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Related Concept Videos

Mutations

Mutations

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...

Global Climate Change

Global Climate Change

Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.

Viral Mutations

Viral Mutations

A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

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Related Experiment Video

Updated: Jan 21, 2026

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

GEMME: A Simple and Fast Global Epistatic Model Predicting Mutational Effects.

Elodie Laine¹, Yasaman Karami^1,2, Alessandra Carbone^1,3

¹Sorbonne Université, CNRS, IBPS, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative (LCQB), 75005 Paris, France.

Molecular Biology and Evolution

|August 14, 2019

Summary

Predicting protein mutations is crucial. The new Global Epistatic Model for predicting Mutational Effects (GEMME) method rapidly and accurately maps these changes by analyzing evolutionary history, outperforming existing tools.

Keywords:

conservation epistasis evolution mutation mutational landscape protein viral sequence

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Last Updated: Jan 21, 2026

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Published on: November 27, 2019

Area of Science:

Computational biology
Protein engineering
Genomics

Background:

Accurate protein mutational landscape description is vital for biology, bioengineering, and medicine.
Current methods leverage genomic data and sequence dependencies but are often time-consuming.
Modeling inter-site dependencies is key to understanding mutation effects.

Purpose of the Study:

To introduce GEMME (Global Epistatic Model for predicting Mutational Effects), a novel, fast computational method.
To predict protein mutational outcomes by explicitly modeling evolutionary history.
To provide a computationally efficient tool for generating full mutational landscapes.

Main Methods:

GEMME models the evolutionary history of natural protein sequences.
It accounts for all sequence positions when estimating mutation effects.
The method utilizes a small set of interpretable biological parameters.

Main Results:

GEMME demonstrates comparable or superior performance against 50 high- and low-throughput mutational experiments.
It accurately predicts mutational landscapes across diverse protein families, including viral and conserved ones.
The method generates complete mutational landscapes within minutes from an input alignment.

Conclusions:

GEMME offers a significant advancement in the speed and accuracy of predicting protein mutational effects.
Its ability to model evolutionary history provides a more comprehensive understanding of mutation impacts.
The freely available GEMME package and webserver facilitate broader research applications.