五肽重复序列
肽
序列(生物学)
寡肽
计算生物学
化学
深度学习
组合化学
计算机科学
纳米技术
人工智能
生物
生物化学
材料科学
作者
Jiaqi Wang,Zihan Liu,Shuang Zhao,Tengyan Xu,Huaimin Wang,Stan Z. Li,Wenbin Li
标识
DOI:10.1002/advs.202301544
摘要
Abstract Self‐assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self‐assembling properties of peptides within the complete sequence space due to the enormous sequence quantities. Here, it is demonstrated that a transformer‐based deep learning model is effective in predicting the aggregation propensity (AP) of peptide systems, even for decapeptide and mixed‐pentapeptide systems with over 10 trillion sequence quantities. Based on the predicted AP values, not only the aggregation laws for designing self‐assembling peptides are derived, but the transferability relation among the APs of pentapeptides, decapeptides, and mixed pentapeptides is also revealed, leading to discoveries of self‐assembling peptides by concatenating or mixing, as consolidated by experiments. This deep learning approach enables speedy, accurate, and thorough search and design of self‐assembling peptides within the complete sequence space of oligopeptides, advancing peptide science by inspiring new biological and medical applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI