BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations

Abstract

The ability of proteins to establish highly selective interactions with a variety of (macro)molecular partners is a crucial prerequisite to the realization of their biological functions. The availability of computational tools to evaluate the impact of mutations on protein-protein binding can therefore be valuable in a wide range of industrial and biomedical applications, and help rationalize the consequences of non-synonymous single-nucleotide polymorphisms. BeAtMuSiC (http://babylone.ulb.ac.be/beatmusic) is a coarse-grained predictor of the changes in binding free energy induced by point mutations. It relies on a set of statistical potentials derived from known protein structures, and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. The BeAtMuSiC server requires as input the structure of the protein-protein complex, and gives the possibility to assess rapidly all possible mutations in a protein chain or at the interface, with predictive performances that are in line with the best current methodologies.

Figure 1.

Schematic representation of the binding and folding free energies. and are the folding free energies of the two partners of the interaction. is the folding free energy of the complex as a whole. In the first binding model, the complex is formed from the association of two individually folded partners, and the binding free energy is . In the second binding model, the proteins are unable to fold independently, and the binding free energy () is thus equal to the folding free energy of the complex (). The figure was made using PyMOL.

Figure 2.

Correlation between predicted and measured changes in binding free energies in the SKEMPI data set. (Black circle) Main data set. (Blue cross) 10% outliers. (Red triangle) Mutations of the lysine at position I15 in the BPTI–BT complex (PDB: 2FTL).

Figure 3.

Structure of the complex formed by BPTI (yellow) and bovine BT (blue) (PDB: 2FTL). The lysine residue in position 15 of BPTI is depicted in magenta. The figure was made using PyMOL.

Figure 4.

Kendall’s τ rank correlation coefficient between predictions and experiments, during the round of the CAPRI experiment (http://www.ebi.ac.uk/msd-srv/capri/round26). The results of our method are depicted in black, and those of other participating methods in gray. Groups that did not submit predictions for the complete set of mutations are not considered here. The symbol ‘X’ is used when a group submitted a full set of predictions for one target but not for the other. (a) Target 55: hemagglutinin-HB36.4. (b) Target 56: hemagglutinin-HB80.3. A detailed analysis of the results of this experiment, along with a description of the different prediction methods, will be reported elsewhere (Moretti et al., manuscript submitted).

Figure 5.

Example output of the BeAtMuSiC server.