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III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction

III：媒介：协作研究：蛋白质功能预测的多级计算方法

基本信息

批准号：
2210356
负责人：
Dukka KC
金额：
$ 15万
依托单位：
Michigan Technological University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210356&HistoricalAwards=false
关键词：
III Medium Collaborative Research Multi

项目摘要

Proteins are the workhorse molecules of life which participate in nearly every activity of cellular processes, including signal transduction, enzyme catalysis, structural support, bodily movement, and defense against pathogens. Interpretation of specific functional roles that each protein molecule plays in cell is thus critical for us to understand the fundamental principles of the biological processes and to design new drug treatments to regulate the processes for improving human health. The task is however highly non-trivial in modern molecular biology studies. The most accurate method to interpret protein biological functions is through structural biology and biochemistry experiments. But the cost of the experimental studies is high, and the process is too slow for large-scale application due to the involvement of manual skill and data processing. As a result, the majority of proteins in human and other important species remain unknown despite decades of efforts. The lack of genome-wide protein function information has significantly impeded the progress of system biology studies aiming at a comprehensive understanding of the life process. In this project, the investigators plan to develop advanced computational methods for automatic and yet reliable protein function annotations. The developed methods and databases will be freely released to the scientific community, which can be used for large-scale and genome-wide protein function annotation studies. The project will also provide opportunities to promote participations of underrepresented groups, including women and African Americans, in computational biology education and method developments.Built on the assumption that similar sequences have similar function, a routine approach to computational protein function annotations is comparative modeling, which deduces functions of target proteins from known homologous proteins. However, the accuracy and coverage of the approach are limited due to the diversity of gene evolution. Significant progress has been recently achieved in protein 3D structure prediction and the state-of-the-art algorithms can generate high-quality structures for distant-homology proteins with an unprecedented capacity. This project seeks to explore various new ideas to enhance the accuracy of distant-homology protein function annotations by using 3D models from the cutting-edge protein structure predictions, with a focus on ligand-protein binding interactions, gene ontology and post-translational modifications. Meanwhile, thermal motion and intrinsic disordering of protein structures are integrated in the pipelines for better function annotations. While the proposed approaches do not expect to address all the fundamental issues, like the first-principle methods, as of how and why proteins fold and function, the success of the studies should help establish a practical knowledge-based relation of structure and function that can be used for genome-scale applications with models useful for guiding new experimental design, and thus significantly enhance the impact of protein structure modeling on biological studies.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.

蛋白质是生命的主要分子，几乎参与细胞过程的每一项活动，包括信号转导、酶催化、结构支持、身体运动和对病原体的防御。因此，理解每个蛋白质分子在细胞中发挥的特定功能对于我们理解生物过程的基本原理和设计新的药物治疗方法以调节改善人类健康的过程是至关重要的。然而，在现代分子生物学研究中，这项任务非常重要。解释蛋白质生物功能最准确的方法是通过结构生物学和生物化学实验。但由于人工技术和数据处理的影响，实验研究的成本较高，而且过程太慢，不适合大规模应用。因此，尽管经过几十年的努力，人类和其他重要物种中的大多数蛋白质仍然未知。全基因组蛋白质功能信息的缺乏严重阻碍了旨在全面了解生命过程的系统生物学研究的进展。在这个项目中，研究人员计划开发先进的计算方法，用于自动而又可靠的蛋白质功能注释。开发的方法和数据库将免费向科学界发布，可用于大规模和全基因组的蛋白质功能注释研究。该项目还将提供机会，促进包括女性和非裔美国人在内的未被充分代表的群体参与计算生物学教育和方法开发。基于相似序列具有相似功能的假设，计算蛋白质功能注释的常规方法是比较建模，即从已知的同源蛋白质中推导出目标蛋白质的功能。然而，由于基因进化的多样性，该方法的准确性和覆盖率受到限制。最近在蛋白质三维结构预测方面取得了重大进展，最先进的算法可以以前所未有的能力为远距离同源蛋白质生成高质量的结构。本项目旨在探索各种新的思路，通过使用来自前沿蛋白质结构预测的3D模型来提高远距离同源蛋白质功能注释的准确性，重点关注配体-蛋白质结合作用、基因本体论和翻译后修饰。同时，在管道中集成了蛋白质结构的热运动和内在无序，以获得更好的功能注释。虽然拟议的方法不会解决所有的基本问题，如第一原理方法，如蛋白质如何折叠和为什么折叠和功能，研究的成功应该有助于建立一个实用的基于知识的结构和功能的关系，可以用于基因组规模的应用，模型有助于指导新的实验设计，从而显著增强蛋白质结构建模对生物学研究的影响。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。