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ColabFold predicts alternative protein structures from single sequences, coevolution unnecessary for AF-cluster.


ABSTRACT: Though typically associated with a single folded state, globular proteins are dynamic and often assume alternative or transient structures important for their functions1,2. Wayment-Steele, et al. steered ColabFold3 to predict alternative structures of several proteins using a method they call AF-cluster4. They propose that AF-cluster "enables ColabFold to sample alternate states of known metamorphic proteins with high confidence" by first clustering multiple sequence alignments (MSAs) in a way that "deconvolves" coevolutionary information specific to different conformations and then using these clusters as input for ColabFold. Contrary to this Coevolution Assumption, clustered MSAs are not needed to make these predictions. Rather, these alternative structures can be predicted from single sequences and/or sequence similarity, indicating that coevolutionary information is unnecessary for predictive success and may not be used at all. These results suggest that AF-cluster's predictive scope is likely limited to sequences with distinct-yet-homologous structures within ColabFold's training set.

SUBMITTER: Porter LL 

PROVIDER: S-EPMC10705582 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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ColabFold predicts alternative protein structures from single sequences, coevolution unnecessary for AF-cluster.

Porter Lauren L LL   Chakravarty Devlina D   Schafer Joseph W JW   Chen Ethan A EA  

bioRxiv : the preprint server for biology 20231125


Though typically associated with a single folded state, globular proteins are dynamic and often assume alternative or transient structures important for their functions<sup>1,2</sup>. Wayment-Steele, et al. steered ColabFold<sup>3</sup> to predict alternative structures of several proteins using a method they call AF-cluster<sup>4</sup>. They propose that AF-cluster "enables ColabFold to sample alternate states of known metamorphic proteins with high confidence" by first clustering multiple sequ  ...[more]

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