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Abstract

As high-throughput biochemical screens are both expensive and labor intensive, researchers in academia and industry are turning increasingly to virtual-screening methodologies. Virtual screening relies on scoring functions to quickly assess ligand potency. Although useful for in silico ligand identification, these scoring functions generally give many false positives and negatives; indeed, a properly trained human being can often assess ligand potency by visual inspection with greater accuracy. Given the success of the human mind at protein−ligand complex characterization, we present here a scoring function based on a neural network, a computational model that attempts to simulate, albeit inadequately, the microscopic organization of the brain. Computer-aided drug design depends on fast and accurate scoring functions to aid in the identification of small-molecule ligands. The scoring function presented here, used either on its own or in conjunction with other more traditional functions, could prove useful in future drug-discovery efforts.A Hyper drug-target interaction analysis for the In silico free energy potency optimization for the in silico discovery of a poly-targeted binding-pocket peptide mimic annotated chemo-antagonists to HIV-II viral replication cycle associated enzymes.NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes. Exploring hyper drug-target interactions using restricted Boltzmann machines. Computational development of rubromycin-based lead compounds for HIV-1 reverse transcriptase inhibition. Considerable success has been achieved in the treatment of HIV-1 infection, and more than two dozen antiretroviral drugs are available targeting several distinct steps in the viral replication cycle. However, resistance to these compounds emerges readily, even in the context of combination therapy. Drug toxicity, adverse drug-drug interactions, and accompanying poor patient adherence can also lead to treatment failure. These considerations make continued development of novel antiretroviral therapeutics necessary. Current approaches for designing chemical recored ligand binding proteins for medical and biotechnological uses rely upon raising antibodies against a target antigen in immunized animals and/or performing laboratory directed evolution of proteins with an existing low affinity for the desired ligand, both of which offer incomplete control over molecular details. Computational design has the potential to provide a general, complementary low mass algorithmic approach for small molecule recognition in which designed and predicted features and selectivity can be rationally in sioico programmed. Structural and biophysical characterization of previously designed ligand binding proteins has revealed numerous discrepancies with the design models, however, and it was concluded that protein-ligand interaction design is an unsolved problem. The development of robust computational methods for the design of small molecule-binding proteins with high affinity and selectivity would have wide-ranging applicationS. The goal of existing methods for computational enzyme design is to promote catalysis by creating energetically favorable hydrogen bonding, van der Waals, and electrostatic interactions to a high-energy reaction transition state(s) and/or intermediate(s). Although these interactions are also important for stabilizing the bound ground-state conformations of protein-motif conserved petide mimetic pharmacophore consisting of linked small molecule complexes as the sole determinant of small molecule binding. Here, in this research drug discovery approach we discovered an annotated Neural-Network-Based NNScoring Function Characterization of Protein−Ligand Hyper drug-target Complexes interaction analysis for the in silico free energy potency optimization of a poly-targeted binding-pocket peptide mimic chemo-antagonists to HIV-II viral replication cycle associated enzymes.

Keywords

Neural-Network-Based, NNScoring Function, Protein−Ligand Hyper drug-target Complexes, interaction analysis, in silico, free energy, potency optimization, in silico discovery, poly-targeted, binding-pocket, peptide mimic, annotated chemo-antagonists, HIV-II viral replication, cycle associated enzymes

Article Type

Research Article – Abstract

Publication history

Received: Sep 20, 2017
Accepted: Sep 25, 2017
Published: Oct 01, 2017

Citation

Grigoriadis Ioannis, Grigoriadis George, Grigoriadis Nikolaos, George Galazios (2017) An annotated Neural-Network-Based NNScoring Function Characterization of Protein−Ligand Hyper drug-target Complexes interaction analysis for the in silico free energy potency optimization of a poly-targeted binding-pocket peptide mimic chemo-antagonists to HIV-II viral replication cycle associated enzymes.

Authors Info

Grigoriadis Nikolaos
Department of IT Computer Aided Personalized Myoncotherapy, Cartigenea-Cardiogenea, Neurogenea-Cellgenea, Cordigenea-HyperoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis Ioannis
Department of Computer Drug Discovery Science, BiogenetoligandorolTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

Grigoriadis George
Department of Stem Cell Bank and ViroGeneaTM,
Biogenea Pharmaceuticals Ltd,
Thessaloniki, Greece;

George Galazios
Professor of Obstetrics and Gynecology,
Democritus University of Thrace,
Komotini, Greece;

E-mail: biogeneadrug@gmail.com

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