Computational protein design with data‐driven approaches: Recent developments and perspectives
Haiyan Liu, Quan Chen
Abstract
Abstract A fundamental and challenging task of computational protein studies is to design proteins of desired structures and functions on demand. Data‐driven approaches to protein design have been gaining tremendous momentum, with recent developments concentrated on protein sequence representation and generation by using deep learning language models, structure‐based sequence design or inverse protein folding, and the de novo generation of new protein backbones. Currently, design methods have been assessed mainly by several useful computational metrics. However, these metrics are still highly insufficient for predicting the performance of design methods in wet experiments. Nevertheless, some methods have been verified experimentally, which showed that proteins of novel sequences and structures can be designed with data‐driven models learned from natural proteins. Despite the progress, an important current limitation is the lack of accurate data‐driven approaches to model or design protein dynamics. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Artificial Intelligence/Machine Learning