SPEACH_AF: Sampling protein ensembles and conformational heterogeneity with Alphafold2

Richard A Stein and Hassane S Mchaourab

PLOS Computational Biology, 2022, vol. 18, issue 8, 1-16

Abstract: The unprecedented performance of Deepmind’s Alphafold2 in predicting protein structure in CASP XIV and the creation of a database of structures for multiple proteomes and protein sequence repositories is reshaping structural biology. However, because this database returns a single structure, it brought into question Alphafold’s ability to capture the intrinsic conformational flexibility of proteins. Here we present a general approach to drive Alphafold2 to model alternate protein conformations through simple manipulation of the multiple sequence alignment via in silico mutagenesis. The approach is grounded in the hypothesis that the multiple sequence alignment must also encode for protein structural heterogeneity, thus its rational manipulation will enable Alphafold2 to sample alternate conformations. A systematic modeling pipeline is benchmarked against canonical examples of protein conformational flexibility and applied to interrogate the conformational landscape of membrane proteins. This work broadens the applicability of Alphafold2 by generating multiple protein conformations to be tested biologically, biochemically, biophysically, and for use in structure-based drug design.Author summary: Many questions have arisen with the remarkable protein prediction capability of Alphafold2. One of the main questions is whether Alphafold2’s architecture is amenable to reveal the intrinsic conformational heterogeneity of proteins. The potential to obtain unseen or hidden conformations with high accuracy would greatly advance a broad range of structural biology pursuits. We have devised a method of in silico mutagenesis of the multiple sequence alignments (MSA) that are central to Alphafold2’s prediction capabilities. The approach consistently unveils conformations not seen with the unmodified default MSA. Analysis of the ensembles of predicted conformations relative to experimental structures fully support the biochemical significance of the models.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010483

DOI: 10.1371/journal.pcbi.1010483

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