## 3 Experimental Section  
### Docking  
The hypothesis underlying docking predictions is that the native complex structure is the state with the lowest free energy available to the system. There are several different approaches on how to develop docking algorithms but the common, basic idea is to first do a sampling of possible conformations followed by a scoring of these conformations. Scoring means to analyze the putative complex structures generated in the first step with regard to chemical, physical and knowledge based aspects. Selecting suitable scoring terms and weighting them in an appropriate way is one of the great challenges in docking. The aim is to rank all putative structures in a way that most of the native-like structures are found in the top part of the ranked output.  
In our work we are using HADDOCK2.0, which was developed by [PERSON] _et al_. [8]. There are two main reasons for this choice: first, HADDOCK introduces the possibility to drive the sampling step by known data about the contact regions of the interacting proteins. These data can for example originate from nuclear magnetic resonance chemical shift perturbation data [10] or from mutagenesis experiments. This information is introduced as ambiguous interaction restraints (AIRs). By this it is possible to define on all docking partners the residues, which are supposed to be in the interface region of the complex. The docking is then driven by a force that pulls the defined regions together. This mechanism scales down the search space considerably and makes it possible to get reasonable results in a passable time. The other main advantage of HADDOCK is that main and side-chain flexibility can be incorporated into the docking process. In this process a three step docking procedure is used. In the first step a rigid body energy minimization is performed followed by semi-flexible simulated annealing in torsion angle space. In the last stage an optional refinement in explicit solvent, e.g. water, can be done. Since in the current application many different complex structures had to be computed this quite time consuming step was omitted. The ranking of the complex structures is based on a score calculated from \(E_{\mathrm{vdW}}\), \(E_{\mathrm{elec}}\), \(E_{\mathrm{AIR}}\), \(BSA\), and \(E_{\mathrm{desolv}}\), where BSA is the buried surface area and \(E_{\mathrm{desolv}}\) describes the desolvation energy. From each docking run the 10 best structures in terms of this score were selected for further analysis. As mentioned before docking runs were performed for native and non native interactions. In the next step the selected complex structures of each docking run were used to discriminate between the native and non native complexes. To base the decisions on something that resembles real physical energies the interaction energies \(E_{\mathrm{int}}=E_{\mathrm{vdW}}+E_{\mathrm{elec}}\) were calculated for the interface of each of the selected complex structures. From these individual energies the average interaction energy was obtained for the 10 selected structures of each docking run and these average interaction energies were used to discriminate between native and non native interactions.