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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.
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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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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Protein Families02:47

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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Related Experiment Video

Updated: Jul 24, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Large-scale predicting protein functions through heterogeneous feature fusion.

Rongtao Zheng1, Zhijian Huang1, Lei Deng1

  • 1School of Computer Science and Engineering, Central South University, 410000 Changsha, China.

Briefings in Bioinformatics
|July 4, 2023
PubMed
Summary

PredGO enhances protein function annotation by integrating AlphaFold structural predictions with non-structural data. This approach significantly improves the accuracy and coverage of Gene Ontology (GO) function predictions for proteins.

Keywords:
data miningfeature fusiongraph neural networkprotein function prediction

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Structural Biology

Background:

  • The rapid growth of protein sequence and structure data necessitates automated methods for function annotation.
  • Existing computational methods for protein function prediction have limitations in accuracy and coverage.
  • Experimental determination of protein functions is not scalable to the vast amount of available data.

Purpose of the Study:

  • To develop a large-scale computational approach for annotating Gene Ontology (GO) functions for proteins.
  • To leverage AlphaFold-predicted protein structures alongside non-structural clues for improved function prediction.
  • To create a robust and accurate method for automated protein function annotation.

Main Methods:

  • Utilized AlphaFold predicted three-dimensional structural information as a key feature.
  • Integrated non-structural clues such as sequence homology, protein-protein interactions, and gene co-expression.
  • Employed a pre-trained language model, geometric vector perceptrons, and attention mechanisms for feature extraction and fusion.

Main Results:

  • The PredGO approach demonstrated superior performance compared to state-of-the-art methods in predicting GO functions.
  • Achieved significant improvements in both coverage and accuracy of protein function predictions.
  • Successfully annotated over 205,000 human UniProt entries, with approximately 90% based on predicted structures.

Conclusions:

  • PredGO effectively integrates structural and non-structural data for accurate and comprehensive protein function annotation.
  • The method addresses the limitations of existing approaches, offering a scalable solution for large-scale annotation.
  • A webserver and database are publicly available, facilitating broader application of the PredGO tool.