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James C. Gumbart

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#protein folding Open access Aug 2026

Autotransporter folding avoids a kinetic trap during vectorial translocation across the bacterial outer membrane

Autotransporter proteins are major virulence factors in Gram-negative pathogens, yet how they fold during secretion remains incompletely understood. A longstanding puzzle is why pertactin folds and is secreted in vivo within minutes but refolds in vitro over hours to days. We introduce BEAM, a multiscale framework that learns slow collective variables from coarse-grained simulations to guide all-atom enhanced sampling. Applied to a C-terminal segment of the pertactin passenger domain from Bordetella pertussis, BEAM achieved four- to six-fold greater conformational coverage than traditional collective-variable-guided adaptive sampling or unbiased molecular dynamics. The resulting free-energy landscape revealed a compact, non-native intermediate accessible in bulk solution but geometrically incompatible with vectorial translocation across the outer membrane. Kinetic simulations show that access to this intermediate slows folding, whereas excluding it produces rapid, in vivo-like kinetics. Together, these results explain how vectorial secretion accelerates pertactin folding by excluding an off-pathway kinetic trap. More broadly, BEAM provides a multiscale strategy for revealing hidden conformational states at atomic resolution.

Lan Yang, Qing Luan, Michael C. Baxa et al. · 0 citations
Open access Jul 2026

Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins

Protein structure predictors achieve high single-state accuracy, but it remains unclear whether they can recover functionally relevant conformational ensembles or account for the presence of ligands and/or binding partners. Here, we benchmark AlphaFold3, Boltz-2, Chai-1, and BioEmu on four canonical multi-state proteins (Pf-MATE, LAO, SecA, and β2AR), quantifying state bias and sampling breadth against experimental reference structures. Models frequently default to a dominant state represented in the PDB; small-molecule ligands have weak or inconsistent effects, while large protein partners drive clear conformational switching between states. Multiple sequence alignment (MSA)-based approaches (AF-Cluster and random subsampling) recapitulate similar biases, indicating that this behavior is not unique to newer architectures. These results underscore current limitations for multi-state protein structure prediction and structure-guided ligand discovery. TOC Graphic

Muhui Ye, Yu-Hong Wang, M. Brogi et al. · 0 citations