Enhancing coevolutionary signals in protein-protein interaction prediction through clade-wise alignment integration
Tao Fang1,2, Damian Szklarczyk1,2, Radja Hachilif1,2
1Department of Molecular Life Sciences, University of Zurich, 8057, Zurich, Switzerland.
Scientific Reports
|March 13, 2024
Summary
This study introduces a novel divide-and-conquer method for generating multiple sequence alignments (MSAs) to improve protein-protein interaction (PPI) prediction. This approach enhances accuracy and alignment quality for biological network analysis.
Area of Science:
- Computational Biology
- Bioinformatics
- Genomics
Background:
- Protein-protein interactions (PPIs) are crucial for biological functions.
- Predicting PPIs often relies on evolutionary constraints found in multiple sequence alignments (MSAs).
- Generating high-quality MSAs involves challenges in ortholog identification and balancing alignment size with accuracy.
Purpose of the Study:
- To develop an improved strategy for generating multiple sequence alignments (MSAs) for enhanced protein-protein interaction (PPI) prediction.
- To address limitations in current MSA construction methods for evolutionary constraint analysis.
Main Methods:
- A divide-and-conquer strategy for MSA generation, creating distinct alignments within specific clades.
- Separate coevolutionary signal searching within each clade.
- Integration of signals using machine learning techniques.
- Application of the Direct Coupling Analysis (DCA) algorithm for interaction scanning.
Main Results:
- The proposed strategy significantly enhances PPI prediction performance.
- Improved alignment quality is achieved compared to traditional single MSA approaches.
- Successful genome-wide PPI screening in a bacterial genome is demonstrated.
Conclusions:
- The divide-and-conquer MSA generation method offers a more accurate and efficient approach for PPI prediction.
- This method can serve as a pre-screening tool to complement high-resolution prediction methods like AlphaFold, reducing computational costs and false positives.
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