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
A category-stratified, statistically powered benchmark comparing pose prediction from receptor conformational ensembles against AlphaFold2, used as a matched static-structure baseline, across 29 protein–ligand systems spanning cryptic-pocket, induced-fit, water-mediated, and autoimmune-indication target classes is pres...
Ryan Varghese, Pooja Tiwary, Krishil Oswal· bioRxiv· 0 citations
It is found that, while AF3 can perform well in favourable settings, this performance is uneven across applications and its predictions and use of confidence metrics will depend strongly on the specific application area and must be interpreted with respect to training-set overlap.
O. Follonier, Yan Liu, Pablo Campomanes et al.· bioRxiv· 1 citation
Results show that structural information from protein databases can be leveraged to steer AlphaFold2 toward alternative conformations, and AlphaConformers ranked first for modelling subtle conformational changes commonly observed between ligand-bound and unbound states.
Julie Daniel, Lucas Vitoriano De Queiroz Lira, Diego J. Zea· bioRxiv· 0 citations
A comprehensive benchmarking of five state-of-the-art protein structure prediction models demonstrates that prediction accuracy systematically improves with peptide length, and demonstrates that a multi-model consensus approach provides a rational framework for identifying robust structural hypotheses in the absence of...
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
It is argued that structure prediction should be reformulated as a state-space inference problem: recovering not one conformation's coordinates but accessible states, their energetic and kinetic relationships, context dependence, and responses to perturbations.
Devlina Chakravarty, Justin J. Miller, Da Teng et al.· 0 citations
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