DIProT: A deep learning based interactive toolkit for efficient and effective Protein design

• 全文链接

DIProT:一个基于深度学习的交互式工具箱，用于高效的蛋白质设计

关键词：

来源：

Synthetic and Systems Biotechnology

全文链接：

//agri.nais.net.cn/topic/downloadFile/86fe07f6-fa53-40b4-8e2b-cb6dc318e677

来源地址：

https://doi.org/10.1016/j.synbio.2024.01.011

资源所属：

饲料用酶工程

类型：

学术文献

语种：

英语

原文发布日期：

2024-02-08

摘要：

The protein inverse folding problem, designing amino acid sequences that fold into desired protein structures, is a critical challenge in biological sciences. Despite numerous data-driven and knowledge-driven methods, there remains a need for a user-friendly toolkit that effectively integrates these approaches for in-silico protein design. In this paper, we present DIProT, an interactive protein design toolkit. DIProT leverages a non-autoregressive deep generative model to solve the inverse folding problem, combined with a protein structure prediction model. This integration allows users to incorporate prior knowledge into the design process, evaluate designs in silico, and form a virtual design loop with human feedback. Our inverse folding model demonstrates competitive performance in terms of effectiveness and efficiency on TS50 and CATH4.2 datasets, with promising sequence recovery and inference time. Case studies further illustrate how DIProT can facilitate user-guided protein design.