Developing new algorithms and concepts towards understanding protein folding, misfolding, and aggregation

开发新的算法和概念来理解蛋白质折叠、错误折叠和聚集

• 批准号: RGPIN-2019-03958
• 负责人: Srebnik, Simcha
• 金额: $ 2.04万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746782
• 关键词: Developing; new; algorithms; concepts; towards

Proteins are essential in performing tasks that are necessary for life, though their proper function can only be performed when folded into their native state. While the native state is stable under physiological conditions, environmental factors such as pH, salt concentration, or interaction with cell membranes may lead to spatial rearrangement of segments within the protein and its subsequent refolding and aggregation into nonnative and often undesirable structures. Protein folding, misfolding, and aggregation plays a critical role in a wide spectrum of fields and applications: Towards advancing protein engineering (a rising multibillion dollar industry); in diminishing protein aggregation that impacts biopharmaceutical development in every stage and is linked to high costs; and in furthering our understanding of protein misfolding disease that afflict over half a million Canadians. Our understanding of the fundamental processes that drive protein behavior has evolved significantly over the past 50 years. Nonetheless, surprisingly little advancements have been made in models that can predict the folding (and misfolding) process de novo, from a different initial conformation. State-of-the art models require substantial computational power and are currently limited to short protein chains, and hence cannot capture all the mechanisms involved. The goal of my lab is to develop new conceptual approaches to understand the mechanisms of protein folding, misfolding, and aggregation that will allow in silico studies of large proteins within a reasonable timeframe using a desktop computer. In the longer run, these methods will be used to understand and predict more complex biomolecular phenomena (such as docking, protein-DNA interactions, and interactions with nanoparticles for biosensor applications). In the proposed program I outline novel approaches for modeling protein behavior using various computational tools and methods. My preliminary work shows that computation time required for protein folding prediction can be significantly reduced if simulated in non-Cartesian coordinates. Combining this work with data mining tools, I recently revealed a relation between helical protein structures and their tendency to form malignant misfolded aggregates. Building on this research, the proposed program will form the basis of 3 PhD theses. The students will be trained in the development of molecular models, data mining, and machine learning in the longer run. Data mining algorithms will be used to recognize patterns within protein databases to reveal sequences that are liable to misfolding and aggregation under environmental stress. Machine learning algorithms will be used to identify reliable folding pathways. This research will advance basic understanding of protein behavior while fostering important computational skills in engineering students.

蛋白质在执行生命所必需的任务时是必不可少的，尽管它们的适当功能只有在折叠到自然状态时才能执行。虽然天然状态在生理条件下是稳定的，但环境因素，如pH、盐浓度或与细胞膜的相互作用，可能会导致蛋白质内部片段的空间重排，并随后将其重折叠和聚集成非天然的、通常是不受欢迎的结构。蛋白质折叠、错误折叠和聚集在广泛的领域和应用中发挥着关键作用：推动蛋白质工程(一个价值数十亿美元的新兴产业)；减少影响生物制药开发每个阶段并与高成本相关的蛋白质聚集；以及加深我们对困扰50多万加拿大人的蛋白质错误折叠疾病的了解。在过去的50年里，我们对驱动蛋白质行为的基本过程的理解发生了重大变化。然而，令人惊讶的是，能够从不同的初始构象预测折叠(和错误折叠)过程的模型取得的进展令人惊讶。最先进的模型需要大量的计算能力，目前仅限于短蛋白质链，因此无法捕捉到涉及的所有机制。我的实验室的目标是开发新的概念性方法来了解蛋白质折叠、错误折叠和聚集的机制，这将使使用台式计算机在合理的时间框架内对大型蛋白质进行计算机研究成为可能。从长远来看，这些方法将被用来理解和预测更复杂的生物分子现象(如对接、蛋白质-DNA相互作用，以及用于生物传感器应用的纳米颗粒的相互作用)。在拟议的计划中，我概述了使用各种计算工具和方法对蛋白质行为进行建模的新方法。我的初步工作表明，如果在非笛卡尔坐标下模拟，蛋白质折叠预测所需的计算时间可以显著减少。结合这项工作和数据挖掘工具，我最近揭示了螺旋蛋白质结构与它们形成恶性错误折叠聚集体的趋势之间的关系。在这项研究的基础上，拟议的计划将形成3篇博士论文的基础。从长远来看，这些学生将接受分子模型开发、数据挖掘和机器学习方面的培训。数据挖掘算法将用于识别蛋白质数据库中的模式，以揭示在环境压力下容易错误折叠和聚集的序列。机器学习算法将被用来识别可靠的折叠路径。这项研究将促进对蛋白质行为的基本理解，同时培养工科学生重要的计算技能。