Accurate predictions of conformational ensembles of disordered proteins with STARLING
Novak, B.; Lotthammer, J. M.; Emenecker, R. J.; Holehouse, A. S.
Show abstract
Intrinsically disordered proteins and regions (collectively IDRs) are found across all kingdoms of life and play critical roles in virtually every eukaryotic cellular process. In contrast to folded proteins, IDRs lack a stable 3D structure and are instead described in terms of a conformational ensemble, a collection of energetically accessible interconverting structures. This unique structural plasticity facilitates diverse molecular recognition and function; thus, a convenient way to view IDRs is through their ensembles. Here, we combine advances in physics-based force fields for IDPs with the power of modern multi-scale generative modeling to develop STARLING, an approach for the rapid and accurate prediction of IDR ensembles directly from sequence. STARLING enables ensembles of hundreds of conformers to be generated in seconds and works on GPUs and CPUs. This, in turn, dramatically lowers the barrier to the computational interrogation of IDR function through the lens of emergent biophysical properties complementing bioinformatic protein sequence analysis. We evaluate STARLINGs accuracy against extant experimental data and offer a series of vignettes illustrating how STARLING can enable rapid hypothesis generation for IDR function and aid the interpretation of experimental data.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Direct prediction of intrinsically disordered protein conformational properties from sequence 98%
- CryoSTAR: Leveraging Structural Prior and Constraints for Cryo-EM Heterogeneous Reconstruction 96%
- Predicting structures of large protein assemblies using combinatorial assembly algorithm and AlphaFold2 96%
Similar papers in this journal
- Protein Language Models Trained on Biophysical Dynamics Inform Mutation Effects 98%
- Parametrically guided design of beta barrels and transmembrane nanopores using deep learning 97%
- Identifying Sequence Perturbations to an Intrinsically Disordered Protein that Determine Its Phase Separation Behavior 96%
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.