Background: Drug design against proteins to cure various diseases has been studied for several years. Numerous design\ntechniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of\nsmall molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem.\nThere has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor\nagainst a protein target have not yet been established.\nMethodology/Principal Findings: A novel de novo peptide design approach is developed to block activities of disease\nrelated protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the\npeptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined\nvia the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by\ngenerating all possible peptide pairs at each point along the path and determining the binding energies between these\npairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices\nof the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface\nis obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that\nresult upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.\nConclusions/Significance: The model is tested on known protein-peptide inhibitor complexes. The present algorithm\npredicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a\nprotein that has excellent binding affinity according to Auto Dock results.
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