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: 10164894
• 负责人: Brian Yuan Chen
• 金额: $ 10.04万
• 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/10164894
• 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。当高时 蛋白质的分辨结构是可用的，并且蛋白质的许多异构体已经连接到不同的。 根据结合偏好，原则上有可能对所有异构体的结构进行建模，并发现 导致绑定偏好发生变化的机制。不幸的是，这一发现过程取决于 人类研究分子结构的专业知识，并考虑到可能存在数百种异构体，人类 会不知所措地客观地研究许多类似的异构体。为了填补这一空白，该项目将(A1)减少 识别导致不同绑定偏好的结构机制的FELE软件，对 类似的结构机制，并用英语解释了这些机制。本项目(A2)的第二个目标是 要在显示各种经过充分检查的结合的蛋白质家族上大规模验证该软件 偏好，以及通过对实验合作者的盲目预测。 我们的方法包括创建模仿Structure所使用的可视化推理技术的软件 生物学家在研究分子结构时。这些技术不仅要对大多数主要的疾病负责， 结构生物学中的发现，但通过非计算研究也很容易理解- 艾尔斯。这一特性将使我们的软件能够立即集成到实验室的现有工作流中，而不是 专注于计算方法。此属性还与现有方法形成对比，现有方法通常输出 非专家难以确定的结构模型、势能、p值和结构分数。 理解或纳入他们的研究。通常，生物物理学专家需要这样解释输出 它们可以在实验室环境中运行。 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。对同行评议实验的验证已经证明了初步的 结果几乎在所有情况下都是正确的。我们的方法也被应用于对结合的盲目预测 蓖麻毒素与人类核糖体结合并损伤的机制。有了实验性的可卡因- ，我们证明了我们的方法正确地识别和预测了几种氨基酸的作用。 到目前为止还不知道核糖体在识别中的作用。使用我们的方法和严格的验证- 战略，这个项目将产生一个高度验证的、可用的软件包，将弥补一个关键的差距 在精密疗法和诊断学的发展中。