Direct prediction of intrinsically disordered protein conformational properties from sequence

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…

• NS
  National Science Foundation

  Awards: 2213983, 2128068, 2139839
• LL
  Longer Life Foundation
• WU
  Washington University in St. Louis
• HF
  Human Frontier Science Program

  Award: RGP0015/2022
• DO
  Division of Molecular and Cellular Biosciences

  Awards: 2128068, 2139839
• DO
  Division of Biological Infrastructure

  Award: 2213983