Structure prediction and in silico screening of protein-peptide interactions

蛋白质-肽相互作用的结构预测和计算机筛选

• 批准号: 10605034
• 负责人: XIAOQIN ZOU
• 金额: $ 5.44万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10605034
• 关键词: Address; Antibiotic Resistance; Attention; Biochemistry; Bioinformatics; Biological Process; Cell physiology; Collaborations; Communities; Complement; Complex; Computational algorithm; Computer Models; Computer software; Disease; Freedom; Goals; Immune response; Investigation; Ligands; Medicine; Methods; Microbiology; Molecular Biology; NMR Spectroscopy; Peptide Library; Peptides; Phage Display; Proteins; Signal Transduction; Structure; Technology; Therapeutic; Therapeutic Intervention; Time; Transcriptional Regulation; X-Ray Crystallography; base; beta-Lactamase; combat; cost; deep learning; deep learning model; design; flexibility; in silico; inhibitor; innovation; novel; open source; peptide drug; peptide structure; physical model; protein data bank; protein structure prediction; screening; therapeutic development; yeast two hybrid system

Protein-peptide interactions are prevalent in many cellular processes, such as signal transduction, transcription regulation, and immune response. Peptide-based therapeutics have attracted much attention in recent years, and a significantly growing number of peptide-based medicines have been designed and approved for a variety of diseases. Therefore, studying protein-peptide interactions is of great significance for mechanistic investigation of many biological processes and for peptide therapeutic development. However, because of the difficulties and cost for determining such structures by X-ray crystallography and NMR spectroscopy, currently there are only a limited number of protein-peptide complex structures in the Protein Data Bank. Thus, the ability to predict protein- peptide complex structures will have a far-reaching impact on understanding important biological processes and on designing therapeutic interventions. However, structure prediction for protein-peptide complexes is challenging, particularly due to peptide flexibility. In this project, we will address this challenging issue by innovative integration of bioinformatics and physical modeling approaches. Specifically, we propose to achieve four goals: Goal #1: We will develop novel deep-learning models for protein-peptide structure prediction. Despite successful application of deep learning to protein structure prediction and protein-ligand interaction, deep learning has not been applied to protein-peptide structure prediction yet, due to the flexibility and the resulting large degrees of freedom in peptides. Goal #2: We will develop the first in silico screening method for the search of peptide-based inhibitors, and will construct novel peptide libraries for screening. Our in silico method will be an attractive complement to valuable experimental technologies such as phage display and yeast two-hybrid system for rapid peptide screening at much lower cost. Goal #3: We will convert our computational algorithms into a modular, extensible, open-source software package that can be disseminated to the computational modeling community at no cost. Goal #4. As a proof-of-concept application of our in silico screening method, we will screen for novel peptide leads by targeting β-lactamase to combat antibiotic resistance, in collaboration with my experimental collaborator whose expertise is in molecular biology, biochemistry and microbiology.

蛋白质-肽相互作用在许多细胞过程中普遍存在，如信号转导、转录、 调节和免疫反应。近年来，基于肽的治疗剂引起了广泛关注， 越来越多的肽类药物被设计和批准用于各种 疾病。因此，研究蛋白质-肽相互作用对于研究其作用机理具有重要意义 许多生物过程和肽治疗的发展。然而，由于困难和 通过X射线晶体学和NMR光谱学确定这种结构的成本，目前只有 蛋白质数据库中的蛋白质-肽复合物结构数量有限。因此，预测蛋白质的能力- 肽复合物结构将对理解重要的生物过程产生深远的影响， 设计治疗干预。然而，对蛋白质-肽复合物的结构预测是困难的。 挑战性，特别是由于肽的灵活性。在本项目中，我们将通过以下方式解决这一具有挑战性的问题： 生物信息学和物理建模方法的创新整合。具体而言，我们建议实现 四个目标： 目标1：我们将开发用于蛋白质-肽结构预测的新型深度学习模型。尽管成功 深度学习在蛋白质结构预测和蛋白质-配体相互作用方面的应用， 由于其灵活性和由此产生的很大程度的 自由肽。 目标#2：我们将开发第一个用于搜索基于肽的抑制剂的计算机筛选方法，并将 构建用于筛选的新肽文库。我们的计算机模拟方法将是对有价值的 噬菌体展示和酵母双杂交系统等实验技术用于快速肽筛选， 更低的成本。 目标3：我们将把我们的计算算法转换成一个模块化的、可扩展的、开源的软件 软件包，可以免费传播到计算建模社区。 目标4：作为我们的计算机筛选方法的概念验证应用，我们将筛选新的肽 通过靶向β-内酰胺酶来对抗抗生素耐药性，与我的实验合作者合作， 他的专长是分子生物学、生物化学和微生物学。