scholarly journals Computational molecular modelling as a platform for a deeper understanding of protein dynamics and rational drug design

2020 ◽  
Author(s):  
Gianvito Grasso ◽  
Lorenzo Pallante ◽  
Jack A. Tuszynski ◽  
Umberto Morbiducci ◽  
Marco A. Deriu
Keyword(s):  
Molecular Modelling ◽  
Molecular Level ◽  
Structural Features ◽  
Rational Drug Design ◽  
Therapeutic Approaches ◽  
Amyloidogenic Proteins ◽  
Rational Drug ◽  
Aggregation Inhibitors ◽  
The Stability ◽  
Toxicity Pathways

Elucidating structural features of protein aggregation at molecular level may provide novel opportunities for overarching therapeutic approaches such as blocking common aggregation-induced cellular toxicity pathways. In this context molecular modelling stimulates further research on amyloid aggregation modulators and modelling platforms can be used to test the efficiency of potential aggregation inhibitors aimed at destabilizing/reducing the stability of the amyloidogenic proteins

scholarly journals Structure of the human gonadotropin-releasing hormone receptor GnRH1R reveals an unusual ligand binding mode

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Wei Yan ◽  
Lin Cheng ◽  
Wei Wang ◽  
Chao Wu ◽  
Xin Yang ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Reproductive Function ◽  
Binding Mode ◽  
Structural Features ◽  
Docking Studies ◽  
Rational Drug Design ◽  
Releasing Hormone ◽  
Promising Therapeutic Target ◽  
Rational Drug ◽  
Main Regulator

Abstract Gonadotrophin-releasing hormone (GnRH), also known as luteinizing hormone-releasing hormone, is the main regulator of the reproductive system, acting on gonadotropic cells by binding to the GnRH1 receptor (GnRH1R). The GnRH-GnRH1R system is a promising therapeutic target for maintaining reproductive function; to date, a number of ligands targeting GnRH1R for disease treatment are available on the market. Here, we report the crystal structure of GnRH1R bound to the small-molecule drug elagolix at 2.8 Å resolution. The structure reveals an interesting N-terminus that could co-occupy the enlarged orthosteric binding site together with elagolix. The unusual ligand binding mode was further investigated by structural analyses, functional assays and molecular docking studies. On the other hand, because of the unique characteristic of lacking a cytoplasmic C-terminal helix, GnRH1R exhibits different microswitch structural features from other class A GPCRs. In summary, this study provides insight into the ligand binding mode of GnRH1R and offers an atomic framework for rational drug design.

Cyclin Dependent Kinase as a Novel Therapeutic Target: An Endless Story

2020 ◽  
Vol 14 ◽  
Author(s):  
Ahmed Mohamed Etman ◽  
Sherif Sabry Abdel Mageed ◽  
Mohamed Ahmed Ali ◽  
Mahmoud Abd El Monem El Hassab
Keyword(s):  
Molecular Mechanisms ◽  
Binding Pocket ◽  
Structural Features ◽  
Rational Drug Design ◽  
Cyclin Dependent Kinase ◽  
Cyclin Dependent Kinases ◽  
Therapeutic Class ◽  
Pharmacological Targets ◽  
Rational Drug ◽  
Cycle Regulation

Abstract:: Cyclin Dependent Kinases (CDKs) are a family of enzymes that along with their Cyclin partners play a crucial role in cell cycle regulation at many biological functions such as proliferation, differentiation, DNA repair and apoptosis. Thus, they are tightly regulated by a vast of inhibitory and activating enzymes. Deregulation of these kinases’ activity either by amplification, overexpression or mutation of CDKs or Cyclins leads to uncontrolled proliferation of cancer cells. Hyperactivity of these kinases has been reported in wide variety of human cancers. Hence, CDKs has been established as one of the most attractive pharmacological targets in the development of promising anticancer drugs. The elucidated structural features and the well characterized molecular mechanisms of CDKs have been the guide in designing inhibitors to these kinases. Yet they remain a challenging therapeutic class as they share conserved structure similarity in their active site. Several inhibitors have been discovered from natural sources or identified through high through put screening and rational drug design approaches. Most of these inhibitors target the ATP binding pocket, so they suffer from many limitations. Now a growing number of ATP non-competitive peptides and small molecules have been reported.

Therapeutic approaches against common structural features of toxic oligomers shared by multiple amyloidogenic proteins

2014 ◽  
Vol 88 (4) ◽  
pp. 468-478 ◽  
Author(s):  
Marcos J. Guerrero-Muñoz ◽  
Diana L. Castillo-Carranza ◽  
Rakez Kayed
Keyword(s):  
Structural Features ◽  
Therapeutic Approaches ◽  
Amyloidogenic Proteins

scholarly journals Restriction of S-adenosylmethionine conformational freedom by knotted protein binding sites

2019 ◽  
Author(s):  
Agata P. Perlinska ◽  
Adam Stasiulewicz ◽  
Ewa K. Nawrocka ◽  
Krzysztof Kazimierczuk ◽  
Piotr Setny ◽  
...  
Keyword(s):  
Binding Sites ◽  
Protein Function ◽  
Tight Binding ◽  
Structural Features ◽  
Rational Drug Design ◽  
Conformational Space ◽  
Nmr Studies ◽  
Rational Drug ◽  
Specific Proteins ◽  
Conformational Freedom

AbstractS-adenosylmethionine (SAM) is one of the most important enzyme substrates. It is vital for the function of various proteins, including large group of methyltransferases (MTs). Intriguingly, some bacterial and eukaryotic MTs, while catalysing the same reaction, possess significantly different topologies, with the former being a knotted one. Here, we conducted a comprehensive analysis of SAM conformational space and factors that affect its vastness. We investigated SAM in two forms: free in water (via NMR studies and explicit solvent simulations) and bound to proteins (based on all data available in the PDB). We identified structural descriptors – angles which show the major differences in SAM conformation between unknotted and knotted methyltransferases. Moreover, we report that this is caused mainly by a characteristic for knotted MTs tight binding site formed by the knot and the presence of adenine-binding loop. Additionally, we elucidate conformational restrictions imposed on SAM molecules by other protein groups in comparison to conformational space in water.Author summaryThe topology of a folded polypeptide chain has great impact on the resulting protein function and its interaction with ligands. Interestingly, topological constraints appear to affect binding of one of the most ubiquitous substrates in the cell, S-adenosylmethionine (SAM), to its target proteins. Here, we demonstrate how binding sites of specific proteins restrict SAM conformational freedom in comparison to its unbound state, with a special interest in proteins with non-trivial topology, including an exciting group of knotted methyltransferases. Using a vast array of computational methods combined with NMR experiments, we identify key structural features of knotted methyltransferases that impose unorthodox SAM conformations. We compare them with the characteristics of standard, unknotted SAM binding proteins. These results are significant for understanding differences between analogous, yet topologically different enzymes, as well as for future rational drug design.

Key Structural Features of Azanaphthoquinone Annelated Pyrrole Derivative as Anticancer Agents Based on the Rational Drug Design Approaches

2013 ◽  
Vol 32 (5-6) ◽  
pp. 541-554 ◽  
Author(s):  
Pharit Kamsri ◽  
Auradee Punkvang ◽  
Nipawan Pongprom ◽  
Apinya Srisupan ◽  
Patchreenart Saparpakorn ◽  
...  
Keyword(s):  
Drug Design ◽  
Anticancer Agents ◽  
Structural Features ◽  
Rational Drug Design ◽  
Pyrrole Derivative ◽  
Rational Drug

scholarly journals Rationally designed selective inhibitors of trypanothione reductase. Phenothiazines and related tricyclics as lead structures

1992 ◽  
Vol 286 (1) ◽  
pp. 9-11 ◽  
Author(s):  
T J Benson ◽  
J H McKie ◽  
J Garforth ◽  
A Borges ◽  
A H Fairlamb ◽  
...  
Keyword(s):  
Drug Design ◽  
Glutathione Reductase ◽  
Binding Site ◽  
Molecular Modelling ◽  
Tricyclic Antidepressants ◽  
Rational Drug Design ◽  
Trypanothione Reductase ◽  
Tricyclic Compounds ◽  
Rational Drug ◽  
Anti Oxidant

Trypanothione reductase, an essential component of the anti-oxidant defences of parasitic trypanosomes and Leishmania, differs markedly from the equivalent host enzyme, glutathione reductase, in the binding site for the disulphide substrate. Molecular modelling of this region suggested that certain tricyclic compounds might bind selectively to trypanothione reductase without inhibiting host glutathione reductase. This was confirmed by testing 30 phenothiazine and tricyclic antidepressants, of which clomipramine was found to be the most potent, with a K(i) of 6 microM, competitive with respect to trypanothione. Many of these compounds have been noted previously to have anti-trypanosomal and anti-leishmanial activity and thus they can serve as lead structures for rational drug design.

scholarly journals On the Rational Drug Design for Hypertension through NMR Spectroscopy

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 12
Author(s):  
Eleni Chontzopoulou ◽  
Andreas G. Tzakos ◽  
Thomas Mavromoustakos
Keyword(s):  
Drug Delivery ◽  
Solid State ◽  
Nmr Spectroscopy ◽  
High Resolution ◽  
Lipid Bilayers ◽  
Rational Design ◽  
Peptide Hormone ◽  
Rational Drug Design ◽  
Rational Drug ◽  
The Stability

Antagonists of the AT1receptor (AT1R) are beneficial molecules that can prevent the peptide hormone angiotensin II from binding and activating the specific receptor causing hypertension in pathological states. This review article summarizes the multifaced applications of solid and liquid state high resolution nuclear magnetic resonance (NMR) spectroscopy in antihypertensive commercial drugs that act as AT1R antagonists. The 3D architecture of these compounds is explored through 2D NOESY spectroscopy and their interactions with micelles and lipid bilayers are described using solid state 13CP/MAS, 31P and 2H static solid state NMR spectroscopy. Due to their hydrophobic character, AT1R antagonists do not exert their optimum profile on the AT1R. Therefore, various vehicles are explored so as to effectively deliver these molecules to the site of action and to enhance their pharmaceutical efficacy. Cyclodextrins and polymers comprise successful examples of effective drug delivery vehicles, widely used for the delivery of hydrophobic drugs to the active site of the receptor. High resolution NMR spectroscopy provides valuable information on the physical-chemical forces that govern these drug:vehicle interactions, knowledge required to get a deeper understanding on the stability of the formed complexes and therefore the appropriateness and usefulness of the drug delivery system. In addition, it provides valuable information on the rational design towards the synthesis of more stable and efficient drug formulations.

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

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