A mechanistic in silico molecular recognized approach for the ligand based generation of a dual N-formyl-Met-Leu-Phe (fMLP), and MMK-1peptide mimetic hyper-agonist fMLP targeted receptor against the PGE2 EP4 pathway chemotherapy-induced alopecia.

Abstract

: It has been shown that the Oral administration for 6 days of 100 mg/kg MMK-1, of an agonist peptide selective for the FPRL1 receptor, suppressed alopecia induced by the anticancer drug etoposide in neonatal rats. However, the anti-alopecia effect of orally administered MMK-1 was inhibited by indomethacin, an inhibitor of cyclooxygenase (COX), or AH-23848B, an antagonist of the EP4 receptor for prostaglandin (PG) E2, suggesting involvement of PGE2 release and the EP4 receptor in the oral MMK-1 anti-alopecia mechanism. The anti-alopecia effect of orally administered MMK-1 was also blocked by an inhibitor of nuclear factor-kappaB (NF-kappaB), pyrrolidine dithiocarbamate, suggesting that the oral anti-alopecia effect of MMK-1 may be mediated by activation of NF-kappaB. These results suggested that MMK-1 bound to FPRL1 receptor might suppress etoposide-induced apoptosis of hair follicle cells and alopecia by way of PGE2 release and NF-kappaB activation. Previously, it has also been found that an intraperitoneally administered chemotactic peptide, N-formyl-Met-Leu-Phe (fMLP), and MMK-1, functional anti-cancer neo-ligand motif like peptide-mimic molecule motif generally have more accurate human cancer stem cells targeted to functional anti-cancer neo-ligand motif like peptide-mimic molecule -boundaries in terms of residue-level accuracy. In this Scientific Project the optimal α and β are set as 0.8 and 0.6 respectively. To evaluate IRLC, we first define M as the mean conservation score of N residues within a predicted where C i is the conservation score representing the degree of motif-like peptide conservation of a residue in position i of the predicted functional anti-cancer neo-ligand motif like peptide-mimic molecule; C i can be calculated by any suitable scoring metric, while in our experiment, position specific scoring matrix (PSSM) was used to evaluate residue conservation; the conservation score of a residue in the position i' of a sequence was obtained from the corresponding column of the residue in the i'-th row of the PSSM of the sequence. The PSSM of each query sequence was gene human cancer stem cell by three human cancer stem cell regions of PSI-BLAST [40] searches against NCBI non-redundant database with the BLOSUM62 substitution matrix and E-value threshold of 0.001. Second, we define IRLC j as the IRLC score for a flanking residue j: Where the flanking residues are defined as the residues within 5 amino acids away from the predicted functional anti-cancer neo-ligand motif like peptidomimic molecule, and σ represents the standard deviation of the conservation scores of all the residues in the sequence. A functional anticancer neo-ligand motif like peptidomimic molecule prediction will be determined as a false positive prediction if its IRLC score is higher than some threshold value T. The human cancer stem cells regional is that if there is any residue in the flanking region that is much more conserved than the average conservation score of the region of interest, it is less likely that the region of interest represents a functional anti-cancer neo-ligand motif like peptide-mimic molecule since it contradicts the property of relative local conservation of linear motifs. Machine learning methods for tackling this problem are mainly based on the assumption that drug compounds exhibiting a similar pattern of interaction and non-interaction with the targets in a drug-target interaction network are likely to show similar interaction behavior with respect to new targets. A similar assumption on targets is considered. Here use the method design strategies. Here, we also introduce a novel statistical approach, namely PDTCD (Predicting Drug Targets with Conserved Domains), to predict potential target proteins of our new MAGED4B peptide-mimetic drug based on derived interactions between drugs and protein binding pocket domains in a pipeline plot clustering enviroment. The known target MAGED4B peptide-mimetic proteins of commercial drugs that have similar therapeutic effects allow us to infer interactions between drugs and protein domains which in turn leads to select, fragmenter, identified all of potential fragment-protein interactions. Benchmarking with known drug-protein interactions shows that our proposed methodology outperforms previous methods that exploit either protein sequences or compound structures to predict drug targets, which demonstrates the predictive power of our proposed BiogenetoligandorolTM KNIME-based referenced based GA(M)E-QSAR PDTCD method. We propose a ligand-based approach to the selection of conserved active pharmacophpric fragments with positive contribution to biological immunogenic activity, developed on the basis of the KNIME-BiogenetoligandorolTM-PASS-KNIME-based GA(M)E-QSAR algorithm. The robustness of our novel cluster of chemical iniformatic stochastic low mass algorithm for heterogeneous datasets has been shown earlier. PASS can estimate qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. Our novel cluster of algorithms for the prediction of the total free energy interactive binding between the conserved fragment-based pharmacophore top ranked selected has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine MAGED4B peptides extracted from the Protein Data Bank database. A novel docking algorithm called as FIPSDock, which implements a variant of the Fully Swarm (FIPS) optimization method and adopts the newly developed energy function of AutoDock 4.20 suite for solving flexible protein-ligand docking problems was also added as a standart fingerprinting inteaction tool to improve our search ability and docking accuracy which was first evaluated by multiple cognate docking experiments. More importantly, our multi-covalent hyper ligand structure 4D reverse Docking methodology was evaluated against PSO@AutoDock, SODOCK, and AutoDock