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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...

Protein and Protein Structure

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...

Protein and Protein Structures

Protein and Protein Structures

Conservation of Protein Domains

Conservation of Protein Domains

Protein Families

Protein Families

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...

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Updated: Mar 3, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Core column prediction for protein multiple sequence alignments.

Dan DeBlasio^1,2, John Kececioglu¹

¹Department of Computer Science, The University of Arizona, Tucson, AZ 85721 USA.

Algorithms for Molecular Biology : AMB

|April 25, 2017

Summary

This summary is machine-generated.

We developed a novel predictor for column coreness in protein multiple sequence alignments. This tool accurately estimates alignment accuracy by identifying reliable columns, outperforming existing methods.

Keywords:

Accuracy estimation Alignment accuracy Core blocks Machine learning Multiple sequence alignment Parameter advising Regression

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

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

Published on: August 16, 2017

Area of Science:

Computational Biology
Bioinformatics
Structural Biology

Background:

Column coreness in protein multiple sequence alignments (MSAs) quantifies the reliability of alignment columns based on reference alignments.
Reference alignments use 3D structures to identify confident core columns, but this is unavailable for practical alignments.
Current methods typically measure MSA accuracy only against these core columns.

Purpose of the Study:

To develop the first predictor for column coreness in protein MSAs.
To enable accurate estimation of MSA accuracy when no reference alignment is known.
To improve the selection of alignment parameters for enhanced accuracy.

Main Methods:

Developed a novel coreness predictor using a nearest-neighbor classification approach.
Transformed nearest-neighbor distances into coreness predictions via a regression function.
Optimized a distance function using large-scale linear programming.

Main Results:

The developed coreness predictor accurately identifies reliable columns in computed MSAs.
Applied to parameter advising, the predictor significantly outperforms existing column-confidence estimators.
Achieved a substantial boost in overall protein multiple sequence alignment accuracy.

Conclusions:

The novel coreness predictor enables reliable estimation of MSA accuracy without a reference.
This method offers a significant improvement for parameter advising in protein alignment.
The approach provides a robust tool for assessing and enhancing the quality of computed MSAs.