Direct prediction of intrinsically disordered protein conformational properties from sequence.
Nat Methods
; 21(3): 465-476, 2024 Mar.
Article
in En
| MEDLINE
| ID: mdl-38297184
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Intrinsically Disordered Proteins
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Nat Methods
Year:
2024
Document type:
Article