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Autore principale: Lacombe, Romain
Natura: Preprint
Pubblicazione: 2025
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Accesso online:https://arxiv.org/abs/2503.17368
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author Lacombe, Romain
author_facet Lacombe, Romain
contents Evolution-based protein structure prediction models have achieved breakthrough success in recent years. However, they struggle to generalize beyond evolutionary priors and on sequences lacking rich homologous data. Here we present a novel, out-of-domain benchmark based on sactipeptides, a rare class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by sulfur-to-$α$-carbon thioether bridges creating cross-links between cysteine residues and backbone. We evaluate recent models on predicting conformations compatible with these cross-links bridges for the 10 known sactipeptides with elucidated post-translational modifications. Crucially, the structures of 5 of them have not yet been experimentally resolved. This makes the task a challenging problem for evolution-based models, which we find exhibit limited performance (0.0% to 19.2% GDT-TS on sulfur-to-$α$-carbon distance). Our results point at the need for physics-informed models to sustain progress in biomolecular structure prediction.
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publishDate 2025
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spellingShingle Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models
Lacombe, Romain
Biomolecules
Artificial Intelligence
Evolution-based protein structure prediction models have achieved breakthrough success in recent years. However, they struggle to generalize beyond evolutionary priors and on sequences lacking rich homologous data. Here we present a novel, out-of-domain benchmark based on sactipeptides, a rare class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by sulfur-to-$α$-carbon thioether bridges creating cross-links between cysteine residues and backbone. We evaluate recent models on predicting conformations compatible with these cross-links bridges for the 10 known sactipeptides with elucidated post-translational modifications. Crucially, the structures of 5 of them have not yet been experimentally resolved. This makes the task a challenging problem for evolution-based models, which we find exhibit limited performance (0.0% to 19.2% GDT-TS on sulfur-to-$α$-carbon distance). Our results point at the need for physics-informed models to sustain progress in biomolecular structure prediction.
title Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models
topic Biomolecules
Artificial Intelligence
url https://arxiv.org/abs/2503.17368