Integrative deep learning algorithms for understanding protein sequence-structure-function relationships: representation, prediction, and discovery

用于理解蛋白质序列-结构-功能关系的集成深度学习算法：表示、预测和发现

• 批准号: 10712082
• 负责人: Yunan Luo
• 金额: $ 36.29万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-05 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10712082
• 关键词: 3-Dimensional; Acceleration; Algorithms; Amino Acid Sequence; Amino Acids; Area; Artificial Intelligence; Biological; Biological Sciences; Biology; Biomedical Engineering; Biotechnology; Computer software; Computing Methodologies; Data; Data Set; Directed Molecular Evolution; Disease; Goals; Health; Heterogeneity; Human; Intelligence; Knowledge; Learning; Measurement; Mechanics; Modeling; Noise; Ontology; Property; Protein Analysis; Protein Engineering; Proteins; Research; Shapes; Structure; Structure-Activity Relationship; Systems Integration; Uncertainty; Variant; analytical tool; artificial intelligence algorithm; artificial intelligence method; computational platform; computer framework; data resource; deep learning; deep learning algorithm; design; drug discovery; functional genomics; genome-wide; high dimensionality; human disease; improved; large scale data; machine learning method; next generation sequencing; novel; personalized diagnostics; personalized medicine; precision medicine; protein function; protein structure; protein structure prediction; statistical learning; structural genomics; synthetic biology; three dimensional structure; vaccine development

Abstract Understanding the sequence-structure-function relationship of proteins is of vital importance to protein biology, biomedicine, and bioengineering. Recent advances in biotechnology have been generating rich datasets to characterize proteins, such as next-generation sequencing data, three-dimensional (3D) structures, ontology annotations, and measurements of functional activities, yet how to computationally operationalize these datasets to fully unveil the structural or functional mechanisms of proteins remains a significant challenge. Existing computational methods often struggle with the size, high-dimensionality, heterogeneity, incompleteness, and intrinsic noise of those data, limiting our ability to study protein biology in a holistic and integrated system view. The goal of this research is to develop new artificial intelligence (AI) methods for effectively integrating and intelligently modeling heterogeneous protein-related datasets and to advance our understanding of the mechanical connections between proteins’ sequence, structure, and function. This project not only represents timely research that leverages the unprecedented opportunities offered by recent AI breakthroughs such as AlphaFold, but also goes beyond these efforts from protein structure prediction to systematic analyses of protein biology and unlocks new analytic frameworks that could not be realized previously. Specifically, we will first develop novel machine learning methods to learn statistical representations that are grounded on the sequence and structure of proteins and reflect their functional properties. The learned representations will allow us to characterize how the composition of amino acids and the 3D shape of protein structure determine the function of a protein. Second, we will develop unified, biology-guided deep learning frameworks to integrate domain knowledge, such as structural properties and evolutionary relationships, and study several key problems for characterizing protein functions, including genome-scale function annotation and variant effect prediction. These efforts will shift the classic sequence-first paradigm of previous studies to a new integrative paradigm and provide accurate, robust, and interpretable predictions of protein functions. Finally, we will develop a computational platform that combines data-efficient AI models, uncertainty-guided exploration algorithms, and deep learning- based generative models for AI-aided directed evolution and sequence-structure co-design of proteins, which will assist and accelerate the discovery and design of functional proteins. Overall, this proposal will study the sequence-structure-function relationship of proteins from an integrative perspective, provide new state-of-the-art AI algorithms with applications in fundamental problems for understanding protein function and human disease, and generate new actionable biological hypotheses for the discovery and design of novel functional proteins. The resulting software and data resources will be publicly available through open-access platforms.

抽象的 了解蛋白质的序列-结构-功能关系对于蛋白质生物学至关重要， 生物医学和生物工程的最新进展已经产生了丰富的数据集。 表征蛋白质，例如下一代测序数据、三维 (3D) 结构、本体论 注释和功能活动的测量，以及如何通过计算操作这些数据集 全面揭示蛋白质的结构或功能机制仍然是一个重大挑战。 计算方法经常与规模、高维性、异质性、不完整性和 这些数据的内在噪音，限制了我们从整体和集成系统角度研究蛋白质生物学的能力。 这项研究的目标是开发新的人工智能（AI）方法，以有效地整合和 智能地建模异质蛋白质相关数据集并增进我们对 该项目不仅代表了蛋白质序列、结构和功能之间的机械联系。 及时的研究利用了最近人工智能突破所提供的前所未有的机遇，例如 AlphaFold，还超越了从蛋白质结构预测到蛋白质系统分析的这些努力 生物学并解锁以前无法实现的新分析框架，具体来说，我们将首先。 开发新颖的机器学习方法来学习基于序列的统计表示 蛋白质和结构并反映它们的功能特性将使我们能够 描述氨基酸的组成和蛋白质结构的 3D 形状如何决定功能 其次，我们将开发统一的、生物学引导的深度学习框架来整合领域。 知识，例如结构特性和进化关系，并研究几个关键问题 表征蛋白质功能，包括基因组规模的功能注释和变异效应预测。 将先前研究的经典顺序优先范式转变为新的积分范式，并提供 最后，我们将开发一种计算方法。 该平台结合了数据高效的人工智能模型、不确定性引导的探索算法和深度学习—— 基于人工智能辅助的蛋白质定向进化和序列结构协同设计的生成模型， 总体而言，该提案将有助于并加速功能蛋白质的发现和设计。 从整合的角度研究蛋白质的序列-结构-功能关系，提供了新的最先进技术 人工智能算法在理解蛋白质功能和人类疾病的基本问题中的应用， 并为发现和设计新型功能蛋白产生新的可操作的生物学假设。 由此产生的软件和数据资源将通过开放获取平台公开提供。