Multi-view self-supervised deep learning for biological sequences and beyond

项目摘要 深度学习在解决基本生物学问题方面的广度和深度一直是 演示了。基于DL的方法，例如用于3D蛋白质结构预测的AlphaFold2，已经成为 被生物界广泛接受。徐实验室一直走在开发新型数字图书馆的前沿 各种生物和医学问题的算法、软件和信息系统。在当前的 在项目期内，徐实验室在解决一些紧迫的挑战和需求方面取得了很好的进展 用于开发生物序列分析和预测以及其他生物信息学中的DL方法 有问题。这个R35项目已经发表了31篇论文，涵盖了从蛋白质序列到 基于对药物设计、分子动力学模拟和单细胞数据分析的预测。此外，它还 还向社区提供了十多个开源工具和三个主要的基于网络的资源。 新技术的快速发展和徐氏实验室在该领域积累的专业知识带来了新的 从分子生物学到塑造数字图书馆的机遇。目前在生物医学中广泛使用的有监督动态链接法 研究往往没有足够的数据和干净准确的标签来进行训练，并且可能没有良好的 概括性。旨在学习信息的新兴自我监督学习(SSL)方法 通过暴露不同数据透视之间的关系而无需人工注释的表示 成为一种新趋势。不同的数据视角被广泛地称为多视图。多视点安全套接层技术 允许我们为单模和多模数据生成联合或协调表示，具有更强的 通用性、更好的健壮性和更少的偏差。尽管SSL在其他领域取得了巨大的成功， 它只在生物学中得到了最低限度的探索。 该续订项目将开发一个多视图SSL框架，可以同时处理单视图和多视图 查看数据，能够执行单任务和多任务。它将解决应用中的关键挑战和瓶颈 用于生物学研究的SSL，例如选择有效的视图和数据增强、融合多模式数据或 来自不同来源的数据，并将生物约束集成到SSL模型中。我们将重点关注 设计了一个生物信息系统，增强了泛化和健壮性，并产生了结果 生物学上可解释的和可评估的信心。XU实验室将把该框架应用并细化到多个 主流生物学应用，包括通过探索各种数据进行抗CRISPR蛋白预测 基于互补的蛋白质序列增强、离子和小配体结合预测方法 蛋白质序列和结构的视图，以及不同条件下的单细胞数据分析。这个 框架还将在基于序列的研究和其他领域的广泛应用中进行测试，例如比对- 构建系统发育树和检测新蛋白质家族的自由方法以及进行 跨物种单细胞数据分析。

Project Abstract The breadth and depth of deep learning (DL) in solving fundamental biological problems have been demonstrated. DL-based approaches, such as AlphaFold2 for 3D protein structure prediction, have become widely accepted by the biology community. The Xu lab has been at the forefront of developing novel DL algorithms, software, and information systems for diverse biological and medical problems. During the current project period, the Xu lab has made excellent progress in addressing some of the urgent challenges and needs for developing DL methods in biological sequence analyses and predictions, as well as other bioinformatics problems. This R35 project has produced 31 papers covering research topics ranging from protein sequence- based predictions to drug design, molecular dynamics simulation, and single-cell data analysis. In addition, it also provided more than ten open-source tools and three major web-based resources to the community. The rapid development of new DL techniques and Xu lab’s accumulating expertise in this field bring new opportunities in shaping DL to molecular biology. The current widely used supervised DL methods in biomedical research often do not have sufficient data with clean and accurate labels for training and may not have good generalizability. The emerging self-supervised learning (SSL) approaches that aim to learn informative representations by exposing relationships between different data perspectives without human annotations are becoming a new trend. Different data perspectives are broadly called multiview. The multi-view SSL techniques allow us to generate joint or coordinated representations for single modal and multimodal data with stronger generalizability, better robustness, and less bias. Though SSL has demonstrated great successes in other fields, it has only been minimally explored in biology. This renewal project will develop a multi-view SSL framework that can handle both single-view and multi- view data and is capable of single and multiple tasks. It will tackle key challenges and bottlenecks in applying SSL for biological studies, such as selecting effective views and data augmentations, fusing multimodal data or data from heterogeneous sources, and integrating biological constraints into SSL models. We will focus on designing a biology-informed system, enhancing generalizability and robustness, and making the results biologically interpretable and confidence assessable. The Xu lab will apply and refine the framework to multiple mainstream biology applications, including anti-CRISPR protein prediction, by exploring various data augmentation methods for protein sequences, ion and small ligand binding prediction using complementary views of protein sequences and structures, and single-cell data analyses across different conditions. The framework will also be tested for broad applications in sequence-based studies and beyond, such as alignment- free methods for constructing phylogenetic trees and detecting novel protein families, as well as conducting cross-species single-cell data analysis.