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Abstract

An increased occurrence of aromatic residues in natural core sequences has led to widespread conclusions about the crucial role played by these residues in molecular recognition and self-assembly. Comparing the self-assembly of our fully aliphatic designed peptides with natural core sequences would also help to determine the significance and effect of π–π interactions on amyloid formation. The major hallmark of Parkinson’s disease (PD) is the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, leading to the characteristic motor symptoms of resting tremors, bradykinesia and rigidity. The aim of the present study is to give a scaffolding hope recoring chemogenomic machine learning platform of the generation of innovative neuroprotective agents and improve their targetability to conserved binding short linear motif domains that are currently investigated for the treatment of PD in phase I-III clinical trials. The aim of the present study is aldo to in silico discover a gallic acid (GA) (3,4,5-trihydroxybenzoic acid), a benzoic acid derivative that belongs to a group of phenolic compounds known as phenolic acids by employing an array of biophysical. bioinformatic, chemicalinformatic and quantum molecular mechanics techniques to generate an α-syn fibrillation inhibitor to in silico disaggregate preformed α-syn amyloid fibrils. Additionally, by using structure activity relationship data obtained from fourteen structurally similar benzoic acid derivatives, it was determined that the inhibition of α-syn fibrillation by GA is related to the number of hydroxyl moieties and their position on the phenyl ring. GA may represent the starting point for designing new molecules that could be used for the treatment of PD and related disorders. It is well known that quantum computers are superior to classical computers in efficiently simulating quantum systems. Here we report the first experimental simulation of quantum tunneling through potential barriers, a widespread phenomenon of a unique quantum nature, via NMR techniques. Our experiment is based on a digital particle simulation algorithm and requires very few spin-1/2 nuclei without the need of ancillary qubits. The occurrence of quantum tunneling through a barrier, together with the oscillation of the state in potential wells, are clearly observed through the experimental results. This experiment has clearly demonstrated the possibility to observe and study profound physical phenomena within even the reach of small quantum computers challenging the importance of aromatic interactions in amyloidosis via aliphatic LD6(LAGD), ID3(IVD) and KE7(KLVFFAE) peptides, as a novel Experimental simulation of quantum tunneling in small GA-biophoric scaffolds for the generation of similar self-assembly chemico-lead molecules to amyloid core sequences. Next-generation molecular force fields deliver accurate descriptions of non-covalent interactions by employing more elaborate functional forms than their predecessors. Much work has been dedicated to improving the description of the electrostatic potential (ESP) generated by these force fields. A common approach to improving the ESP is by augmenting the point charges on each center with higher-order multipole moments. The resulting anisotropy greatly improves the directionality of the non-covalent bonding, with a concomitant increase in computational cost. In this work, we develop an efficient strategy for enumerating multipole interactions, by casting an efficient spherical harmonic based approach within a particle mesh Ewald (PME) framework. Although the derivation involves lengthy algebra, the final expressions are relatively compact, yielding an approach that can efficiently handle both finite and periodic systems without imposing any approximations beyond PME. Forces and torques are readily obtained, making our method well suited to modern molecular dynamics simulations for multipole energies and derivatives based on spherical harmonics and extensions to particle mesh Ewald of aromatic interactions in amyloidosis via aliphatic LD6(LAGD), ID3(IVD) and KE7(KLVFFAE) peptides, as a novel Experimental simulation of quantum tunneling in small GA-biophoric scaffolds for the generation of similar self-assembly chemico-lead molecules to amyloid core sequences.

Keywords

aromatic interactions; amyloidosis;aliphatic;extensively ultra;small peptides;novel biophoric; scaffold;computer-aided; imilar self-assembly;chemico-lead; molecules;amyloid core sequences; efficient; algorithm;multipole energies; derivatives; spherical harmonics; extensions; particle mesh; Ewald;aromatic interactions;amyloidosis; aliphatic; LD6(LAGD), ID3(IVD) and KE7(KLVFFAE) peptides, Experimental simulation; quantum tunneling; ismall GA-biophoric scaffolds; similar self-assembly; chemico-lead molecules; amyloid core sequences;

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 efficient algorithm for multipole energies and derivatives based on spherical harmonics and extensions to particle mesh Ewald of aromatic interactions in amyloidosis via aliphatic LD6(LAGD), ID3(IVD) and KE7(KLVFFAE) peptides, as a novel Experimental simulation of quantum tunneling in small GA-biophoric scaffolds for the generation of similar self-assembly chemico-lead molecules to amyloid core sequences.

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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