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.
Abstract
Proteins are dynamic molecules capable of adopting multiple conformations. However, AlphaFold2 predominantly generates models around a single conformation, usually representing a ligand-bound state. To address this limitation, we developed AlphaConformers, a structure-guided pipeline that steers AlphaFold2 toward alternative conformations. It is based on the idea that protein structure databases can capture the structural space accessible to members of a protein family. Given a target protein, AlphaConformers retrieves structures from structurally similar proteins. These structures are organized into structure-based alignments and template sets, which are supplied to AlphaFold2 as conformational hypotheses. The resulting models are clustered and filtered, facilitating their analysis. Evaluated on a curated benchmark of 88 proteins with known ligand-bound and unbound conformations, AlphaConformers expanded AlphaFold2 conformational sampling and recovered alternative states missed by AlphaFold2 and other state-of-the-art methods. AlphaConformers ranked first for modelling subtle conformational changes commonly observed between ligand-bound and unbound states. These results show that structural information from protein databases can be leveraged to steer AlphaFold2 toward alternative conformations.
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 protein function.
Hassan Nadeem, D. Kleiman, Yu-Ming Zhou et al.· bioRxiv· 0 citations
Deep learning-based protein structure prediction methods that leverage evolutionary information from multiple sequence alignments (MSAs), exemplified by AlphaFold2, have achieved remarkable accuracy. However, existing methods still struggle to predict challenging proteins, particularly those with novel folds or limited evolutionary information, and to recover alternative conformational states. Here we show that structure prediction models trained under different MSA-depth distributions corresponding to different levels of evolutionary information exhibit complementary generalization behaviors, and that a model trained on a mixture of these distributions can combine their complementary generalization strengths. Building on this insight, we developed ProtMonomer, a deep learning framework trained on MSA-depth distributions representing a broad range of evolutionary information levels to improve structure prediction. Across benchmarks comprising CASP15 targets, non-redundant experimentally determined structures, orphan proteins, and short peptides, ProtMonomer performed comparably to or better than leading methods, including AlphaFold2 and AlphaFold3, with particularly strong performance on challenging targets. For fold-switching proteins, ProtMonomer also recovered alternative conformational states more accurately than AlphaFold2 and AlphaFold3 across diverse homologous sequence sampling strategies. In addition to improving predictive accuracy, ProtMonomer substantially reduced inference cost through an efficient architecture, enabling high-throughput applications. Together, these findings provide insights into the generalization of evolution-informed structure prediction models and support ProtMonomer as an accurate and efficient framework for protein structure prediction.
Protein kinases are critical drug targets, requiring therapeutics that can modulate their active and inactive conformational states. While cofolding models can generate global folds directly from kinase sequences and ligand SMILES strings, these models have not yet been tested on their ability to recover ligand-induced-fit conformational states of the kinase proteins. Here, we introduce KinConfBench, a curated benchmark of 2225 high-quality human kinase chains to evaluate the ability of four state-of-the-art cofolding models—Boltz-2, Chai-1, Protenix, and RoseTTAFold-All-Atom—to recover both canonical and rare conformational states. We show that geometric success metrics of a ligand pose in the active site do not correlate strongly with the correct kinase conformational state, motivating a new set of dynamical benchmarks for assessing cofolding models. While all four cofolding models achieve ~60–80% prediction accuracy for kinase conformational classification, they exhibit severe mode collapse when performing multiple inferences, show negligible structural diversity in sampling induced-fit motions, and display a prevalent “apo-drift” in which most cofolding models predominantly predict the kinase to be in its ligand-free state. Our results highlight that capturing ligand-induced protein conformational diversity, not just geometric fit, is critical for next-generation structure-based drug discovery.
Kunyang Sun, T. Head-Gordon· npj Drug Discovery· 0 citations
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 presented.
Ryan Varghese, Pooja Tiwary, Krishil Oswal· bioRxiv· 0 citations
Carbonara is presented, a framework that uses experimental small-angle X-ray scattering (SAXS) data to predict alternative physically plausible protein conformations and provides a route from static structural models of flexible multi-domain proteins and multimeric assemblies to solution-state ensembles.
By smoothing the Evoformer's weight tensors with a Gaussian convolution and scaling the result, it is shown that the trained model produces physically structured conformational landscapes, appearing to encode structural constraints that extend beyond what unperturbed inference reveals.
Kaustav Mehta· arXiv.org· 0 citations
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