Evaluating the Performance of Protein Structure Prediction in Detecting Structural Changes of Pathogenic Nonsynonymous Single Nucleotide Variants

Hong-Sheng Lai, Hong-Sheng Lai, Chien-Yu Chen, Chien-Yu Chen

2024

Abstract

Protein 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 in 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 2,371 pathogenic nsSNVs. 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 nonsynonymous SNVs. Discrepancies in predicted structures, 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. The findings from this study highlight the ongoing challenges in accurately predicting the structure of mutated sequences. To enhance the refinement of prediction models, there is a clear need for additional experimentally determined structures of proteins with nsSNVs in the future.

Paper Citation

in Harvard Style

Lai H. and Chen C. (2024). Evaluating the Performance of Protein Structure Prediction in Detecting Structural Changes of Pathogenic Nonsynonymous Single Nucleotide Variants. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-688-0, SciTePress, pages 495-503. DOI: 10.5220/0012431600003657

in Bibtex Style

@conference{bioinformatics24,
author={Hong-Sheng Lai and Chien-Yu Chen},
title={Evaluating the Performance of Protein Structure Prediction in Detecting Structural Changes of Pathogenic Nonsynonymous Single Nucleotide Variants},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={495-503},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012431600003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Evaluating the Performance of Protein Structure Prediction in Detecting Structural Changes of Pathogenic Nonsynonymous Single Nucleotide Variants
SN - 978-989-758-688-0
AU - Lai H.
AU - Chen C.
PY - 2024
SP - 495
EP - 503
DO - 10.5220/0012431600003657
PB - SciTePress