Direct prediction of intrinsically disordered protein conformational properties from sequences

JM Lotthammer and GM Ginell and D Griffith and RJ Emenecker and AS Holehouse, NATURE METHODS, 21 (2024).

DOI: 10.1038/s41592-023-02159-5

Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well described by a stable three-dimensional structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means that IDRs are largely absent from the Protein Data Bank, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations and deep learning to develop ALBATROSS, a deep- learning model for predicting ensemble dimensions of IDRs, including the radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences at a proteome-wide scale. ALBATROSS is lightweight, easy to use and accessible as both a locally installable software package and a point-and-click-style interface via Google Colab notebooks. We first demonstrate the applicability of our predictors by examining the generalizability of sequence-ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize the sequence-specific biophysical behavior of IDRs within and between proteomes. ALBATROSS is a deep-learning-based model for predicting ensemble properties of intrinsically disordered proteins and protein regions, such as radius of gyration, end-to-end distance, polymer-scaling exponent and ensemble asphericity, directly from sequences.

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