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Related Concept Videos

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...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...

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Related Experiment Video

Updated: Jul 17, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
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Protein family classification with partial least squares.

Stephen O Opiyo1, Etsuko N Moriyama

  • 1Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583-0915, USA.

Journal of Proteome Research
|February 3, 2007
PubMed
Summary

Accurate protein function prediction requires effective classification methods. Alignment-free partial least-squares classifiers maintain performance with limited protein samples, unlike profile hidden Markov models and PSI-BLAST.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Protein Science

Background:

  • Protein function prediction is crucial for understanding biological systems.
  • The performance of protein classification methods is sensitive to the quantity of available training data.
  • Divergent protein families often present challenges due to limited sample sizes.

Purpose of the Study:

  • To evaluate the robustness of different protein classification methods under data scarcity.
  • To compare the performance of alignment-free methods against traditional sequence alignment-based methods in challenging scenarios.

Main Methods:

  • Utilized partial least-squares classifiers, profile hidden Markov models, and PSI-BLAST for protein function prediction.
  • Assessed classifier performance using limited and fragmented protein sequence datasets representative of divergent families.

Main Results:

  • Profile hidden Markov models and PSI-BLAST showed a significant decrease in predictive performance with limited data.
  • Alignment-free partial least-squares classifiers demonstrated consistent performance even with small and fragmented protein sequence datasets.

Conclusions:

  • Alignment-free methods, specifically partial least-squares, offer a more reliable approach for protein function prediction when training data is scarce.
  • The findings suggest that alignment-free strategies are advantageous for classifying proteins in divergent families or identifying fragmented sequences.