Bridging the gaps in statistical models of protein alignment.
Dinithi Sumanaweera1, Lloyd Allison1, Arun S Konagurthu1
1Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia.
Bioinformatics (Oxford, England)
|June 27, 2022
Summary
This study introduces a unified statistical model for protein sequence evolution, integrating substitutions and indels. The new MMLSUM model outperforms existing methods in quantifying evolutionary relationships.
Area of Science:
- Bioinformatics
- Computational Biology
- Evolutionary Biology
Background:
- Protein sequence evolution is typically modeled by analyzing substitutions and insertions/deletions (indels) separately.
- This disconnected approach simplifies computation but lacks a holistic view of evolutionary processes.
- A unified model is needed to accurately quantify protein sequence relationships.
Purpose of the Study:
- To develop a complete statistical model that simultaneously quantifies protein sequence evolution, including both substitutions and indels.
- To create a novel unified model, MMLSUM, and compare its performance against established models.
- To provide a systematic method for deriving model parameters from aligned protein sequence data.
Main Methods:
- Constructed a time-parameterized substitution matrix and a time-parameterized alignment state machine for a unified evolutionary model.
- Developed methods to derive all model parameters from benchmark collections of aligned protein sequences.
- Evaluated model performance by measuring Shannon information content for lossless explanation of alignments.
Main Results:
- Generated unified statistical models for nine widely used substitution matrices and introduced the new MMLSUM model.
- MMLSUM demonstrated superior performance in quantifying protein sequence evolution compared to existing models.
- PFASUM, VTML, BLOSUM, and MIQS were identified as the next best-performing models.
Conclusions:
- The developed unified statistical model provides a more accurate and comprehensive approach to quantifying protein sequence evolution.
- MMLSUM represents a significant advancement, offering improved performance over established models.
- The methodology enables systematic model generation and performance evaluation for evolutionary studies.
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