Dark Proteins Important for Cellular Function

By Andrea Schafferhans, S. O’Donoghue, Michael Heinzinger, Burkhard Rost. 2018

10 citations recorded by the source corpus.

Despite substantial and successful projects for structural genomics, many proteins remain for which neither experimental structures nor homology‐based models are known for any part of the amino acid sequence. These have been called "dark proteins," in contrast to non‐dark proteins, in which at least part of the sequence has a known or inferred structure. It has been hypothesized that non‐dark proteins may be more abundantly expressed than dark proteins, which are known to have much fewer sequence relatives. Surprisingly, the opposite has been observed: human dark and non‐dark proteins had quite similar levels of expression, in terms of both mRNA and protein abundance. Such high levels of expression strongly indicate that dark proteins—as a group—are important for cellular function. This is remarkable, given how carefully structural biologists have focused on proteins crucial for function, and highlights the important challenge posed by dark proteins in future research.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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].”
Modeling the language of life – Deep Learning Protein Sequences (2019, background): “for proteins with 41 substantial intrinsically disordered regions [15, 37, 38], or the entire Dark Proteome [39] full of 42 proteins that are less-well studied but important for function [40].”
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning (2021, background): “Whether protein LMs do not implicitly extract EI will have to be proven in separate publications. prediction offered by BERT or Albert suit protein sequences?”