Protein–Protein Interactions and Genetic Disease

Abstract

The advent of high‐throughput experiments to measure protein–protein interactions has created a flood of proteomic information parallel to the influx of data caused by the advent of next‐generation sequencing technologies. The creation of whole organism protein interaction maps has opened new avenues of predicting genetic disease. Advances in network science now allow the association of genes with disease directly from the characteristics of the protein map without reference to the characteristics of the gene itself. However, none of these techniques has reached the ‘black box’ level and require careful consideration of the systematic errors in both the underlying experimental data and the computational methods to give reliable results. Here, we review the main methods to characterise protein interactions in vitro and in vivo, the methods by which protein networks are constructed and the characteristics of the major protein interaction databases, and the techniques used to predict the functional impact of mutations on protein interaction networks.

Key Concepts

  • Protein–protein interactions are the driving force for cellular responses and disruption of protein–protein interfaces.
  • The sequence conservation of functionally important residues across protein interfaces allows the prediction of disease‐associated mutations.
  • Disease‐associated mutations often affect protein–protein binding affinities that can be measured with high accuracy in vitro using purified proteins by a variety of biophysical techniques.
  • High‐throughput techniques to measure protein interactions in vivo are prone to both high false‐positive and high false‐negative rates.
  • Each method of identifying protein interactions has systematic errors associated with it that can be partly corrected by cross validating the results with two techniques.
  • Functional associations between genes can be inferred by bioinformatic approaches, but the results do not necessarily imply a physical interaction between proteins.
  • Protein–protein interaction databases may include both complexes between proteins and purely functional associations. This distinction must be kept in mind when analysing results from protein–protein interaction databases.
  • Disease‐associated genes tend to be clustered together in protein–protein interaction networks. It is possible to predict new genes for a disease by a random walk across the protein interaction network from genes already known to be associated with the disease.
  • Currently, protein–protein interaction surfaces are mostly considered undruggable by small molecules. This belief is changing with new concepts in drug design.

Keywords: protein–protein interaction; morbidity; protein networks; yeast two hybrid; affinity copurification; systems biology; network medicine; biological database; druggability; binding affinity

Figure 1. The yeast two‐hybrid assay. (a) The yeast two‐hybrid assay begins with the construction of a prey plasmid library. Each prey plasmid encodes a protein fused to the transcription factor activation domain along with a selection marker to detect the successful incorporation of the plasmid into the cell. A bait plasmid is also constructed encoding the protein of interest fused to the DNA (deoxyribonucleic acid) binding domain of the transcription factor along with a second orthogonal selection marker. (b) The yeast cells are then permeabilised to allow entry of the plasmid and transformation of the yeast genome. (c) Once transformed, the yeasts are grown in media‐deficient pathway. (d) Binding of the prey protein to the bait protein brings the activation domain into proximity of the reporter gene and activates transcription. (e) Colonies showing transcription of the reporter gene are selected. (f) The plasmids from the active colonies are extracted and (g) the DNA corresponding to the prey protein sequenced.
Figure 2. Integration of different data sets into a protein–protein network. Accuracy can be increased by considering only the strict intersection of the data sets where a positive PPI (protein–protein interaction) exists in each data set. Alternatively, the coverage can be extended by considering the union of the data sets where a PPI is considered to exist if it is found in either data set. Weighted integration counts only the PPIs in each data set considered to be the most reliable through the consultation of an outside gold standard database.
Figure 3. Modified screenshot of an example query from the STRING database. Example of a protein network from the STRING database using the KRas protein, an oncogene implicated in the development of many cancers. The colour of the lines connecting the protein nodes indicates the particular lines of evidence used in establishing a functional association whereas the distance between the nodes is a measure of the confidence of the interaction as established by the Bayesian scoring system. Predicted GO pathways are also available (not shown).
Figure 4. Comparison of PPI interfaces and small molecule binding pockets. (a) A large and shallow PPI with three potential hot spots for small molecule binding. (b) Enzyme binding pocket. Note the smaller size and greater depth of the enzyme binding pocket in comparison with the PPI interface. Adapted from Zerbe (2012) © American Chemical Society.
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Brender, Jeffrey R, and Zhang, Yang(Oct 2017) Protein–Protein Interactions and Genetic Disease. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0026856]