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...
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2021-08-01
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doaj-90d56263634d4b0783db996a58c6e7492021-08-20T05:27:43ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2021-08-01810.3389/fmolb.2021.646288646288Packpred: Predicting the Functional Effect of Missense MutationsKuan Pern Tan0Kuan Pern Tan1Tejashree Rajaram Kanitkar2Chee Keong Kwoh3Mallur Srivatsan Madhusudhan4Bioinformatics Institute, Singapore, SingaporeSchool of Computer Engineering, Nanyang Technological University, Singapore, SingaporeIndian Institute of Science Education and Research, Pune, IndiaSchool of Computer Engineering, Nanyang Technological University, Singapore, SingaporeIndian Institute of Science Education and Research, Pune, IndiaPredicting 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.https://www.frontiersin.org/articles/10.3389/fmolb.2021.646288/fullmissense mutation effect predictionamino acid depthlocal environment/cliquestatistical potentialmeta predictor |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kuan Pern Tan Kuan Pern Tan Tejashree Rajaram Kanitkar Chee Keong Kwoh Mallur Srivatsan Madhusudhan |
spellingShingle |
Kuan Pern Tan Kuan Pern Tan Tejashree Rajaram Kanitkar Chee Keong Kwoh Mallur Srivatsan Madhusudhan Packpred: Predicting the Functional Effect of Missense Mutations Frontiers in Molecular Biosciences missense mutation effect prediction amino acid depth local environment/clique statistical potential meta predictor |
author_facet |
Kuan Pern Tan Kuan Pern Tan Tejashree Rajaram Kanitkar Chee Keong Kwoh Mallur Srivatsan Madhusudhan |
author_sort |
Kuan Pern Tan |
title |
Packpred: Predicting the Functional Effect of Missense Mutations |
title_short |
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_sort |
packpred: predicting the functional effect of missense mutations |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Molecular Biosciences |
issn |
2296-889X |
publishDate |
2021-08-01 |
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. |
topic |
missense mutation effect prediction amino acid depth local environment/clique statistical potential meta predictor |
url |
https://www.frontiersin.org/articles/10.3389/fmolb.2021.646288/full |
work_keys_str_mv |
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