bioinformatics field by developing modern computational tools and a database for understanding, predicting, and modeling tertiary structures of interactions of IDRs with proteins and nucleotides. The resulting new deep learning technologies will be transferrable to other bioinformatics areas that rely on the prediction and analysis from protein sequences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

许多关键的细胞过程依赖于蛋白质-蛋白质和蛋白质-核酸的相互作用。这些相互作用中有很大一部分是由蛋白质中的固有无序区域(IDR)进行的，并且具有重要的功能。具有IDRs的蛋白质与许多人类疾病的发病机制有关，被认为是有吸引力的有效药物靶点。IDR在生理条件下缺乏稳定的结构，因此分析和处理特别具有挑战性。该项目通过开发一整套先进的计算工具和数据库来应对这一挑战，以预测和模拟IDR-蛋白质和IDR-核酸相互作用的功能和结构。相互作用的IDR、它们的结合伙伴和相互作用的建模3D结构的知识将指导建立实验设计和实验数据解释的假设。该项目将通过普渡大学和弗吉尼亚联邦大学(VCU)的跨学科课程和指导来培养不同背景的博士生。高中生将通过PI参与的外展活动和计划招募。总而言之，这个项目集中在普渡大学和VCU的跨学科计算生命科学教育和研究工作，将开发三种互锁计算方法来研究一维(1D)、二维和三维水平上的IDR的分子相互作用，显著优于仅限于一维/序列预测的传统解决方案。相应的目标是：(1)使用尖端多任务深度学习模型(1D水平)高精度预测IDR序列中的蛋白质和核苷酸结合区域；(2)整合识别这些结合区域的伙伴分子(蛋白质和核酸)(2D水平)；以及(3)通过IDR与伙伴蛋白质和核苷酸之间的创新对接进行结构建模(3D水平)。开发的工具和成果将通过基于网络的数据库和开放源码储存库提供给研究界。总体而言，这项工作通过开发现代计算工具和数据库来理解、预测和模拟IDR与蛋白质和核苷酸相互作用的三级结构，从而显著推动了结构生物信息学领域的发展。由此产生的新的深度学习技术将被转移到其他依赖于蛋白质序列预测和分析的生物信息学领域。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints

• DOI: 10.1016/j.jmb.2024.168486
• 发表时间: 2024-02-16
• 期刊: JOURNAL OF MOLECULAR BIOLOGY
• 影响因子: 5.6
• 作者: Christoffer，Charles;Harini，Kannan;Kihara，Daisuke
• 通讯作者: Kihara，Daisuke