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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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Predicting Pathology of Missense Mutations through Protein-Specific Evolutionary Pattern.

Bowei Ye, Boshen Wang, Jie Liang

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

Summary

Investigating protein evolution helps understand missense mutations. Protein-specific scoring matrices (PSM) improve detection of disease-causing mutations compared to general matrices like Blosum62.

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Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Missense mutations are common genetic alterations in human exons, potentially causing diseases.
Assessing missense mutation effects requires understanding protein evolutionary history under selection pressures.

Purpose of the Study:

To develop and evaluate protein-specific scoring matrices (PSM) for improved detection of missense mutation pathogenicity.
To investigate evolutionary patterns in protein sequences using a species tree and Markov models.

Main Methods:

Employed a continuous-time Markov model for analyzing protein sequence evolution.
Utilized Bayesian Markov chain Monte Carlo for estimating substitution rates and creating scoring matrices.
Examined evolutionary patterns in human muscle glycogen phosphorylase and 63 other proteins with known missense mutations.

Main Results:

Identified characteristic evolutionary patterns in 63 proteins, encompassing both deleterious and neutral missense mutations.
Protein-specific scoring matrices (PSM) demonstrated higher sensitivity in detecting pathological missense mutation effects than the general Blosum62 (BL62) matrix.
Incorporating PSM enhanced the performance of the structure-based SPRI model for missense mutation evaluation.

Conclusions:

Protein-specific evolutionary patterns provide a more sensitive approach to evaluating missense mutations.
PSM offer a valuable tool for understanding the functional impact of genetic variants and improving disease prediction models.