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Mutations

Mutations

Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
Chromosomal Alterations Are Large-Scale Mutations
While point mutations are changes in a single nucleotide in...

Mutations

Mutations

Mutations

Mutations

Point and Frameshift Mutations

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...

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...

From DNA to Protein

From DNA to Protein

The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...

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Related Experiment Video

Updated: Jun 13, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Linear predictive coding representation of correlated mutation for protein sequence alignment.

Chan-seok Jeong¹, Dongsup Kim

¹Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, Korea.

BMC Bioinformatics

|April 22, 2010

Summary

This summary is machine-generated.

This study introduces a novel CM profile method to represent correlated mutations for improved protein sequence alignment. Combining CM profiles with sequence profiles significantly enhances alignment quality, outperforming secondary structure predictions.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Published on: July 14, 2015

An Integrated Approach for Microprotein Identification and Sequence Analysis

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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:

Bioinformatics
Computational Biology
Structural Bioinformatics

Background:

Traditional sequence alignment methods primarily utilize conservation information.
Correlated mutation (CM) information, crucial for understanding evolutionary context, is often overlooked in sequence alignment.
A gap exists in general methods for representing and integrating CM into sequence alignment.

Purpose of the Study:

To develop a novel method, CM profile, for representing correlated mutation information.
To integrate CM profile with conventional sequence profiles to enhance protein sequence alignment quality.
To evaluate the effectiveness of CM profile in improving profile-profile alignment, particularly for distantly related proteins.

Main Methods:

Developed a CM profile method using linear predictive coding to represent correlated mutations as spectral features.
Combined the novel CM profile with conventional sequence profiles for profile-profile alignment.
Evaluated alignment improvements with and without predicted secondary structure information.

Main Results:

CM profile significantly improved profile-profile alignment for distantly related protein pairs.
At the superfamily level, combining CM profile with sequence profile yielded a 9.5% improvement, surpassing secondary structure predictions (6.0%).
Using both CM profile and secondary structure information resulted in a substantial 13.9% improvement in alignment quality, preserving coevolution and contacts.

Conclusions:

The novel CM profile effectively represents correlated mutation information in a parallel format for sequence alignment.
CM profile integration with sequence profiles offers superior alignment quality compared to predicted secondary structure information.
This method is beneficial for protein structure prediction tasks like target-template alignment and has broad applicability in bioinformatics.