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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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SAlign-a structure aware method for global PPI network alignment.

Umair Ayub1,2, Imran Haider1,2, Hammad Naveed3,4

  • 1Department of Computing, National University of Computer and Emerging Sciences, Islamabad, 40100, Pakistan.

BMC Bioinformatics
|November 5, 2020
PubMed
Summary
This summary is machine-generated.

A new protein-protein interaction network alignment algorithm, SAlign, improves accuracy by integrating sequence and structural data. This method offers more biologically significant alignments and identifies more protein interactions than existing approaches.

Keywords:
Global network alignmentMonte–Carlo algorithmProtein–protein interactionSequence similarityStructure similarity

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • High-throughput experiments generate vast protein interaction data for studying biological networks.
  • Comparative analysis of protein-protein interaction (PPI) networks across species aids disease analysis and drug design.
  • Accurate and scalable global network alignment remains a challenge.

Purpose of the Study:

  • To present a novel global pairwise network alignment algorithm, SAlign.
  • To improve the biological significance and accuracy of protein-protein interaction network alignments.
  • To incorporate both topological and biological information, including sequence and structural data.

Main Methods:

  • Developed SAlign, a global pairwise network alignment algorithm.
  • Incorporated sequence and structural information to compute biological scores.
  • Compared SAlign with state-of-the-art algorithms using semantic similarity and aligned node counts.

Main Results:

  • SAlign demonstrates significantly better pairwise alignments by combining sequence and structural information.
  • Achieved 3-63% semantic improvement and aligned 5-14% more nodes on networks with available protein structures.
  • Results are comparable or superior to existing methods on other network pairs.
  • Introduced a Monte Carlo-based algorithm for generating multiple, biologically meaningful alignments.

Conclusions:

  • SAlign generates more biologically significant and relevant alignments than existing methods.
  • The algorithm facilitates the study of different genes and proteins within a species by providing alternate alignments.