Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Glycoprotein-96, a non-polymorphic heat-shock protein, associates with intracellular peptides. Autologous tumor-derived heat shock protein-peptide complex 96 (HSPPC-96) can elicit potent tumor-specific T cell responses and protective immunity in animal models. Chemokines were described originally in the context of providing migrational cues for leukocytes. They are now known to have broader activities, including those that favor tumor growth. Treatment with autologous tumor-derived HSPPC-96 was feasible and safe at all doses tested. Observed immunological effects and antitumor activity were modest, precluding selection of a biologically active dose. Coevolution between proteins is crucial for understanding protein-protein interaction. Simultaneous changes allow a protein complex to maintain its overall structural-functional integrity. In this Research Scientific Project, we combined statistical coupling analysis (SCA) and molecular dynamics simulations on thecomplex-96 (HSPPC-96) protein complex to evaluate coevolution between conserved binding protein domain regions. We reconstructed an inter-protein residue coevolution network, consisting of 37 residues and 37complex-96 (HSPPC-96) binding domains conserved peptide derived residues and its fitness scoring reverse ligand docking interactions. It shows that most of the coevolved residue pairs are spatially proximal. When the mutations happened, the stable local structures were broken up and thus the protein interaction was decreased or inhibited, with a following increased risk of melanoma. The identification of inter-protein coevolved residues in thecomplex-96 (HSPPC-96) complex can be helpful for designing protein drug target and in silico discovery of engineering novel nanomolecule experiments.
Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability.
Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets. In this scientific study we have in silico discovered a Unique Small Molecule Modulator of CXCR4 tumor-derived heat-shock protein peptide complex-96 (HSPPC-96) by identifying Hits of a High-Throughput Screen Identify the Hydrophobic Pocket of Autotaxin/Lysophospholipase D as an Inhibitory Surface Molecular dynamic simulation and statistical coupling analysis via a Asymmetric bagging and feature selection for activities prediction of drug molecules of an in silico developed unique fragment poly-pharmacologic modulator of CXCR4 tumor-derived heat-shock GGHFGPFDY peptide mimotopic complex-96 (HSPPC-96) by highthroughput identifying hits in the hydrophobic autotaxin/lysophospholipase D pocket.
In silico development; unique fragment; poly-pharmacologic modulator; CXCR4 tumor-derived; heat-shock; GGHFGPFDY peptide; mimotopic complex-96; (HSPPC-96); highthroughput identifying hits; hydrophobic autotaxin/lysophospholipase D pocket.