Cargando…
Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies
Protein language models, trained on millions of biologically observed sequences, generate feature-rich numerical representations of protein sequences. These representations, called sequence embeddings, can infer structure-functional properties, despite protein language models being trained on primar...
Autores principales: | Yeung, Wayland, Zhou, Zhongliang, Mathew, Liju, Gravel, Nathan, Taujale, Rahil, O’Boyle, Brady, Salcedo, Mariah, Venkat, Aarya, Lanzilotta, William, Li, Sheng, Kannan, Natarajan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851311/ https://www.ncbi.nlm.nih.gov/pubmed/36642409 http://dx.doi.org/10.1093/bib/bbac619 |
Ejemplares similares
-
Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings
por: Yeung, Wayland, et al.
Publicado: (2023) -
Modularity of the hydrophobic core and evolution of functional diversity in fold A glycosyltransferases
por: Venkat, Aarya, et al.
Publicado: (2022) -
Evolution of Functional Diversity in the Holozoan Tyrosine Kinome
por: Yeung, Wayland, et al.
Publicado: (2021) -
KinOrtho: a method for mapping human kinase orthologs across the tree of life and illuminating understudied kinases
por: Huang, Liang-Chin, et al.
Publicado: (2021) -
PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network
por: Wang, Xiaowen, et al.
Publicado: (2022)