Unexpected features of the dark proteome

By N. Perdigão, Julian Heinrich, C. Stolte, Kenneth Sabir, M. Buckley, Bruce Tabor, Bethany Signal, Brian S. Gloss, C. Hammang, Burkhard Rost and 2 more. 2015

197 citations recorded by the source corpus.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, background): “Secondly, evolutionary information is still missing for some proteins, e.g. for proteins with substantial intrinsically disordered regions [15, 37, 38], or the entire Dark Proteome [39] full of proteins that are less-well studied but important for function [40].”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing (2020, background): “Second, EI is not available for all proteins (intrinsically disordered proteins [26] or dark proteome [27]).”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Secondly, evolutionary information is missing for some proteins, e.g. for proteins with substantial intrinsically disordered regions [15, 33, 34], the entire Dark Proteome [35], and many less-well studied proteins.”
Single-sequence protein structure prediction using language models from deep learning (2021, background): “…using ML nonetheless remains a challenge: the requirement in AlphaFold2 for co-evolutionary information from MSAs makes it less performative with proteins that lack sequence homologs, currently estimated at ~20% of all metagenomic protein sequences 9 and ~11% of eukaryotic and viral proteins 10 .”
Limits and potential of combined folding and docking (2021, background): “However, reliable templates are not available for close to half the residues in the human proteome [1].”
Single-sequence protein structure prediction using language models and deep learning (2022, background): “…sequences using ML nonethe-less remains a challenge: the requirement in AF2 for co-evolutionary information from MSAs makes it less performant with proteins that lack sequence homologs, currently estimated at ~20% of all metagenomic protein sequences 9 and ~11% of eukaryotic and viral proteins 10 .”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning (2021, background): “Secondly, EI is not available for all proteins (intrinsically disordered proteins [26] or dark proteome [27]).”
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints (2018, background): “0% of the so-called dark Pfam families [25].”