AlphaConformers: Structure-guided sampling enables prediction of multiple protein conformations

Authors

Daniel, J. L., Vitoriano De Queiroz Lira, L., Zea, D. J.

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 lligand-bound and unbound states. These results show that structural information from protein databases can be leveraged to steer AlphaFold2 toward alternative conformations.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 19 Aug 2026.

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