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A sequence-based evolutionary distance method for Phylogenetic analysis of highly divergent proteins
Wei Cao1, Lu-Yun Wu1, Xia-Yu Xia1
1Key Laboratory of Ministry of Education for Protein Science, School of Life Sciences, Tsinghua University, Beijing, 100084, China.
Scientific Reports
|November 21, 2023
Summary
A new sequence distance (SD) algorithm improves phylogenetic analysis for divergent protein sequences. This method accurately reveals evolutionary relationships and protein structures, offering a faster alternative to existing tools.
Area of Science:
- Bioinformatics
- Computational Biology
- Evolutionary Biology
Background:
- Phylogenetic analysis of highly divergent protein sequences is challenging due to limitations in current methods.
- Accurate evolutionary relationship inference is crucial for understanding protein family evolution and function.
Purpose of the Study:
- To develop a novel sequence-based evolutionary distance algorithm, termed sequence distance (SD).
- To improve the accuracy and efficiency of phylogenetic analysis for divergent protein sequences.
- To provide a reliable tool for exploring evolutionary relationships within and between protein families.
Main Methods:
- Developed the sequence distance (SD) algorithm, incorporating site-to-site correlations in protein sequences.
- Applied SD to analyze evolutionary relationships in protein superfamilies.
- Compared SD-derived phylogenetic trees with those based on structural information.
Main Results:
- SD effectively distinguishes evolutionary relationships within and between protein families, even with <20% sequence identity.
- Phylogenetic trees generated by SD closely align with those based on structural information.
- SD calculates evolutionary distances rapidly (seconds per thousands of pairs) on a single CPU, outperforming structure prediction methods.
Conclusions:
- The sequence distance (SD) algorithm offers a significant advancement in phylogenetic analysis for divergent proteins.
- SD provides a more accurate, reliable, and computationally efficient tool for evolutionary studies.
- This method enhances the understanding of protein evolution and structural similarities.
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