ScienceBiophysicsPeer-reviewed journal article
Direct prediction of intermolecular interactions driven by disordered regions
Garrett M. GinelliD, Ryan J. EmeneckeriD, Jeffrey M. LotthammeriD, Alex T. KeeleyiD, Stephen P. PlassmeyeriD, Nicholas J. RazoiD, Emery T. UsheriD, Jacqueline F. PelhamiD, Alex S. HolehouseiD
Abstract
Intrinsically disordered regions (IDRs) are critical for a wide variety of cellular functions, many of which involve interactions with partner proteins. Molecular recognition is typically considered through the lens of sequence-specific binding events. However, a growing body of work has shown that IDRs often interact with partners in a manner that does not depend on the precise order of the amino acid order, instead driven by complementary chemical interactions leading to disordered bound-state complexes. Despite this emerging paradigm, we lack tools to describe, quantify, predict, and interpret these types of structurally heterogeneous interactions from the underlying amino acid sequences. Here, we repurpose the chemical physics developed originally for molecular simulations to develop an approach for predicting intermolecular interactions between IDRs and partner proteins. Our approach enables the direct prediction of phase diagrams, the identification of chemically-specific interaction hotspots on IDRs, and a route to develop and test mechanistic hypotheses regarding IDR function in the context of molecular recognition. We use our approach to examine a range of systems and questions to highlight its versatility and applicability.
- assay: protein sequence
- research focus: protein folding & dynamics
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
- Citations
- 123
- FWCI
- 28.29
- Field percentile
- top 1%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A Comprehensive Atlas and Machine-Learning Framework for Predicting IDR-Protein Binding Affinity
Subinoy Adhikari, Soham Choudhuri, Jagannath Mondal·bioRxiv·Feb 23, 2026
- BindCORE: Biophysical Ensemble Learning for Predicting Interaction Sites in Intrinsically Disordered Regions
Nicolas Buton, Luiz Felipe Piochi, Hamed Khakzad·bioRxiv·Aug 22, 2026
- Disobind: a sequence-based, partner-dependent contact map and interface residue predictor for intrinsically disordered regions
Kartik Majila, Varun Ullanat, Shruthi Viswanath·Cell SystemsPeer-reviewed·Jan 1, 2026
- DIRseq: a method for predicting drug-interacting residues of intrinsically disordered proteins from sequences
Matthew MacAinsh, Sanbo Qin, Huan‐Xiang Zhou·bioRxiv·May 2, 2025
- Sequence- and chemical specificity define the functional landscape of intrinsically disordered regions
I. Langstein-Skora, A. Schmid, … A. S. Holehouse·bioRxiv·Feb 10, 2022
- An interpretable machine learning algorithm to predict disordered protein phase separation based on biophysicalinteractions
H. Cai, R. M. Vernon, J. D. Forman-Kay·bioRxiv·Jul 6, 2022