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Related Concept Videos

Mutations in Microorganisms01:18

Mutations in Microorganisms

Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
Mutations01:35

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...
Mutations01:39

Mutations

Overview
Mutations01:39

Mutations

Overview
Mismatch Repair01:20

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
Mismatch Repair01:36

Mismatch Repair

Overview

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Related Experiment Video

Updated: May 9, 2026

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

News from the protein mutability landscape.

Maximilian Hecht1, Yana Bromberg, Burkhard Rost

  • 1Department of Bioinformatics and Computational Biology I12, Technische Universität München, Boltzmannstrasse 3, 85748 Garching, Germany.

Journal of Molecular Biology
|July 31, 2013
PubMed
Summary

Understanding protein mutations is key. This study introduces the concept of a mutability landscape to better predict the impact of genetic variants on protein function and stability.

Keywords:
3DG-protein-coupled receptorGPCRPDBProtein Data BankSAASSIFTSNAPSNPSNP effectsalanine scanningcomplete single mutagenesisexome-wide mutagenesishMC4Rhuman melanocortin 4 receptorin silico mutagenesisnon-synonymous SNPnsSNPscreening for non-acceptable polymorphismssingle nucleotide polymorphismsingle-amino-acid substitutionsorting intolerant from tolerantthree-dimensional

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Last Updated: May 9, 2026

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Gene-targeted Random Mutagenesis to Select Heterochromatin-destabilizing Proteasome Mutants in Fission Yeast
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Gene-targeted Random Mutagenesis to Select Heterochromatin-destabilizing Proteasome Mutants in Fission Yeast

Published on: May 15, 2018

Area of Science:

  • Biochemistry and Molecular Biology
  • Computational Biology and Bioinformatics
  • Genetics and Genomics

Background:

  • Distinguishing between functionally significant and neutral protein variants is crucial due to advances in deep sequencing and genotyping.
  • Simple methods predicting variant effects based on residue conservation are often inaccurate.
  • Existing computational tools offer detailed predictions for single point mutations.

Purpose of the Study:

  • To expand the analysis of protein variants from single mutations to a comprehensive mutability landscape.
  • To define the mutability landscape by assessing the impact of substituting each amino acid at every protein position.
  • To explore how mutability landscapes inform protein function, stability, and robustness to mutations.

Main Methods:

  • Reviewing conclusions drawn from mutability landscape analysis.
  • Discussing the integration of large-scale experimental and computational mutagenesis data.
  • Examining the application of these approaches to enhance predictions of protein function and variant pathogenicity.

Main Results:

  • Mutability landscape analysis provides powerful insights into protein properties and their response to genetic alterations.
  • Large-scale mutagenesis experiments significantly advance the understanding of protein function and genotype-phenotype relationships.
  • The mutability landscape framework aids in improving predictions of protein function and the pathogenicity of missense variants.

Conclusions:

  • The mutability landscape offers a more holistic view of protein variant impact compared to single-site predictions.
  • Integrating experimental and computational mutagenesis data is essential for robust predictions.
  • This approach enhances the ability to predict protein function and identify disease-causing variants.