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Prediction of Functional Effects of Protein Amino Acid Mutations

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Bioinformatics and Biomedical Engineering (IWBBIO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13920))

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Abstract

Human Single Amino Acid Polymorphisms (SAPs) or Single Amino Acid Variants (SAVs) usually named as nonsynonymous Single Nucleotide Variants nsSNVs) represent the most frequent type of genetic variation among the population. They originate from non-synonymous single nucleotide variations (missense variants) where a single base pair substitution alters the genetic code in such a way that it produces a different amino acid at a given position. Since mutations are commonly associated with the development of various genetic diseases, it is of utmost importance to understand and predict which variations are deleterious and which are neutral. Computational tools based on machine learning are becoming promising alternatives to tedious and highly costly mutagenic experiments. Generally, varying quality, incompleteness and inconsistencies of nsSNVs datasets degrade the usefulness of machine learning approaches. Consequently, robust and more accurate approaches are essential to address these issues. In this paper, we present the application of a consensus classifier based on the holdout sampling, which shows robust and accurate results, outperforming currently available tools. We generated 100 holdouts to sample different classifiers’ architectures and different classification variables during the training stage. The best performing holdouts were selected to construct a consensus classifier and tested by blindly utilizing a k-fold (1 ≤ k ≤ 5) cross-validation approach. We also performed an analysis of the best protein attributes for predicting the effects of nsSNVs by calculating their discriminatory power. Our results show that our method outperforms other currently available tools, and provides robust results, with small standard deviations among folds and high accuracy. The superiority of our algorithm is based on the utilization of a tree of holdouts, where different machine learning algorithms are sampled with different boundary conditions or different predictive attributes.

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Acknowledgment

AK acknowledges the financial support from NSF grant DBI 1661391, and NIH grants R01GM127701, and R01HG012117.

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Authors and Affiliations

  1. Department of Mathematics, University of Oviedo, 33007, Oviedo, Spain

    Óscar Álvarez-Machancoses, Enrique J. de Andrés-Galiana & Juan Luis Fernández-Martínez

  2. Department of Physics, Indiana University Purdue University, Indianapolis, IN, 46202, USA

    Eshel Faraggi

  3. The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children Hospital, Columbus, OH, 43205, USA

    Andrzej Kloczkowski

  4. Department of Pediatrics, The Ohio State University, Columbus, OH, 43205, USA

    Andrzej Kloczkowski

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  1. Óscar Álvarez-Machancoses
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  2. Eshel Faraggi
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  3. Enrique J. de Andrés-Galiana
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  4. Juan Luis Fernández-Martínez
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  5. Andrzej Kloczkowski
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Correspondence to Andrzej Kloczkowski .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Olga Valenzuela

  3. University of Granada, Granada, Spain

    Fernando Rojas Ruiz

  4. University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Álvarez-Machancoses, Ó., Faraggi, E., de Andrés-Galiana, E.J., Fernández-Martínez, J.L., Kloczkowski, A. (2023). Prediction of Functional Effects of Protein Amino Acid Mutations. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_5

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  • DOI: https://doi.org/10.1007/978-3-031-34960-7_5

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