基于深度学习的蛋白质建模与设计

Protein modeling and design based on deep learning

摘要:

随着蛋白质序列及结构数据的大量累积，在获得了大量描述性信息之后如何有效利用海量数据，从已有数据中高效提取信息并且应用到下游任务当中就成为了研究者亟待解决的问题。蛋白质的设计可使新蛋白的研发不再受限于实验条件，这对药物靶点预测、新药研发和材料设计等领域具有重要意义。深度学习作为一种高效的数据特征提取方法，可以通过它对蛋白质数据进行建模，进而加入先验信息对蛋白质进行设计。故此基于深度学习的蛋白质设计就成为一个具有广阔前景的研究领域。文中主要阐述基于深度学习的蛋白质序列与结构数据的建模和设计方法。详述该方法的策略、原理、适用范围、应用实例。讨论了深度学习方法在本领域的应用前景及局限性，以期为相关研究提供参考。

Abstract:

The accumulation of protein sequence and structure data allows researchers to obtain large amount of descriptive information, simultaneously it poses an urgent need for researchers to extract information from existing data efficiently and apply it to downstream tasks. Protein design enables the development of novel proteins that are no longer restricted by experimental conditions, which is of great significance for drug target prediction, drug discovery, and material design. As an efficient method for data feature extraction, deep learning can be used to model protein data, and further add a priori information to design novel proteins. Therefore, protein design based on deep learning has become a promising approach despite of many challenges. This review summarizes the deep learning-based modeling and design methods of protein sequence and structure data, highlighting the strategies, principle, scope of application and case studies, with the aim to provide a valuable reference for relevant researchers.