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: 2146027
• 负责人: Lukasz Kurgan
• 金额: $ 24.85万
• 依托单位: Virginia Commonwealth 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=2146027&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。这些相互作用的一个大的和功能上重要的部分是由蛋白质中的固有无序区（IDR）进行的。具有IDR的蛋白质参与许多人类疾病的发病机制，并且被认为是有吸引力的和有效的药物靶标。IDR在生理条件下缺乏稳定的结构，因此对分析和工作特别具有挑战性。该项目通过开发一整套先进的计算工具和数据库来预测和建模IDR-蛋白质和IDR-核酸相互作用的功能和结构，从而解决了这一挑战。相互作用的IDR，它们的结合伙伴，和建模的3D结构的相互作用的知识将指导实验设计和实验数据的解释建立假设。该项目将培养博士。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，为不同背景的学生提供服务。高中生将通过PI参与的外展活动和项目招募。总而言之，这个项目的重点是在普渡大学和VCU的跨学科计算生命科学教育和研究工作。三个互锁的计算方法将被开发用于研究IDRs的分子相互作用在1维（1D），2D和3D水平，显着推进传统的解决方案，仅限于1D/序列预测。相应的目标是：（1）使用尖端的多任务深度学习模型（1D水平）高精度预测IDR序列内的蛋白质和核苷酸结合区域;（2）整合识别这些结合区域的伴侣分子（蛋白质和核酸）（2D水平）;以及（3）通过IDR与伴侣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网络数据库和开放源码储存库提供给研究界。总的来说，这项工作通过开发现代计算工具和数据库来理解，预测和建模IDR与蛋白质和核苷酸相互作用的三级结构，显着推进了结构生物信息学领域。该奖项反映了NSF的法定使命，通过使用基金会的知识价值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

Resources for computational prediction of intrinsic disorder in proteins

• DOI: 10.1016/j.ymeth.2022.03.018
• 发表时间: 2022-05-27
• 期刊: METHODS
• 影响因子: 4.8
• 作者: Kurgan，Lukasz

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

• DOI: 10.1002/cpz1.802
• 发表时间: 2023-06-01
• 期刊: CURRENT PROTOCOLS
• 作者: Uversky，Vladimir N.;Kurgan，Lukasz
• 通讯作者: Kurgan，Lukasz