PepCrawler is a fast RRT-based algorithm for high-resolution refinement and binding-affinity estimation of peptide inhibitors. About |
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Design of protein-protein interaction (PPI) inhibitors is a key challenge in Structural Bioinformatics and Computer Aided Drug Design. Peptides, which partially mimic the interface area of one of the interacting proteins, are natural candidates to form protein-peptide complexes competing with the original PPI. Some inhibitory peptides were designed by deriving a short linear segment from one of the proteins in a given PPI complex. These peptides were successfully able to inhibit interactions with the partner protein. The prediction of such complexes is especially challenging due to the high flexibility of peptide conformations. We present PepCrawler, a new tool for deriving binding peptides from protein-protein complexes and prediction of protein-peptide complexes. The implemented algorithm is extremely fast, while allowing backbone flexibility of the peptide combined with side-chain flexibility for both the peptide and the receptor protein. The algorithm accepts as input a structure of a protein-protein complex. First, it derives a relatively short, low-energy binding peptide from one of the interacting proteins. Then, it generates a large amount of clash-free peptide docking conformations by using Rapidly-exploring Random Trees (RRT) and grid-based collision detection. By computing the binding energy of each of the conformations, an initial protein-peptide conformation is refined to some low-energy docking solutions. Moreover, a dense binding-energy/RMSD plot is created, that assists in evaluating the affinity of the binding solution via energy funnels. |