AI Insight
Researchers created BENDER, a comprehensive database containing molecular dynamics simulations of 11,533 intrinsically disordered protein sequences from 13 different taxonomic groups across the tree of life. The study demonstrates that physics-based computational models developed for human proteins work reliably across diverse organisms, and reveals that certain structural properties of disordered proteins, particularly their contact network topology, follow universal physical laws rather than being unique to specific evolutionary lineages. Machine learning models trained on this cross-species data improved predictions of protein ensemble properties, including features relevant to phase separation behavior.
Why it matters
This work establishes that computational tools for studying disordered proteins can be reliably applied across all domains of life, enabling better understanding of protein behavior in diverse organisms from bacteria to humans. The findings could accelerate drug discovery and biotechnology applications by improving predictions of how disordered proteins behave and aggregate in different species.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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.