Peptid dizayn stratejileri

Short peptide segments have gained importance as drug candidates. There exist three main problems for peptide design: determining the appropriate sequence with the desired function; properly docking peptide on the protein surface; and the unbound state of the peptide that is to be used as a drug. The `unbound state? means peptide chains in the denatured state at physiological conditions. The details of the potentials for peptide docking simulations and the statistical features of peptides are defined in the literature.As a solution to the peptide sequence determination problem, several experimental and in silico techniques exist to screen peptides. There is no general computational tool to determine peptide sequences. On the other hand, peptide motifs are crucial for selective and specific binding. There have been successful attempts to discover biological motifs by different research groups. To our knowledge, the efforts in the literature are based on the alignment of evolutionarily conserved motifs from proteins. The evolutionary peptide motif search algorithms/servers/software are available. However, there is no general methodology to discover a binding peptide motif for any protein target. We aim to predict peptide sequences and peptide binding motifs for any given protein using no prior information. Here, four different algorithms are developed for peptide design. The implementation of genetic algorithm, Markov model and hidden Markov model with Viterbi decoding leads to prediction of peptides for different protein targets. The algorithms are successful to determine peptide sequences with good theoretical binding affinities. The peptide motifs for two case-studies are also offered. A web-server, VitAL, is constructed based on Viterbi decoding.The statistical thermodynamics features of the unbound peptide as a small thermodynamics system in a thermal reservoir is lacking in the literature. A novel statistical thermodynamics approach is applied to the free peptide segments in order to classify them according to their conformational energies and entropies and heat capacities. The conformational partition function, Helmholtz free energy, energy, entropy and heat capacity are obtained. The model is applied to randomly produced peptides and to known peptide inhibitors. Peptides with low energy, low entropy and low heat capacity are determined to be essential for a peptide to be a good candidate inhibitor.