## 4 Discussion
These \"seeds\", when expanded to their neighborhood and clustered, allow reconstruction of the nearby P-P and P-L interface. In FdII, which has more accordant monomers with the 3D Gauss function than FdI, only one discordant cluster is registered, originating from K30 (class \(B_{2}\)). This residue has similar physical location to V56 in respect to the ligand but exhibits opposite status (hydrophobicity deficiency rather than excess). Due to different complex formation, K30 is located in between the binding pocket and the P-P interface, capturing residues from both of these regions during the prediction experiment. FOD-based classification of discordant (\(RD\geq 0.5\)) and non-globular proteins and use of this information to determine their interaction areas is also possible, as shown on the example of Rad50 domain, where the protein-protein interface is resolved almost perfectly by using \(D\) class data. Interestingly, when the region closer to this interface is inspected (Pfam coiled-coil zn hook, residues 423-474), it becomes clear that as one limits the calculation to the vicinity of P-P area, the high peak in the central \(O\) fragment starts to be \"followed\" by raising \(T\) distribution, causing residues to leave class \(D\) for class \(C\) (Figure 11). Similar, but weaker phenomenon can be observed in whole 1L8D dataset. This suggests that hydrophobicity may play a role in initial formation of contact between Rad50 units even though the dimer is not hydrophobically stable. It partially fits FOD expectation where accordant complex is made from discordant monomers. It is worth mentioning here that most structures we worked with so far subscribe to \"monomers accordant, dimer accordant\" and \"monomers discordant, dimer discordant\" themes. Classification of FOD residues proposed in this paper has high flexibility, allowing choosing of class level suitable for given structure. Higher level (i.e., 3) means narrower search but focused on the most outstanding data, while lower level (i.e., 1) can be either ignored or included in further analysis, depending on available number of members of significant classes. Alternatively, residues from class \(Z\) can be used as a \"support\", for example (if there is a lot of them) instead of the nearest neighbors. One must remember however that class \(Z\) members will be mostly located between core and surface. Based on the results, in case of FdI and FdII it is not recommended to perform the search on level 1 as it leads to high count of false positive results. The presented contact prediction method is not without limitations. One of them is intrinsic to the model itself: current algorithm for the orientation of the molecule (for \(T\) distribution calculation) is heavily influenced by effective atom outliers. Sometimes a single outstanding residue (i.e., at the termini) may change the size of the 3D Gauss capsule, returning different results between almost identical chains. Second problem is related to the fact that \(D\) and \(B\) classes are populated with residues from opposite sides of the \(T_{2}\) threshold. This makes it harder to detect a P-P interface which spans elongated patches on the surface of the monomers which may not exhibit the same hydrophobic quality (i.e., they belong to class \(Z\)). Finally, given how P-P and P-L interaction areas may be pointed to by both \(D\) and \(B\) classes, there is a question how to discern them. Since our approach is an ab initio method, one way to conduct it would be to employ another ab initio method, for example a cavity detector based on the geometric shape of the molecular surface. There are already tools capable of handling this task using various techniques, convex hull being one among them [62; 63].