Evaluating the performance of protein structure prediction in detecting structural changes of pathogenic nonsynonymous single nucleotide variants

Abstract

AO_SCPLOWBSTRACTC_SCPLOWProtein structure prediction serves as an efficient tool, saving time and circumventing the need for laborious experimental endeavors. Distinguished methodologies, including AlphaFold, RoseTTAFold, and ESMFold, have proven their precision through rigorous evaluation based on the last Critical Assessment of Protein Structure Prediction (CASP14). The success of protein structure prediction raises the following question: can the prediction tools discern structural alterations resulting from single amino acid changes? In this regard, the objective of this study is to assess the performance of existing structure prediction tools on mutated sequences. In this study, we posited that a specific fraction of the pathogenic nonsynonymous single nucleotide variants (nsSNVs) would experience structural alterations following amino acid mutations. We meticulously assembled an extensive dataset by initially sourcing data from ClinVar and subsequently applying multiple filters, resulting in 964 alternative sequences and their corresponding reference sequences. Utilizing UniProt, we acquired reference sequences and generated the corresponding alternative sequences based on variant information. This study performed three tools of structure prediction on both the reference and alternative sequences and expected some structural changes upon mutations. Our findings affirm AlphaFold as the foremost prediction tool presently; nonetheless, our experimental results underscore persistent challenges in accurately predicting structural alterations induced by nsSNVs. Discrepancies between the predicted structures of reference and alternative sequences, when observed, often stem from a lack of confidence in the predictions or the spatial separation between compact domains interrupted by disordered regions, posing challenges to successful alignment.