Design of inhibitors of Ras-Raf interaction using a computational combinatorial algorithm

Drugs that inhibit important protein-protein interactions are hard to find either by screening or rational design, at least so far. Most drugs on the market that target proteins today are therefore aimed at well-defined bindi ng pockets in proteins. While computer-aided design is widely used to facil itate the drug discovery process for binding pockets, its application to th e design of inhibitors that target the protein surface initially seems to b e limited because of the increased complexity of the task. Previously, we h ad started to develop a computational combinatorial design approach based o n the well-known 'multiple copy simultaneous search' (MCSS) procedure to ta ckle this problem. In order to identify sequence patterns of potential inhi bitor peptides, a three-step procedure is employed: first, using MCSS, the locations of specific functional groups on the protein surface are identifi ed; second, after constructing the peptide main chain based on the location of favorite locations of N-methylacetamide groups, functional groups corre sponding to amino acid side chains are selected and connected to the main c hain C-alpha atoms; finally, the peptides generated in the second step are aligned and probabilities of amino acids at each position are calculated fr om the alignment scheme. Sequence patterns of potential inhibitors are dete rmined based on the propensities of amino acids at each C-alpha position. H ere we report the optimization of inhibitor peptides using the sequence pat terns determined by our method. Several short peptides derived from our pre diction inhibit the Ras-Raf association in vitro in ELISA competition assay s, radioassays and biosensor-based assays, demonstrating the feasibility of our approach. Consequently, our method provides an important step towards the development of novel anti-Ras agents and the structure-based design of inhibitors of protein-protein interactions.