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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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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.
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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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cpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein-Protein Interactions.

Zi Liu1, Dong-Jun Yu1

  • 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

International Journal of Molecular Sciences
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

Generating accurate protein complex multiple sequence alignments (cpxMSA) is crucial for coevolution analysis. The novel cpxDeepMSA approach effectively combines monomer MSAs using multiple strategies, overcoming previous limitations for improved predictions.

Keywords:
STRING interaction networkgenomic distancemultiple sequence alignmentphylogeny informationprotein complexprotein–protein interactionssequence coevolution analysis

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • Protein-protein interactions (PPIs) drive essential biological functions.
  • Coevolution analysis of Multiple Sequence Alignments (MSAs) predicts interacting residues in protein complexes.
  • Current methods for collecting protein complex MSAs (cpxMSA) lack standardization and efficiency.

Purpose of the Study:

  • To develop a novel and efficient pipeline for sensitive protein complex MSA (cpxMSA) generation.
  • To address the challenges in constructing cpxMSA for coevolution analysis.
  • To provide a flexible tool for users to analyze sequence coevolution based on custom cpxMSA.

Main Methods:

  • Developed the cpxDeepMSA algorithm for cpxMSA generation.
  • Integrated multiple protein monomer databases.
  • Employed three strategies—genomic distance, phylogeny, and STRING interaction network—to merge monomer MSAs.

Main Results:

  • The cpxDeepMSA approach successfully generates cpxMSA by combining monomer MSAs.
  • Utilizing multiple strategies prevents failure in cpxMSA construction caused by single-method limitations.
  • The method is anticipated to be a valuable high-throughput tool.

Conclusions:

  • cpxDeepMSA offers a robust solution for cpxMSA generation, enhancing coevolution analysis.
  • This tool facilitates accurate prediction of protein complex structures and inter-protein contacts.
  • The algorithm improves the analysis of biological sequence coevolution.