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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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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Significance of Sequence Features in Classification of Protein-Protein Interactions Using Machine Learning.

Sini S Raj1, S S Vinod Chandra2

  • 1Machine Intelligence Research Lab, Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India. sinisraj@gmail.com.

The Protein Journal
|December 19, 2023
PubMed
Summary

Machine learning models predict human-virus protein-protein interactions using sequence features. Retaining high-dimensional features, rather than reducing them, improves model accuracy for understanding host-pathogen associations and developing antivirals.

Keywords:
Amino acidsDimensionality reductionFeature extractionLinear discriminant analysisMachine learningPrincipal component analysisProtein–Protein interactionsRandom forest

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Area of Science:

  • Computational biology
  • Virology
  • Bioinformatics

Background:

  • Protein-protein interactions (PPIs) are vital for viral entry and understanding human-virus associations.
  • Experimental methods for analyzing PPIs are labor-intensive; machine learning offers a predictive alternative.
  • Accurate prediction of PPIs aids in developing drugs and understanding viral pathogenesis.

Purpose of the Study:

  • To evaluate the impact of sequence feature dimensionality on machine learning models for predicting human-virus PPIs.
  • To highlight the importance of high-dimensional sequence features in capturing complex host-pathogen interactions.
  • To develop a more biologically meaningful classification model for virology and drug development.

Main Methods:

  • Extraction of high-dimensional sequence features: Amino Acid Composition (AAC), Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), and Pseudo-Amino Acid Composition (PAAC).
  • Creation of three datasets: one with all features and two with dimensionality-reduced features.
  • Training a random forest classifier on the three datasets to predict interacting and non-interacting proteins.

Main Results:

  • Dimensionality reduction, while yielding high accuracy, failed to capture the full complexity of protein-protein interactions.
  • Models retaining high-dimensional features demonstrated superior performance in classifying interacting human and viral proteins.
  • High-dimensional features are crucial for accurately modeling host-pathogen associations.

Conclusions:

  • Preserving high-dimensional sequence features is essential for robust prediction of human-virus protein-protein interactions.
  • This approach offers deeper insights into host-pathogen dynamics, crucial for antiviral drug development.
  • The proposed method provides a more realistic and comprehensive classification model for virological research.