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Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning
Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning
- Publication Year :
- 2021
- Publisher :
- Cold Spring Harbor Laboratory, 2021.
-
Abstract
- A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call “reverse homology”, exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homologue from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.
- Subjects :
- Ecology
Proteome
Artificial neural network
Protein Conformation
business.industry
Computer science
Deep learning
Feature discovery
Mutagenesis (molecular biology technique)
Computational biology
Homology (biology)
Evolution, Molecular
Intrinsically Disordered Proteins
Cellular and Molecular Neuroscience
Computational Theory and Mathematics
Modeling and Simulation
Genetics
Identification (biology)
Amino Acid Sequence
Artificial intelligence
business
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Function (biology)
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....6566ffa05809bacf6bb9a0e93c4cfb3e
- Full Text :
- https://doi.org/10.1101/2021.07.29.454330