Packpred: predicting the functional effect of missense mutations
Predicting the functional consequences of single point mutations has relevance to protein function annotation and to clinical analysis/diagnosis. We developed and tested Packpred that makes use of a multi-body clique statistical potential in combination with a depth-dependent amino acid substitution...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/160847 |
| _version_ | 1811685009779589120 |
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| author | Tan, Kuan Pern Kanitkar, Tejashree Rajaram Kwoh, Chee Keong Madhusudhan, Mallur Srivatsan |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Tan, Kuan Pern Kanitkar, Tejashree Rajaram Kwoh, Chee Keong Madhusudhan, Mallur Srivatsan |
| author_sort | Tan, Kuan Pern |
| collection | NTU |
| description | Predicting the functional consequences of single point mutations has relevance to protein function annotation and to clinical analysis/diagnosis. We developed and tested Packpred that makes use of a multi-body clique statistical potential in combination with a depth-dependent amino acid substitution matrix (FADHM) and positional Shannon entropy to predict the functional consequences of point mutations in proteins. Parameters were trained over a saturation mutagenesis data set of T4-lysozyme (1,966 mutations). The method was tested over another saturation mutagenesis data set (CcdB; 1,534 mutations) and the Missense3D data set (4,099 mutations). The performance of Packpred was compared against those of six other contemporary methods. With MCC values of 0.42, 0.47, and 0.36 on the training and testing data sets, respectively, Packpred outperforms all methods in all data sets, with the exception of marginally underperforming in comparison to FADHM in the CcdB data set. A meta server analysis was performed that chose best performing methods of wild-type amino acids and for wild-type mutant amino acid pairs. This led to an increase in the MCC value of 0.40 and 0.51 for the two meta predictors, respectively, on the Missense3D data set. We conjecture that it is possible to improve accuracy with better meta predictors as among the seven methods compared, at least one method or another is able to correctly predict ∼99% of the data. |
| first_indexed | 2024-10-01T04:37:43Z |
| format | Journal Article |
| id | ntu-10356/160847 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:37:43Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1608472022-08-03T07:28:22Z Packpred: predicting the functional effect of missense mutations Tan, Kuan Pern Kanitkar, Tejashree Rajaram Kwoh, Chee Keong Madhusudhan, Mallur Srivatsan School of Computer Science and Engineering Engineering::Computer science and engineering Missense Mutation Effect Prediction Amino Acid Depth Predicting the functional consequences of single point mutations has relevance to protein function annotation and to clinical analysis/diagnosis. We developed and tested Packpred that makes use of a multi-body clique statistical potential in combination with a depth-dependent amino acid substitution matrix (FADHM) and positional Shannon entropy to predict the functional consequences of point mutations in proteins. Parameters were trained over a saturation mutagenesis data set of T4-lysozyme (1,966 mutations). The method was tested over another saturation mutagenesis data set (CcdB; 1,534 mutations) and the Missense3D data set (4,099 mutations). The performance of Packpred was compared against those of six other contemporary methods. With MCC values of 0.42, 0.47, and 0.36 on the training and testing data sets, respectively, Packpred outperforms all methods in all data sets, with the exception of marginally underperforming in comparison to FADHM in the CcdB data set. A meta server analysis was performed that chose best performing methods of wild-type amino acids and for wild-type mutant amino acid pairs. This led to an increase in the MCC value of 0.40 and 0.51 for the two meta predictors, respectively, on the Missense3D data set. We conjecture that it is possible to improve accuracy with better meta predictors as among the seven methods compared, at least one method or another is able to correctly predict ∼99% of the data. Published version This work was supported by a Wellcome Trust-DBT India Alliance Senior Fellowship. 2022-08-03T07:28:22Z 2022-08-03T07:28:22Z 2021 Journal Article Tan, K. P., Kanitkar, T. R., Kwoh, C. K. & Madhusudhan, M. S. (2021). Packpred: predicting the functional effect of missense mutations. Frontiers in Molecular Biosciences, 8, 646288-. https://dx.doi.org/10.3389/fmolb.2021.646288 2296-889X https://hdl.handle.net/10356/160847 10.3389/fmolb.2021.646288 34490344 2-s2.0-85114375483 8 646288 en Frontiers in Molecular Biosciences © 2021 Tan, Kanitkar, Kwoh and Madhusudhan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Missense Mutation Effect Prediction Amino Acid Depth Tan, Kuan Pern Kanitkar, Tejashree Rajaram Kwoh, Chee Keong Madhusudhan, Mallur Srivatsan Packpred: predicting the functional effect of missense mutations |
| title | Packpred: predicting the functional effect of missense mutations |
| title_full | Packpred: predicting the functional effect of missense mutations |
| title_fullStr | Packpred: predicting the functional effect of missense mutations |
| title_full_unstemmed | Packpred: predicting the functional effect of missense mutations |
| title_short | Packpred: predicting the functional effect of missense mutations |
| title_sort | packpred predicting the functional effect of missense mutations |
| topic | Engineering::Computer science and engineering Missense Mutation Effect Prediction Amino Acid Depth |
| url | https://hdl.handle.net/10356/160847 |
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