Algorithmic identification of binding specificity mechanisms in proteins

蛋白质结合特异性机制的算法识别

• 批准号: 10251944
• 负责人: Brian Yuan Chen
• 金额: $ 25.78万
• 依托单位: LEHIGH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10251944
• 关键词: Address; Algorithms; Amino Acids; Artificial Intelligence; Benchmarking; Binding; Binding Proteins; Binding Sites; Biochemical; Biophysical Process; Biophysics; Charge; Clinical; Collaborations; Complex; Computer software; Computing Methodologies; Development; Diagnosis; Disease; Docking; Drug Targeting; Electrostatics; Elements; English Language; Environment; Evaluation; Exhibits; Feedback; HIV Protease; Hot Spot; Human; Hydrogen Bonding; Hydrophobicity; Immune; Individual; Influentials; Laboratories; Letters; Ligand Binding; Ligands; Link; Literature; Major Histocompatibility Complex; Maps; Measures; Mechanics; Methodology; Methods; Molecular; Molecular Conformation; Molecular Structure; Mutation; Nicotinic Receptors; Outcome; Output; Patients; Peer Review; Peptide Hydrolases; Population; Potential Energy; Precision therapeutics; Process; Property; Protein Family; Protein Isoforms; Proteins; Research; Resolution; Ribosomes; Ricin; Role; Serine Protease; Shapes; Site; Specificity; Structural Biologist; Structural Models; Structure; Techniques; Testing; Text; Toxin; Tweens; Universities; Validation; Variant; Visual; base; blind; human-in-the-loop; hydropathy; inhibitor/antagonist; insight; mutant; novel; personalized diagnostics; precision medicine; preference; protein structure; prototype; receptor; simulation; software development; structural biology; therapy development; tool; tumor

Project Summary Variations in protein binding preferences are a critical barrier to the precision treatment of disease. When high resolution structures of a protein are available, and many isoforms of the protein have been connected to dif- fering binding preferences, it is possible in principle to model the structures of all isoforms and discover the mechanisms that cause variations in binding preferences. Unfortunately, this discovery process depends on human expertise for examining molecular structure, and given that hundreds of isoforms may exist, a human would be overwhelmed to objectively examine many similar isoforms. To fill this gap, this project will (A1) de- velop software that identifies structural mechanisms that cause differential binding preferences, categorizes similar structural mechanisms, and explains the mechanisms in English. The second aim of this project (A2) is to validate the software at a large scale on families of proteins that exhibit a variety of well-examined binding preferences, and through blind predictions with experimental collaborators. Our approach involves creating software that mimics the visual reasoning techniques employed by structural biologists when examining molecular structures. Not only are these techniques responsible for most major dis- coveries in structural biology, but they are also straightforward to understand by non-computational research- ers. This property will enable our software to immediately integrate into existing workflows at labs that do not focus on computational methods. This property also contrasts from existing methods, which generally output structural models, potential energies, p-values and structural scores which are difficult for non-experts to un- derstand or incorporate into their research. Often, an expert in biophysics is required to interpret the outputs so that they can be operationalized in laboratory environments. In preliminary results, our methods have already identified molecular mechanisms that govern specificity in several families of proteins. Verification against peer-reviewed experimentation has proven the preliminary results correct in almost all cases. Our methods have also been applied to make a blind prediction of binding mechanisms in the ricin toxin, which binds to and damages the human ribosome. With experimental collabo- rators, we showed that our methods correctly identified and predicted the roles of several amino acids with a hitherto unknown role in recognizing the ribosome. Using our methodological approach and our rigorous valida- tion strategy, this project will produce a highly validated, usable software package that will bridge a critical gap in the development of precision therapies and diagnostics.

项目摘要 蛋白质结合偏好的变化是疾病精确治疗的关键障碍。高 可以使用蛋白质的分辨率结构，并且蛋白质的许多同工型已连接到不同 Fering结合偏好，原则上有可能对所有同工型的结构进行建模并发现 导致结合偏好变化的机制。不幸的是，这个发现过程取决于 人类检查分子结构的专业知识，并鉴于可能存在数百种同工型，人类 将不知所措以客观地检查许多类似的同工型。为了填补这一空白，该项目将（A1） 识别导致不同结合偏好的结构机制的Velop软件，对 类似的结构机制，并解释了英语的机制。该项目的第二个目标（A2）是 大规模验证该软件的蛋白质家族，这些蛋白质表现出各种审查的结合 偏好，以及通过实验合作者的盲目预测。 我们的方法涉及创建模仿结构性视觉推理技术的软件 研究分子结构时的生物学家。这些技术不仅造成了大多数主要的疾病 结构生物学的覆盖物，但非计算研究也很直接理解 - ers。该属性将使我们的软件能够立即集成到没有实验室的现有工作流中 专注于计算方法。该属性也与现有方法形成鲜明对比，这些方法通常会输出 结构模型，势能，P值和结构分数，非专家难以实现 构成或纳入他们的研究。通常，需要生物物理学专家来解释输出，因此 它们可以在实验室环境中进行操作。 在初步结果中，我们的方法已经确定了控制特异性的分子机制 几个蛋白质家族。针对同行评审实验的验证已证明了初步 在几乎所有情况下，结果都正确。我们的方法也已应用于盲目预测 ricin毒素中的机制，与人核糖体结合并损害人类核糖体。与实验合作 Rators，我们表明我们的方法正确识别并预测了几种氨基酸的作用 迄今为止在识别核糖体中的作用未知。使用我们的方法论方法和严格的验证 该项目将产生一个高度验证的可用软件包，将弥合关键差距 在开发精确疗法和诊断方面。