Structural Consequences of Disease‐Related Mutations for Protein–Protein Interactions


Mutation of a single amino acid in a protein often has consequences on the interaction with other proteins, which may affect other interaction networks and pathways and ultimately lead to pathological phenotypes. A detailed structural analysis of these altered protein–protein complexes is essential to interpret the impact of a given mutation at the molecular level, which may facilitate intervention with therapeutic purposes. Given current limitations in the structural coverage of the human interactome, computational docking is emerging as a complementary source of information. Structural analysis can help to locate a given mutation at a protein–protein interface, but further characterisation of its impact on binding affinity is needed for a full interpretation. The integration of computational docking methods and energy‐based descriptors is facilitating the characterisation of an increasing number of disease‐related mutations, thus improving our understanding of the consequences of such mutations at the phenotypic level.

Key Concepts

  • Protein–protein interactions are key to understand disease at the molecular level.
  • Disease‐related mutations can have significant structural and energetic impact on protein–protein interactions.
  • The 3D structure of a complex is essential to interpret the functional impact of a mutation.
  • Computational docking can provide structural models for protein–protein interactions with no available structure.
  • In addition to structural data, further energetic description is needed to fully interpret the impact of a mutation.
  • Hot‐spot interface residues are causing the greater impact on a protein–protein interaction when mutated.
  • Altered protein–protein interactions are potentially suitable drug targets for therapeutic purposes.

Keywords: protein–protein interactions; single amino acid variants; structural bioinformatics; computational docking; interface prediction; binding affinity change

Figure 1. Possible effects of disease‐related mutations on protein–protein interactions (PPIs). Pie charts show the results from a recent experimental study on 197 disease‐related mutations and 47 nondisease variants (Sahni et al., ), showing no effects on PPIs (green), dramatic effect on all PPIs (red) and effect on specific PPIs (blue). Sahni et al. (). Reproduced with permission of Elsevier.
Figure 2. (a) Classification of ETFA residues as buried, exposed or interface (core and rim) based on the 3D structure of its complex with ETFB (PDB 1EFV). Upper pie chart shows the distribution of residues in these regions for a set of disease‐related proteins and their interactions (Navío et al., ). Bottom pie charts show the distribution of disease‐related and neutral mutations for the same proteins in the mentioned structural regions. (b) Structure‐based analysis of disease‐related mutations in HBB and their impact on its interaction with different partners (HBZ, HBA and HP). Affected interactions by each mutation are shown by lines, with HBB rim and core interface residues based on the corresponding complex structure. Adapted from Navío et al. ().
Figure 3. (a) Scheme of pyDock docking protocol. (b) Docking‐based prediction of interface residues in HADHA for different interactions for which there is no available structure (Navío et al., ). A recent structure of HADHA bound to HADHB (PDB 6DV2) is also shown. Adapted from Navío et al. ().
Figure 4. (a) Complex structure of human prolactin (hPRL), represented in white ribbon, bound to its receptor (hPRLr), in gold ribbon (PDB 3MZG). In CPK are shown two interface mutations with different impacts on binding free energy. (b) Distribution of experimental binding affinity changes (ΔΔGbind) of mutations in SKEMPI from complexes of protein–protein docking benchmark 5.0.
Figure 5. Crystal structure of WT FOXP3 dimeric protein (monomers in blue and gold ribbon) in complex with nuclear factor NFAT1 (green surface) and DNA (cyan stick) (PDB code 3QRF), showing F367 residue in both FOXP3 monomers (transparent CPK), whose mutation to valine is linked to IPEX syndrome. Side chains of aromatic residues at FOXP3 dimeric interface are also shown.


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Rosell, Mireia, and Fernández‐Recio, Juan(May 2020) Structural Consequences of Disease‐Related Mutations for Protein–Protein Interactions. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0028975]