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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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LePrimAlign: local entropy-based alignment of PPI networks to predict conserved modules.

Sawal Maskey¹, Young-Rae Cho^2,3

¹Department of Computer Science, Baylor University, One Bear Place #97141, Waco, 76798, TX, USA.

|December 26, 2019

Summary

We developed LePrimAlign, a novel algorithm for local network alignment. It accurately identifies conserved protein-protein interaction modules across species, improving our understanding of cellular evolution.

Keywords:

Conserved modules Local network alignment Network alignment PPI networks Protein complex prediction Protein-protein interactions

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Area of Science:

Systems Biology
Bioinformatics
Computational Biology

Background:

Cross-species analysis of protein-protein interaction (PPI) networks reveals conserved patterns.
Understanding these conserved substructures aids in deciphering evolutionary principles of cellular organization and function.
Network alignment is crucial for predicting evolutionary conserved modules in genome-scale PPI networks.

Purpose of the Study:

To introduce a novel pairwise local network alignment algorithm, LePrimAlign.
To predict conserved modules between PPI networks of multiple species.
To address the challenge of developing scalable and accurate local network alignment methods.

Main Methods:

LePrimAlign utilizes results from a pairwise global alignment algorithm with many-to-many node mapping.
Graph entropy is employed to identify initial cluster pairs between two networks.
Initial clusters are expanded to optimize local alignment scores based on intra- and inter-network metrics.

Main Results:

LePrimAlign accurately predicts conserved protein complexes and yields high-quality alignments.
The algorithm demonstrates superior performance compared to existing state-of-the-art approaches.
Cross-species analysis using LePrimAlign enhances the detection of evolutionary conserved modules.

Conclusions:

LePrimAlign achieves higher accuracy in local network alignment for predicting conserved modules.
The method is effective even for large biological networks.
LePrimAlign offers a reduced computational cost compared to other methods.