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Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

Protein Families

Protein Families

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 locations, protein...

Protein Families

Protein Families

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 locations, protein...

Conservation of Protein Domains

Conservation of Protein Domains

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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

Gene Families

Gene Families

Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
Occasionally these regions can be adapted to take on new roles within the organism, becoming novel genes...

Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Related Experiment Video

Updated: May 26, 2026

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Out-of-distribution generalization enhances protein function annotation for low-homology sequences.

Yiwei Fu¹, Jiaxiao Chen², Haoyu Lin²

¹School of Mathematical Sciences, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing 100871, China.

Briefings in Bioinformatics

|May 25, 2026

Summary

This summary is machine-generated.

ProteinScore enhances protein function prediction for diverse sequences using a graph transformer. This approach improves generalization to novel proteins, aiding disease research and drug discovery.

Keywords:

contrastive learning graph transformer out-of-distribution protein function prediction subgraph generator

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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

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Last Updated: May 26, 2026

Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

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Published on: April 11, 2019

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

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Published on: August 15, 2019

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Area of Science:

Computational biology
Bioinformatics
Protein science

Background:

Understanding protein function is crucial for disease research and drug discovery.
Current methods struggle with novel, low-homology protein sequences (out-of-distribution generalization).

Purpose of the Study:

To develop an improved protein function prediction method for out-of-distribution settings.
To enhance generalization to diverse and low-homology protein sequences.

Main Methods:

Developed ProteinScore, a graph transformer approach.
Integrated a label-invariant variational subgraph generator and self-supervised contrastive learning.
Identified and highlighted informative protein substructures while filtering redundant features.

Main Results:

ProteinScore demonstrated superior performance over baselines on diverse and low-homology sequences.
The method showed effective generalization to downstream tasks like drug-target interaction and subcellular localization prediction.
ProteinScore provided biologically meaningful interpretability by identifying binding sites.

Conclusions:

ProteinScore significantly improves out-of-distribution protein function prediction.
The approach offers reliable interpretability and broad applicability in biological research and drug discovery.