Genomic language model predicts protein co-regulation and function : научное издание

Hwang Yunha; Cornman Andre L.; Kellogg Elizabeth H.; Ovchinnikov Sergey; Girguis Peter R.

Дополнительные ссылки: eLibrary

База данных: Каталог библиотеки СФУ

Библиографическое описание: Genomic language model predicts protein co-regulation and function : научное издание / Y. Y. Hwang [et al.]. - Текст : непосредственный // Nature Communications. - 2024. - Т. 15, № 1. - P. 2880. - ISSN 2041-1723, DOI 10.1038/s41467-024-46947-9.

Аннотация: Abstract Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we train a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLM’s unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region.

Год издания: 2024

Авторы: Hwang Yunha, Cornman Andre L., Kellogg Elizabeth H., Ovchinnikov Sergey, Girguis Peter R.

Источник: Nature Communications

Выпуск: Т. 15, № 1

Номера страниц: 2880

Количество экземпляров:

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