SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability
Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learni...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/22/2/606 |
| _version_ | 1797413961883189248 |
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| author | Gen Li Shailesh Kumar Panday Emil Alexov |
| author_facet | Gen Li Shailesh Kumar Panday Emil Alexov |
| author_sort | Gen Li |
| collection | DOAJ |
| description | Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers’ code. |
| first_indexed | 2024-03-09T05:26:00Z |
| format | Article |
| id | doaj.art-22fbd8c5c58444d5afab2fd1ed686158 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T05:26:00Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-22fbd8c5c58444d5afab2fd1ed6861582023-12-03T12:36:50ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-01-0122260610.3390/ijms22020606SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic StabilityGen Li0Shailesh Kumar Panday1Emil Alexov2Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USADepartment of Physics and Astronomy, Clemson University, Clemson, SC 29634, USADepartment of Physics and Astronomy, Clemson University, Clemson, SC 29634, USAModeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers’ code.https://www.mdpi.com/1422-0067/22/2/606thermodynamics stabilitysingle point mutationsequence-basedmachine learningweb server |
| spellingShingle | Gen Li Shailesh Kumar Panday Emil Alexov SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability International Journal of Molecular Sciences thermodynamics stability single point mutation sequence-based machine learning web server |
| title | SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability |
| title_full | SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability |
| title_fullStr | SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability |
| title_full_unstemmed | SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability |
| title_short | SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability |
| title_sort | saafec seq a sequence based method for predicting the effect of single point mutations on protein thermodynamic stability |
| topic | thermodynamics stability single point mutation sequence-based machine learning web server |
| url | https://www.mdpi.com/1422-0067/22/2/606 |
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