bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.01.29.702558

Resolving Conformational Heterogeneity in IntrinsicallyDisordered Proteins via Experimentally Guided Multi-Replica Simulations

Yang, W.·Yang, S.·Zheng, W.
View original source ↗Export metadata ↓

Abstract

The conformational landscapes of intrinsically disordered proteins (IDPs) remain obscured by the ensemble averaging inherent to experimental observables. Here, we present Multi-replica Averaged Restraint Simulation (MARS), a data-driven modeling framework that reconstructs these landscapes by enforcing ensemble-averaged restraints across multiple replicas without imposing prior structural knowledge. Using the N-terminal domain of estrogen receptor alpha (ER-NTD) as a model system, MARS simultaneously integrates small-angle X-ray scattering (SAXS) and six paramagnetic relaxation enhancement (PRE) profiles, comprising over 600 pairwise restraints, to generate ensembles that quantitatively fit all input structural restraints. The resulting ensemble is consistent with independent backbone relaxation measurements and reveals two major conformational states: a dominant extended state and a low-populated yet functionally relevant compact state whose structural features align with prior mutagenesis studies. Systematic benchmarking demonstrates that SAXS and PRE provide orthogonal global and local constraints, that each PRE profile contributes non-redundant structural information, and that multi-replica sampling is essential for preserving conformational heterogeneity. MARS offers a scalable framework for integrating orthogonal biophysical measurements to resolve both dominant and rare functional states in IDPs.

Explore related subjects

biophysicsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yang, W., Yang, S., Zheng, W.. 2026-02-03. Resolving Conformational Heterogeneity in IntrinsicallyDisordered Proteins via Experimentally Guided Multi-Replica Simulations. https://doi.org/10.64898/2026.01.29.702558

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Mechanism of molecular recognition revealed through dynamic drug binding pathways to SARS-CoV-2 main protease

Characterization of drug-binding pathways remains experimentally limited by transient intermediates and computationally challenging due to long timescales intractable for conventional molecular dynamics. To address these challenges, we combined solution NMR titrations with weighted ensemble (WE) enhanced sampling simulations to resolve atomistic pathways of nirmatrelvir binding to the SARS-CoV-2 main protease. NMR titration revealed residue-dependent heterogeneity spanning fast, intermediate, and slow exchange regimes. WE simulations complement the NMR by providing insights into unassigned residues and adding time-resolved and three-dimensional structural context. We map key interactions along two distinct binding pathways, provide dynamic explanations for residues involved in resistance, and capture unique backbone conformations compared to those sampled in unbound or bound states. Our comprehensive binding model is consistent with a combined conformational selection and induced fit mechanism in which early transient contacts are made with residues E47 and L50 and allosteric motions are centered around residue V204 of the distal domain. This synergistic application of WE and titration NMR enables a more comprehensive characterization of drug binding than either method alone, providing an integrated framework that may have broader applicability to defining structure-kinetic relationships and guiding design of next-generation inhibitors.

biophysics↗
bioRxiv2026

Discriminating betacoronavirus receptor usage across subgenera using protein structure prediction and molecular dynamics

A critical step in the emergence of a virus is the ability of the viral protein to bind a host receptor and mediate cell entry. For many coronaviruses, this interaction occurs between the Spike S1 subunit and the human ACE2 receptor. Whether this binding interface can be computationally distinguished across unstudied viruses without experimentally resolved protein structures remains an open question. We predicted how 28 emerging coronaviruses may bind to human ACE2 using structural predictions, static interaction prediction programs, and molecular dynamics simulations. To screen the emerging coronaviruses, we predicted a library of S1 structures using AlphaFold. These predicted structures were then used to model the S1-ACE2 interaction with AlphaFold, ClusPro, and HADDOCK. We used known ACE2-binding sarbecoviruses as positive controls and coronaviruses that bind other receptors as negative controls to threshold predicted binding. Contact analysis quantified the predicted binding and revealed that these static interaction prediction methods varied in discriminative power. Less restrained static predictions separated binders from non-binders, whereas heavily restrained docking did not, potentially forcing an interaction where none should exist. This analysis highlighted an emerging coronavirus, Zhejiang2013, as a potential ACE2 binder. We used molecular dynamics simulations to further assess the static predictions and model the interaction over time. Overall, our results indicate that Zhejiang2013 exhibits dynamic interaction patterns consistent with ACE2 binding. Given that two ACE2-binding coronaviruses have caused global pandemics within the past two decades, identifying potential ACE2 binders is critical for early warning and pandemic preparedness.

biophysics↗
bioRxiv2026

De novo design of flexible protein interactions with GuideFlip

De novo design of protein binders requires a target structure. However, for flexible targets, such as intrinsically disordered proteins, this structure does not exist until the binder has stabilized the interaction. Such targets are therefore difficult for methods that separate structure generation from sequence design. We introduce GuideFlip, which co-designs structure and sequence through guided discrete flow matching: binder residues are assigned progressively while the complex is re-predicted at each step, allowing the evolving interface to affect the design process. GuideFlip reduces the hydrophobic bias of direct AlphaFold optimization and improves in silico success rates over existing approaches. We release a database of binder candidates for 177 human disordered proteins. Experimentally, we obtain de novo binders to the C-terminus of -synuclein and the disordered amino terminus of RBX1 with hit rates of 13.5% and 41.7%, respectively, and we confirm the epitopes of selected binders by NMR and mutagenesis. Applying GuideFlip to flexibility on the binder side, we design a nanobody that binds the agonist-bound {beta}1-adrenergic receptor in the active state, but not the receptor in its inactive state, with a 75% hit rate and cryo-EM structure confirming the design. GuideFlip enables protein design where bound structures emerge only upon binding.

biophysics↗