We further investigated the position of the affinity-improving mutations in the coding sequence of antibody and other proteins. the same basic strategy under selection pressure to maintain interactions. Additionally, our data indicate that classical simulation techniques incorporating the evolutionary information derived from antibody MLN120B affinity maturation can be utilized as a powerful tool to improve the binding affinity of protein-protein complex with a high accuracy. (1) provided the first visualization of the maturation of antibodies to protein. By directly comparing the structures of four antibodies bound to the same site on hen egg white lysozyme (HEL) at different MLN120B stages of affinity maturation, they revealed that antibody affinity maturation is the result of small structural changes, mostly confined to the periphery of the antibody-combining site. Moreover, comparison of the germline to mature sequences in a structural region-dependent fashion allows insights into the methods that nature uses to mature antibodies (Abs)3 during the somatic hypermutation process. Tomlinson (8) have previously analyzed the diversity of amino acids at specific positions in the germline and mature Ab sequences. They found that the frequency of somatic hypermutation and the diversity of the germline sequences are highest in the CDRs. Rather than focus on the mutation frequencies, Clark (9) examined the type of mutation and its functional implications deduced from the location in the structure. Their results indicated that residue type changes during the somatic hypermutation process were significant and had underlying functional rationales. In the present study, several strategies incorporating the evolutionary information derived from antibody affinity maturation with classical simulation techniques was used to investigate whether the evolution of protein-protein interface acts in a similar way as antibody affinity maturation. If the same evolutionary mechanism is used in all the protein-protein interfaces, antibody evolutionary information would help to improve the prediction success rate of the classical simulation method in affinity enhancement of other protein-protein complexes. Our design strategies were evaluated in four different types of protein-protein complexes. It was interesting to find that even in other protein-protein complexes besides antibody-antigen complexes, one of the strategies yields exceptional high success rates ( 57%) for single mutations from wild type. We further investigated the position of the affinity-improving mutations in the coding sequence of antibody and other proteins. Our data suggest that the evolution of distinct protein-protein interfaces may use the same basic mechanism under selection pressure to maintain interactions. MLN120B The present study also demonstrates the generality of our design strategy and suggests that it may be used to accurately predict affinity improvement of any protein. EXPERIMENTAL PROCEDURES Protein Simulation Crystal structures of MLN120B the target proteins complexed with their respective binding partners were from the Protein Data Bank. Most crystallographic water molecules and ions were removed, except for water molecules bridging the binding interface or buried away from bulk solvent. Hydrogen atom positions were assigned using the Biopolymer module of Insight II (Accelrys). The computational mutation was carried out on the target protein. Docking was performed using MCSA for random generation of a maximum of sixty structures through the Affinity module of Insight II (CVFF pressure field) (33). The resulting set of structures was evaluated for total energy and how close each was to the crystal structure based on a heavy-atom RMSD of the binding partner crucial amino acids for interaction. Then the lowest energy complexes presenting lower RMSD were selected for the binding free energy calculations. Briefly, molecular dynamics (MD) simulations were done using the MMP10 CHARMM program (34) with the PARAM22 all-atom parameter set.