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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein Networks02:26

Protein Networks

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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Related Experiment Video

Updated: Apr 17, 2026

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

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A multiobjective memetic algorithm for PPI network alignment.

Connor Clark1, Jugal Kalita1

  • 1Department of Computer Science, University of Colorado Colorado Springs, Colorado Springs, CO 80918, USA.

Bioinformatics (Oxford, England)
|February 11, 2015
PubMed
Summary
This summary is machine-generated.

Optnetalign is a new algorithm for protein-protein interaction (PPI) network alignment. It balances topological and sequence similarity for better ortholog prediction, offering diverse, high-quality alignments.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Network Science

Background:

  • Protein-protein interaction (PPI) network alignment is crucial for identifying orthologous proteins across species.
  • Existing methods struggle to effectively integrate both network topology and sequence similarity for accurate orthology prediction.
  • Current aligners often prioritize one objective (topology or sequence similarity) over the other, leading to suboptimal results.

Purpose of the Study:

  • To develop a novel algorithm, Optnetalign, for multi-objective protein-protein interaction network alignment.
  • To optimize the conflicting objectives of topological and sequence similarity simultaneously.
  • To generate a diverse set of high-quality candidate alignments that represent the trade-off between these objectives.

Main Methods:

  • Implemented Optnetalign, a multiobjective memetic algorithm utilizing efficient swap-based local search, mutation, and crossover operations.
  • Employed the concept of Pareto dominance to optimize topological and sequence similarity concurrently.
  • Generated a population of alignments to explore the trade-off space between the two objectives.

Main Results:

  • Optnetalign produces superior alignments that achieve a better compromise between topological and biological match quality compared to previous methods.
  • The algorithm effectively characterizes the diversity of potential high-quality alignments between two networks.
  • Results offer valuable insights for future research in alignment evaluation, objective design, and interpretation of alignment outcomes.

Conclusions:

  • Optnetalign provides a robust framework for protein-protein interaction network alignment by effectively handling multiple, conflicting objectives.
  • The approach enhances the accuracy and diversity of ortholog predictions derived from network alignment.
  • This work has significant implications for advancing computational methods in comparative genomics and systems biology.