scholarly journals Miniaturized peptidomimetics and nano-vesiculation in endothelin types through probable nano-disk formation and structure property relationships of endothelins’ fragments

2021 ◽  
Vol 11 (1) ◽  
pp. 220-243
Author(s):  
Riaz A. Khan ◽  
Azra J. Khan
Keyword(s):  
Chemical Properties ◽  
Quantitative Structure Activity Relationship ◽  
Total Charge ◽  
Structure Property ◽  
Disk Formation ◽  
Structure Property Relationships ◽  
Physico Chemical ◽  
Functional Peptides ◽  
Potent Antagonist ◽  
Total Structure

Abstract Endothelins (ETs), which are multi-functional-peptides with potential for antagonist-based-therapy in various physiological-malfunctionings, including cardiovascular, nephrological, oncologic, and diabetic conditions, may produce newer chemical entities and drug leads. The present study deals with molecular-modeling of the ETs’ sub-types, ET-I, II, and III to find the structure property-relationship (SPR) of the ETs, and individual fragments derived from the ET sub-type ET-I. The ETs peptidic tails’ amino acid (AA) sequence’s structural differences and similarities, various dissected fragments of the ET-I, and SPR comparison with the sarafotoxin-6b (SRT-6b), a structurally-related snake-venom, showed points of dissimilarities for their structural specifications, geometric disposition, and physico-chemical properties. The generation of miniaturized (shortened sequence) peptides towards offering peptidomimetic compounds of near- and far-values compared SPR with estimations for log P, hydration energy, and other molecular and quantitative structure activity relationship (QSAR) were based on random and ordered-fragments derived from the original ET-I AA’s sequence, and sequential distance changes in the original ET-I sequence’s chain of 1–21 AA. The feasibility of alternate and bond length parameters-based possible cysteine–cysteine cyclizations, sequence homology, AA’s positional demarcation, and presence/absence of cysteines, homology-based basic non-cysteine and cysteines-AA based cyclization, total structure and fragments end-to-end cyclizations, and geometrical analogy-based miniaturized sequence of the shorter AAs from the original ET-I sequence, together with mutated replacements with naturally constituent AAs of the ETs, and SRT-6 sequences were utilized. The major findings of the fragmented sequences, and sequences at par with the original ETs to provide structures similar to the size, volume and with molecular and electronic properties of electrostatic potential and total charge density distribution, crucial factors in receptor bindings were investigated. The SPRs, molecular properties, and QSAR values were estimated to compare and validate the findings with the known homologous compounds, ET-I, and its known and potent antagonists. The study resulted in leads of smaller and larger sizes of peptide-based compounds which may have prospects as potent antagonist and in future needs their bioactivity evaluations after the synthesis. Moreover, approach to plausible vesiculation of the ETs, and the involved processes and structural requirements, together with the molecular interactions in settling a nano-vesicle of the peptidic structure with a possible mechanism is also suggested.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Chitosan-Nanocellulose Composites for Regenerative Medicine Applications

2020 ◽  
Vol 27 (28) ◽  
pp. 4584-4592 ◽  
Author(s):  
Avik Khan ◽  
Baobin Wang ◽  
Yonghao Ni
Keyword(s):  
Regenerative Medicine ◽  
Preparation Method ◽  
Chemical Properties ◽  
Biological Properties ◽  
Next Generation ◽  
Structure Property ◽  
Physico Chemical ◽  
Structure Property Relationship ◽  
Attractive Candidate ◽  
Fundamental Understanding

Regenerative medicine represents an emerging multidisciplinary field that brings together engineering methods and complexity of life sciences into a unified fundamental understanding of structure-property relationship in micro/nano environment to develop the next generation of scaffolds and hydrogels to restore or improve tissue functions. Chitosan has several unique physico-chemical properties that make it a highly desirable polysaccharide for various applications such as, biomedical, food, nutraceutical, agriculture, packaging, coating, etc. However, the utilization of chitosan in regenerative medicine is often limited due to its inadequate mechanical, barrier and thermal properties. Cellulosic nanomaterials (CNs), owing to their exceptional mechanical strength, ease of chemical modification, biocompatibility and favorable interaction with chitosan, represent an attractive candidate for the fabrication of chitosan/ CNs scaffolds and hydrogels. The unique mechanical and biological properties of the chitosan/CNs bio-nanocomposite make them a material of choice for the development of next generation bio-scaffolds and hydrogels for regenerative medicine applications. In this review, we have summarized the preparation method, mechanical properties, morphology, cytotoxicity/ biocompatibility of chitosan/CNs nanocomposites for regenerative medicine applications, which comprises tissue engineering and wound dressing applications.

scholarly journals The use of UniProtKB/BIOPEP for the analysis of oat globulin physicochemical parameters and bioactivity

2018 ◽  
Vol 36 (No. 2) ◽  
pp. 119-125 ◽  
Author(s):  
Szerszunowicz Iwona ◽  
Nałęcz Dorota
Keyword(s):  
Biological Activity ◽  
Ace Inhibitors ◽  
Physicochemical Parameters ◽  
Chemical Properties ◽  
Signal Peptides ◽  
Optimum Conditions ◽  
Physico Chemical ◽  
Functional Peptides ◽  
Chymotrypsin A ◽  
11S Globulins

The physico-chemical properties of oat proteins (globulins) were determined and an analysis was done whether products of in silico proteolysis contain mono- and multi-functional peptides with various biological activity. The MW(s), calculated by the ProtParam program, for precursors of 12S and 11S globulins and proteins without signal peptides were in the range of 50.78–61.86 kDa. The pH at which the solubility of the proteins under analysis was the lowest ranged from 7.29 to 9.44. A simulation of proteolysis with three enzymes (pepsin, trypsin, and chymotrypsin A) in the optimum conditions of the enzyme action can produce 6–8 bi-functional, 5–10 mono-functional biopeptides from oat globulins (12S, 11S globulins), and one tri-functional biopeptide (VY). The mono-functional biopeptides exhibited the activity of DPPIV inhibitors or ACE inhibitors, and the multi-functional biopeptides can exhibit the activity of inhibitors of both enzymes (DPPIV and ACE). Sensory peptides accounted for 43% of all the released mono- and multi-functional biopeptides.

Structure – Property Relationships for Dyes of Different Nature

2013 ◽  
Vol 821-822 ◽  
pp. 488-492
Author(s):  
Felix Telegin ◽  
Irina Shushina ◽  
Jian Hua Ran ◽  
Yulia Biba ◽  
Aleksandr Mikhaylov ◽  
...  
Keyword(s):  
Practical Importance ◽  
Structure Property ◽  
Light Fastness ◽  
Theoretical Evaluation ◽  
Fixation Rate ◽  
Systematic Analysis ◽  
Structure Property Relationships ◽  
Physico Chemical ◽  
Quantitative Structure Property Relationships ◽  
Homo And Lumo

Systematic analysis of quantitative structure – property relationships for dyes of different nature has been reviewed. On the basis of the experimental results published in the literature and theoretical evaluation of amphiphilic and electrophilic properties of dyes of different nature several basic conclusions of scientific and practical importance are proposed. It was found that water/octanol partition coefficients exhibit correlation with dye partition between hydrophobic synthetic fibres and dyebath as well as dye affinity. Hydrophobicity of dyes controls several technical properties of dyes and dyeings, such as wash fastness and light fastness, migration factor, rate of dyeing and fixation rate. Energy of frontier electronic orbitals (HOMO and LUMO energies) correlates with different properties characterizing redox properties of dyes: oxidative and reductive destruction in chemical reactions, photochemical and biochemical destruction of dyes, wash and light fastness of dyeings. The results of this study are useful for physico-chemical analysis of dye sorption by textile fibres, destruction of dyes in polymers and solutions as well as for design of new dyes of high quality.

scholarly journals Structure-Property Relationships in Benzofurazan Derivatives: A Combined Experimental and DFT/TD-DFT Investigation

2021 ◽  
Author(s):  
Hanen Raissi ◽  
Imen Chérif ◽  
Hajer Ayachi ◽  
Ayoub Haj Said ◽  
Fredj Hassen ◽  
...  
Keyword(s):  
Energy Levels ◽  
Electrophilic Aromatic Substitution ◽  
Aromatic Substitution ◽  
Structure Property ◽  
Electrophilicity Index ◽  
Cyclic Amines ◽  
Structure Property Relationships ◽  
Physico Chemical ◽  
Homo And Lumo ◽  
The Relationship

In this work we seek to understand and to quantify the reactivity of benzofurazan derivatives toward secondary cyclic amines, like pyrrolidine, piperidine and morpholine, acting as nucleophile groups in SNAr reactions. For this aim, physico-chemical and structural descriptors were determined experimentally and theoretically using the DFT/B3LYP/6-31+ g (d,p) methodology. Thus, different 4-X-7-nitrobenzofurazans (X = OCH3, OC6H5 and Cl) and products corresponding to the electrophilic aromatic substitution by pyrrolidine, piperidine and morpholine, were investigated. Particularly, the HOMO and LUMO energy levels of the studied compounds, determined by Cyclic Voltammetry (CV) and DFT calculations, were used to evaluate the electrophilicity index (ω). The latter was exploited, according to Parr’s approach, to develop a relationship which rationalizes the kinetic data previously reported for the reactions of the 4-X-7-nitrobenzofurazans with nucleophiles cited above. Moreover, the Parr’s electrophilicity index (ω) of these benzofurazans determined in this work were combined with their electrophilicity parameters (E), reported in preceding papers, was found to predict the unknown electrophilicity parameters E of 4-piperidino, 4-morpholino and 4-pyrrolidino-7-nitrobenzofurazan. In addition, the relationship between the Parr’s electrophilicity index (ω) and Hammett constants σ, has been used as a good model to predict the electronic effect of the nucleophile groups. Finally, we will subsequently compare the electrophilicity index (ω) and the electrophilicity parameters (E) of these series of 7-X-4-nitrobenzofurazans with the calculated dipole moment (μ) in order to elucidate general relationships between E, ω and μ.

Introduction

2004 ◽  
Author(s):  
Robert E. Newnham
Keyword(s):  
Transport Properties ◽  
Chemical Properties ◽  
Polycrystalline Materials ◽  
Structure Property ◽  
Structure Property Relationships ◽  
Rank Tensor ◽  
Linear Relationships ◽  
Basic Physics ◽  
Curie Principle ◽  
Textured Materials

The physical and chemical properties of crystals and textured materials often depend on direction. An understanding of anisotropy requires a mathematical description together with atomistic arguments to quantify the property coefficients in various directions. Tensors and matrices are the mathematics of choice and the atomistic arguments are partly based on symmetry and partly on the basic physics and chemistry of materials. These are subjects of this book: tensors, matrices, symmetry, and structure–property relationships. We begin with transformations and tensors and then apply the ideas to the various symmetry elements found in crystals and textured polycrystalline materials. This brings in the 32 crystal classes and the 7 Curie groups. After working out the tensor and matrix operations used to describe symmetry elements, we then apply Neumann’s Law and the Curie Principle of Symmetry Superposition to various classes of physical properties. The first group of properties is the standard topics of classical crystal physics: pyroelectricity, permittivity, piezoelectricity, elasticity, specific heat, and thermal expansion. These are the linear relationships between mechanical, electrical, and thermal variables as laid out in the Heckmann Diagram. These standard properties are all polar tensors ranging in rank from zero to four. Axial tensor properties appear when magnetic phenomena are introduced. Magnetic susceptibility, the relationship between magnetization and magnetic field, is a polar second rank tensor, but the linear relationships between magnetization and thermal, electrical, and mechanical variables are all axial tensors. As shown in Fig. 1.2, magnetization can be added to the Heckmann Diagram converting it into a tetrahedron of linear relationships. Pyromagnetism, magnetoelectricity, and piezomagnetism are the linear relationships between magnetization and temperature change, electric field, and mechanical stress. Examples of tensors of rank zero through four are given in Table 1.1. In this book we will also treat many of the nonlinear relationships such as magnetostriction, electrostriction, and higher order elastic constants. The third group of properties is transport properties that relate flow to a gradient. Three common types of transport properties relate to the movement of charge, heat, and matter. Electrical conductivity, thermal conductivity, and diffusion are all polar second rank tensor properties.

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