序列(生物学)
功能(生物学)
计算机科学
计算生物学
自然语言处理
人工智能
生物
进化生物学
遗传学
作者
Tymor Hamamsy,Meet Barot,James T. Morton,Martin Steinegger,Richard Bonneau,Kyunghyun Cho
出处
期刊:
[Cold Spring Harbor Laboratory]
日期:2023-11-26
被引量:10
标识
DOI:10.1101/2023.11.26.568742
摘要
Abstract The sequence-structure-function relationships that ultimately generate the diversity of extant observed proteins is complex, as proteins bridge the gap between multiple informational and physical scales involved in nearly all cellular processes. One limitation of existing protein annotation databases such as UniProt is that less than 1% of proteins have experimentally verified functions, and computational methods are needed to fill in the missing information. Here, we demonstrate that a multi-aspect framework based on protein language models can learn sequence-structure-function representations of amino acid sequences, and can provide the foundation for sensitive sequence-structure-function aware protein sequence search and annotation. Based on this model, we introduce a multi-aspect information retrieval system for proteins, Protein-Vec, covering sequence, structure, and function aspects, that enables computational protein annotation and function prediction at tree-of-life scales.
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