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Prediction of protein stability changes upon single-point variant using 3D structure profile
Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791599/ https://www.ncbi.nlm.nih.gov/pubmed/36582438 http://dx.doi.org/10.1016/j.csbj.2022.12.008 |
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author | Gong, Jianting Wang, Juexin Zong, Xizeng Ma, Zhiqiang Xu, Dong |
author_facet | Gong, Jianting Wang, Juexin Zong, Xizeng Ma, Zhiqiang Xu, Dong |
author_sort | Gong, Jianting |
collection | PubMed |
description | Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP. |
format | Online Article Text |
id | pubmed-9791599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-97915992022-12-28 Prediction of protein stability changes upon single-point variant using 3D structure profile Gong, Jianting Wang, Juexin Zong, Xizeng Ma, Zhiqiang Xu, Dong Comput Struct Biotechnol J Research Article Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP. Research Network of Computational and Structural Biotechnology 2022-12-08 /pmc/articles/PMC9791599/ /pubmed/36582438 http://dx.doi.org/10.1016/j.csbj.2022.12.008 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Gong, Jianting Wang, Juexin Zong, Xizeng Ma, Zhiqiang Xu, Dong Prediction of protein stability changes upon single-point variant using 3D structure profile |
title | Prediction of protein stability changes upon single-point variant using 3D structure profile |
title_full | Prediction of protein stability changes upon single-point variant using 3D structure profile |
title_fullStr | Prediction of protein stability changes upon single-point variant using 3D structure profile |
title_full_unstemmed | Prediction of protein stability changes upon single-point variant using 3D structure profile |
title_short | Prediction of protein stability changes upon single-point variant using 3D structure profile |
title_sort | prediction of protein stability changes upon single-point variant using 3d structure profile |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791599/ https://www.ncbi.nlm.nih.gov/pubmed/36582438 http://dx.doi.org/10.1016/j.csbj.2022.12.008 |
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