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Related Concept Videos

Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
Protein Families02:47

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 Families02:47

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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Gene Families01:57

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...
Gene Families01:57

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...

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Related Experiment Video

Updated: Jun 16, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Global sequence properties for superfamily prediction: a machine learning approach.

Richard J B Dobson1, Patricia B Munroe, Mark J Caulfield

  • 1The William Harvey Research Institute, Bart's and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, UK. richarddobson@gmail.com

Journal of Integrative Bioinformatics
|February 6, 2010
PubMed
Summary

Predicting protein function is challenging without experimental data. This study shows that machine learning models, enhanced by sequence enrichment, can improve protein superfamily classification accuracy.

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Last Updated: Jun 16, 2026

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Functional annotation of protein sequences is difficult without experimental data or clear sequence similarity.
  • Physicochemical and predicted structural attributes are key for inferring protein function.

Purpose of the Study:

  • To predict protein superfamily membership using machine learning.
  • To explore sequence enrichment techniques for improving model performance.

Main Methods:

  • Utilized physicochemical and predicted structural sequence attributes as input for machine learning.
  • Employed sequence enrichment to increase training data.
  • Applied LibSVM and AdaBoostM1 algorithms for classification.
  • Tested predictions on 24 and 49 protein superfamilies from the SCOP database.

Main Results:

  • Sequence enrichment significantly improved machine learning model performance.
  • Achieved 66.3% accuracy for 24 superfamilies and 55.6% for 49 superfamilies.
  • Confirmed that protein domains within superfamilies share global sequence properties.

Conclusions:

  • Machine learning models for SCOP database classification benefit from sequence enrichment.
  • These methods can aid in detecting distant protein relationships where function inference is challenging.
  • The study highlights the utility of sequence attributes and machine learning in protein classification.