Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning | bioRxiv

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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. W

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning | bioRxiv Skip to main content New Results Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning View ORCID Profile Alex X Lu , Amy X Lu , View ORCID Profile Iva Pritišanac , View ORCID Profile Taraneh Zarin , View ORCID Profile Julie D Forman-Kay , Alan M Moses doi: https://doi.org/10.1101/2021.07.29.454330 Alex X Lu 1 Department of Computer Science, University of Toronto, Toronto , Canada ( Microsoft Research , Cambridge, MA)

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning | bioRxiv

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