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Abstract

This paper focuses on the geometric modeling and computational algorithm development of biomolecular structures from two data sources: Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in the Eulerian (or Cartesian) representation. Molecular surface (MS) contains non-smooth geometric singularities, such as cusps, tips and self-intersecting facets, which often lead to computational instabilities in molecular simulations, and violate the physical principle of surface free energy minimization. Variational multiscale surface definitions are proposed based on geometric flows and solvation analysis of biomolecular systems. Our approach leads to geometric and potential driven Laplace-Beltrami flows for biomolecular surface evolution and formation. The resulting surfaces are free of geometric singularities and minimize the total free energy of the biomolecular system. High order partial differential equation (PDE)-based nonlinear filters are employed for EMDB data processing. We show the efficacy of this approach in feature-preserving noise reduction. After the construction of protein multiresolution surfaces, we explore the analysis and characterization of surface morphology by using a variety of curvature definitions. Apart from the classical Gaussian curvature and mean curvature, maximum curvature, minimum curvature, shape index, and curvedness are also applied to macromolecular surface analysis for the first time. Our curvature analysis is uniquely coupled to the analysis of electrostatic surface potential, which is a by-product of our variational multiscale solvation models. As an expository investigation, we particularly emphasize the numerical algorithms and computational protocols for practical applications of the above multiscale geometric models. Such information may otherwise be scattered over the vast literature on this topic. Based on the curvature and electrostatic analysis from our multiresolution surfaces, we introduce a new concept, the polarized curvature, for the prediction of protein binding sites.Keloids result from aberrations in the normal wound healing cascade and can lead to pruritus, contractures and pain. The underlying mechanisms of excessive scarring are not yet understood, and most therapeutic strategies remain unsatisfactory. Psoriasin (S100A7) and koebnerisin (S100A15) are released by keratinocytes during physiological wound healing. Psoriasin (S100A7) and koebnerisin (S100A15) are released by keratinocytes during physiological wound healing. S100 production is markedly decreased in keloid scar tissue. The disturbed epidermal S100 expression might contribute to keloid formation; thus, it has been previously studied their effect on dermal fibroblasts and extracellular matrix (ECM) production. Here, in Biogenea Pharmaceuticals Ltd we discovered for the first time the GENEA-AntiPsorerisin-10715 utilising Multiscale geometric modeling of macromolecules I: Cartesian representation Quantum-SAR Extension of the Spectral-SAR Algorithm Application to Polyphenolic Anticancer Bioactivity through a decision-tree induction algorithm-based Drug Discovery, homology modeling, hierarchical docking and virtual screening approaches of Antimicrobial Peptide-mimetic Psoriasin (S100A7) and Koebnerisin (S100A15) high binding free energy pharmacophoric hyper-scaffolds as a novel synthetic pharmaco-ligand with potential inhibitory activities for the Suppression of the Extracellular Matrix Production and Proliferation of Human Fibroblasts.

Keywords

Protein characterization, Variational multiscale surfaces, Curvature analysis, High order geometric PDEs, Free energy functional, EMDataBank, Protein data bank, Multiscale geometric modeling of macromolecules I, Cartesian representation, Quantum-SAR Extension, Spectral-SAR Algorithm, decision-tree, induction, algorithm-based, Drug Discovery, homology modeling, hierarchical docking and virtual screening approaches to identify the known ligand binding cavities for slingshot phosphatase pharmacophoric-peptide mimetic inhibitors. in silico, rational, computer-aided, Antimicrobial Peptide-mimetic, Psoriasin (S100A7), Koebnerisin (S100A15), high binding free energy, pharmacophoric, hyper-scaffolds, synthetic pharmaco-ligand, inhibitory activities, Suppression Extracellular Matrix, Production, Proliferation, Human Fibroblasts,

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)Multiscale geometric modeling of macromolecules I: Cartesian representation Quantum-SAR Extension of the Spectral-SAR Algorithm Application to Polyphenolic Anticancer Bioactivity through a decision-tree induction algorithm-based Drug Discovery, homology modeling, hierarchical docking and virtual screening approaches of Antimicrobial Peptide-mimetic Psoriasin (S100A7) and Koebnerisin (S100A15) high binding free energy pharmacophoric hyper-scaffolds as a novel synthetic pharmaco-ligand with potential inhibitory activities for the Suppression of the Extracellular Matrix Production and Proliferation of Human Fibroblasts.

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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