TemBERTure: advancing protein thermostability prediction with deep learning and attention mechanisms

• 全文链接

TemBERTure:利用深度学习和注意力机制推进蛋白质热稳定性预测

关键词：

来源：

Bioinformatics Advances

全文链接：

//agri.nais.net.cn/topic/downloadFile/f7c73010-15cf-4e04-af41-9cae684ec0f6

来源地址：

https://academic.oup.com/bioinformaticsadvances/article/4/1/vbae103/7713394?login=true

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：

2024-07-13

摘要：

MotivationUnderstanding protein thermostability is essential for numerous biotechnological applications, but traditional experimental methods are time-consuming, expensive, and error-prone. Recently, deep learning (DL) techniques from natural language processing (NLP) was extended to the field of biology, since the primary sequence of proteins can be viewed as a string of amino acids that follow a physicochemical grammar.ResultsIn this study, we developed TemBERTure, a DL framework that predicts thermostability class and melting temperature from protein sequences. Our findings emphasize the importance of data diversity for training robust models, especially by including sequences from a wider range of organisms. Additionally, we suggest using attention scores from Deep Learning models to gain deeper insights into protein thermostability. Analyzing these scores in conjunction with the 3D protein structure can enhance understanding of the complex interactions among amino acid properties, their positioning, and the surrounding microenvironment. By addressing the limitations of current prediction methods and introducing new exploration avenues, this research paves the way for more accurate and informative protein thermostability predictions, ultimately accelerating advancements in protein engineering.