News from the protein mutability landscape.

By Maximilian Hecht, Yana Bromberg, Burkhard Rost. 2013

80 citations recorded by the source corpus.

Some mutations of protein residues matter more than others, and these are often conserved evolutionarily. The explosion of deep sequencing and genotyping increasingly requires the distinction between effect and neutral variants. The simplest approach predicts all mutations of conserved residues to have an effect; however, this works poorly, at best. Many computational tools that are optimized to predict the impact of point mutations provide more detail. Here, we expand the perspective from the view of single variants to the level of sketching the entire mutability landscape. This landscape is defined by the impact of substituting every residue at each position in a protein by each of the 19 non-native amino acids. We review some of the powerful conclusions about protein function, stability and their robustness to mutation that can be drawn from such an analysis. Large-scale experimental and computational mutagenesis experiments are increasingly furthering our understanding of protein function and of the genotype-phenotype associations. We also discuss how these can be used to improve predictions of protein function and pathogenicity of missense variants.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Á. Tóth-Petróczy
2. Liat Rockah-Shmuel
3. Dan S. Tawfik

In Molecular Biology

1. Á. Tóth-Petróczy
2. Liat Rockah-Shmuel
3. Dan S. Tawfik

Citations in the corpus

• Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations (2015, background): “Systematic experimental mappings of the effects of mutations within one given gene/protein are therefore crucial for understanding protein evolution, as well as an attractive resource for improving predictions [15, 16] and for refining protein design algorithms [17].”
• PredictProtein—an open resource for online prediction of protein structural and functional features (2014, unknown): “Other views present additional annotations and predictions, e.g. functional landscapes of the effect of point mutations (SNAP2, Figure 1C), predicted GO terms (metastudent, Figure 1D) or subcellular localization (LocTree3, Figure 1E).”