Exploring computational lead optimisation with affinity constants obtained by surface plasmon resonance for the interaction of PorA epitope peptides with antibody against Neisseria meningitidis.
LUDI is a program used for de novo structure-based design of ligands and can predict binding of ligands quantitatively using a scoring function. Here we evaluate LUDI in a lead optimisation study with ligands for the antibody MN12H2, that has been raised against outer membrane protein PorA epitope P1.16 of Neisseria meningitidis. The ligands were synthetic peptides that are derived from the smallest binding epitope (182)DTNNN(186). LUDI's fragment building rules are used for the proposal of new peptide-ligands for MN12H2 and were focused on replacements of Asp(186) in the epitope. Accordingly, a series of peptides was synthesised with isosteric mutations. The interaction of the peptides with MN12H2 was analysed with a surface plasmon resonance competition assay yielding equilibrium binding constants in solution (K(S)). The binding affinity seems to be largely determined by entropy, and the side chain of Asn(186) is sensitive for charge, inversion, hydrophobicity and size. Head-to-tail cyclisation of the peptide in a nine-amino-acid ring gives little reduction in affinity. It is concluded that the scoring function of LUDI does not help in optimisation of the peptide lead for MN12H2 binding. Other more elaborate molecular mechanics calculations show similar results. This implies that our current knowledge of molecular recognition is insufficient for explaining a case of peptide-protein binding, where the design process requires subtle changes in structure (from lead finding to lead optimisation).