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Related Concept Videos

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-protein Interfaces02:04

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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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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.
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Conservation of Protein Domains Over Different Proteins02:26

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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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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Protein Complexes with Interchangeable Parts01:57

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SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features.

A J Preto1, Irina S Moreira2

  • 1CNC-Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal.

International Journal of Molecular Sciences
|October 6, 2020
PubMed
Summary

SPOTONE is a new machine learning (ML) predictor that identifies protein hot spots (HS) using only amino acid sequences. This sequence-based approach offers a more accessible tool for the scientific community, complementing structure-based methods.

Keywords:
big-datahot-spotsmachine learningprotein–protein complexesstructural biology

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Protein hot spots (HS) are crucial amino acids involved in ligand binding and protein-protein interactions.
  • Current prediction methods predominantly rely on 3D protein structures, limiting their applicability due to the scarcity of structural data compared to sequence data.

Purpose of the Study:

  • To develop a machine learning (ML) predictor, SPOTONE, for identifying protein hot spots (HS) using only sequence-based features.
  • To provide a more broadly applicable tool for the scientific community by leveraging the abundance of protein sequence data.

Main Methods:

  • Development of a novel ML algorithm (SPOTONE) that utilizes sequence-only features for HS prediction.
  • Rigorous evaluation of the algorithm on an independent testing set to assess its performance.

Main Results:

  • SPOTONE achieved high performance metrics, including an accuracy of 0.82, AUROC of 0.83, precision of 0.91, recall of 0.82, and F1-score of 0.85 on the independent test set.
  • The algorithm demonstrates the feasibility and effectiveness of sequence-based prediction for protein hot spots.

Conclusions:

  • SPOTONE offers an accurate and efficient method for predicting protein hot spots directly from amino acid sequences.
  • The developed webserver provides a user-friendly, free resource for researchers to predict HS, enhancing accessibility and utility in biological research.