Intrinsically disordered proteins and regions are found across all kingdoms of life, yet the computational characterisation of their conformational ensembles has remained almost entirely confined to the human proteome. Whether the physics-based force fields developed on eukaryotic sequences remain reliable for taxonomically distant organisms, and whether the sequence–ensemble relationships they reveal reflect conserved physical laws or the peculiarities of a single evolutionary window, are questions fundamental to the field. Here we introduce BENDER, a dataset of 11,533 IDP sequences spanning 13 taxonomic groups, each simulated under CALVADOS-2 molecular dynamics and annotated with ensemble-level geometric and novel contact-network properties, together with per-sequence pi–pi and cation–pi contact frequencies linked to phase-separation propensity. We show that CALVADOS-2 ensembles agree strongly with an orthogonal structural reference across the full dataset, with both held-out taxa performing above the dataset median, and that direct comparison against a second independently parameterised force field reveals no systematic scaling-exponent bias. We find that cross-taxon training data improves out-of-distribution ensemble prediction in two independent architectures, and that ensemble contact-network global efficiency is accurately predictable from sequence alone on held-out viral sequences. Positive degree assortativity is conserved across all taxonomic groups, suggesting that hub topology in disordered protein contact networks is a conserved physical feature of sequence-encoded disorder rather than an evolutionary contingency.
BindCORE is introduced, an ensemble-aware deep learning framework that integrates global, local, and pairwise biophysical descriptors to predict interaction sites within IDRs and shows that conformational ensembles contain interpretable determinants of LIP and MoRF binding residues.
Nicolas Buton, Luiz Felipe Piochi, Hammed Khakzad· bioRxiv· 0 citations
The discordance persists: pLDDT correlates positively with PUNCH2 disorder in random and de novo proteins and negatively with β-strand fraction, opposite to the conserved and disordered baselines, a concrete failure mode that protein designers and other working on sequences remote in sequence space should be aware of w...
Lars A. Eicholt, Lasse Middendorf· bioRxiv· 0 citations
Pi-Ensemble (Predicting Interpolated Ensemble), a sequence-guided framework for generating protein conformational ensembles interpolating between two structural anchor states, provides an extensible framework for studying protein flexibility, guiding adaptive sampling, and accelerating mechanistic investigations of pro...
Hassan Nadeem, D. Kleiman, Yu-Ming Zhou et al.· bioRxiv· 0 citations
An improved force field is developed, derived from its parent, Amber ff24EXP-GA, and its evaluation against Amber ff14SB and other contemporary force fields, such as CHARMM36m, in capturing the empirically determined conformational properties of unfolded systems: short peptides that serve as model systems for IDPs, and...
The benchmark quantifies which atomistic properties can be recovered after projecting a single-bead coarse-grained force field ensemble into all-atom space and identifies improved CG geometric encoding and sequence-conditioned AI priors as the directions for further progress.
Jianxiang Huang, Xin Qiao, Ning Liu et al.· Journal of Chemical Informat...· 0 citations
This work proposes the first Bayesian flow formulation for protein backbone orientations by recasting orientation modeling as an equivalent hyperspherical generation problem with antipodal symmetry and delivers consistently exceptional performance in both peptide and antibody design tasks.
Han-Lin Wu, Yuxuan Song, Zhe Zhang et al.· Neural Information Processin...· 0 citations
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