ArticlePLoS computational biology2022
Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- FAIDR-Desi: interactive software for feature analysis and design of intrinsically disordered regions.Bioinformatics (Oxford, England) · 2026Article
- Evolution of intrinsically disordered regions in vertebrate galectins for phase separation.EMBO reports · 2026Article
- Sequence and chemical specificity define the functional landscape of intrinsically disordered regions.Nature cell biology · 2026Article
- Inferring fungal cis-regulatory networks from genome sequences via unsupervised and interpretable representation learning.Genetics · 2026Article
- Investigating the role of the I-II linker in Nav1.5 channel function.The Journal of general physiology · 2025Article
- Protein language model identifies disordered, conserved motifs implicated in phase separation.eLife · 2025Article
- Sequence-based prediction of condensate composition reveals that specificity can emerge from multivalent interactions among disordered regions.Communications chemistry · 2025Article
- Cross-species rescue reveals sequence requirements for a rapidly evolving intrinsically disordered region.PLoS biology · 2025Article
- Sequence-based prediction of condensate composition reveals that specificity can emerge from multivalent interactions among disordered regions.bioRxiv : the preprint server for biology · 2025Article
- Enhancing nucleotide sequence representations in genomic analysis with contrastive optimization.Communications biology · 2025Article
- Evaluation of predictions of disordered binding regions in the CAID2 experiment.Computational and structural biotechnology journal · 2025Review
- SHARK enables sensitive detection of evolutionary homologs and functional analogs in unalignable and disordered sequences.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Beyond monopole electrostatics in regulating conformations of intrinsically disordered proteins.PNAS nexus · 2024Article
- PairK: Pairwise k-mer alignment for quantifying protein motif conservation in disordered regions.bioRxiv : the preprint server for biology · 2024Article
- Preserving condensate structure and composition by lowering sequence complexity.Biophysical journal · 2024Article
- Conserved molecular recognition by an intrinsically disordered region in the absence of sequence conservation.Research square · 2024Article
- Direct prediction of intermolecular interactions driven by disordered regions.bioRxiv : the preprint server for biology · 2024Article
- Evolutionary analyses of intrinsically disordered regions reveal widespread signals of conservation.PLoS computational biology · 2024Article
- Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.Bioinformatics (Oxford, England) · 2024Article
- EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.Bioinformatics (Oxford, England) · 2024Article
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6 authors.
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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 homolog 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.
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