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: 10394298
• 负责人: XIAOQIN ZOU
• 金额: $ 38.33万
• 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/10394298
• 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-肽相互作用普遍存在于许多细胞过程中，例如信号转导、转录 近年来，基于肽的治疗引起了广泛关注。 并且越来越多的基于肽的药物已被设计并批准用于各种 因此，研究蛋白质-肽相互作用对于疾病的机制研究具有重要意义。 许多生物过程和肽治疗开发。 通过 X 射线晶体学和核磁共振波谱确定此类结构的成本很高，目前只有 蛋白质数据库中蛋白质-肽复合物结构的数量有限，因此预测蛋白质的能力。 肽复杂结构将对理解重要的生物过程和 然而，蛋白质-肽复合物的结构预测是关键。 具有挑战性，特别是由于肽的灵活性，在这个项目中，我们将通过以下方式解决这个具有挑战性的问题。 具体来说，我们建议实现生物信息学和物理建模方法的创新整合。 四个目标： 目标#1：尽管取得了成功，我们仍将开发用于蛋白质肽结构预测的新型深度学习模型。 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，深度学习还没有 由于灵活性和由此产生的大程度的预测，它已被应用于蛋白质-肽结构预测。 肽的自由度。 目标#2：我们将开发第一个用于寻找基于肽的抑制剂的计算机筛选方法，并将 构建用于筛选的新型肽库。我们的计算机方法将成为有价值的补充。 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 成本低得多。 目标#3：我们将把我们的计算算法转换成模块化、可扩展的开源软件 可以免费传播到计算建模社区的软件包。 目标#4。作为我们的计算机筛选方法的概念验证应用，我们将筛选新型肽。 与我的实验合作者合作，通过靶向 β-内酰胺酶来对抗抗生素耐药性 他的专长是分子生物学、生物化学和微生物学。