scholarly journals Molecular Dynamics Simulations to Investigate the Binding Mode of the Natural Product Liphagal with Phosphoinositide 3-Kinase α

Molecules ◽  
2016 ◽  
Vol 21 (7) ◽  
pp. 857 ◽  
Author(s):  
Yanjuan Gao ◽  
Ying Ma ◽  
Guangde Yang ◽  
Yiping Li
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Natural Product ◽  
Binding Mode ◽  
Phosphoinositide 3 Kinase ◽  
Dynamics Simulations

Pyrrolidine Derivatives as Plasmepsin Inhibitors: Binding Mode Analysis Assisted by Molecular Dynamics Simulations of a Highly Flexible Protein

ChemMedChem ◽  
2010 ◽  
Vol 5 (3) ◽  
pp. 443-454 ◽  
Author(s):  
Torsten Luksch ◽  
Andreas Blum ◽  
Nina Klee ◽  
Wibke E. Diederich ◽  
Christoph A. Sotriffer ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Mode ◽  
Mode Analysis ◽  
Flexible Protein ◽  
Dynamics Simulations

Revealing the binding mode between respiratory syncytial virus fusion protein and benzimidazole-based inhibitors

2015 ◽  
Vol 11 (7) ◽  
pp. 1857-1866
Author(s):  
Dingjue Ji ◽  
Wei Ye ◽  
HaiFeng Chen
Keyword(s):  
Molecular Dynamics ◽  
Respiratory Syncytial Virus ◽  
Fusion Protein ◽  
Molecular Dynamics Simulations ◽  
Binding Mode ◽  
Virus Fusion ◽  
Syncytial Virus ◽  
Dynamics Simulations

The binding mode between benzimidazole-based inhibitors and RSVF protein was revealed by docking and molecular dynamics simulations.

Structural insights into the binding mode of d-sorbitol with sorbitol dehydrogenase using QM-polarized ligand docking and molecular dynamics simulations

2016 ◽  
Vol 114 ◽  
pp. 244-256 ◽  
Author(s):  
Chandrabose Selvaraj ◽  
Gopinath Krishnasamy ◽  
Sujit Sadashiv Jagtap ◽  
Sanjay K.S. Patel ◽  
Saurabh Sudha Dhiman ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Mode ◽  
Sorbitol Dehydrogenase ◽  
Ligand Docking ◽  
Dynamics Simulations ◽  
Structural Insights

scholarly journals Identification of CDK7 Inhibitors from Natural Sources Using Pharmacoinformatics and Molecular Dynamics Simulations

Biomedicines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1197
Author(s):  
Vikas Kumar ◽  
Shraddha Parate ◽  
Gunjan Thakur ◽  
Gihwan Lee ◽  
Hyeon-Su Ro ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Affinity ◽  
Cost Effective ◽  
Binding Mode ◽  
Kinase Domain ◽  
Dynamics Simulation ◽  
Mode Analysis ◽  
Atp Binding Site ◽  
Dynamics Simulations

The cyclin-dependent kinase 7 (CDK7) plays a crucial role in regulating the cell cycle and RNA polymerase-based transcription. Overexpression of this kinase is linked with various cancers in humans due to its dual involvement in cell development. Furthermore, emerging evidence has revealed that inhibiting CDK7 has anti-cancer effects, driving the development of novel and more cost-effective inhibitors with enhanced selectivity for CDK7 over other CDKs. In the present investigation, a pharmacophore-based approach was utilized to identify potential hit compounds against CDK7. The generated pharmacophore models were validated and used as 3D queries to screen 55,578 natural drug-like compounds. The obtained compounds were then subjected to molecular docking and molecular dynamics simulations to predict their binding mode with CDK7. The molecular dynamics simulation trajectories were subsequently used to calculate binding affinity, revealing four hits—ZINC20392430, SN00112175, SN00004718, and SN00262261—having a better binding affinity towards CDK7 than the reference inhibitors (CT7001 and THZ1). The binding mode analysis displayed hydrogen bond interactions with the hinge region residues Met94 and Glu95, DFG motif residue Asp155, ATP-binding site residues Thr96, Asp97, and Gln141, and quintessential residue outside the kinase domain, Cys312 of CDK7. The in silico selectivity of the hits was further checked by docking with CDK2, the close homolog structure of CDK7. Additionally, the detailed pharmacokinetic properties were predicted, revealing that our hits have better properties than established CDK7 inhibitors CT7001 and THZ1. Hence, we argue that proposed hits may be crucial against CDK7-related malignancies.

scholarly journals Prediction of Molecular Interactions And Physicochemical Properties Relevant For Vasopressin V2 Receptor Antagonism

2021 ◽  
Author(s):  
Ania de la Nuez Veulens ◽  
Yoanna María Álvarez Ginarte ◽  
Rolando Eduardo Rodríguez Fernandez ◽  
Fabrice Leclerc ◽  
Luis Alberto Montero Cabrera
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Physicochemical Properties ◽  
Molecular Dynamics Simulations ◽  
Binding Mode ◽  
Qsar Model ◽  
Antagonist Activity ◽  
Vasopressin V2 Receptor ◽  
V2 Receptor ◽  
Dynamics Simulations

Abstract We have developed two ligand and receptor-based computational approaches to study the physicochemical properties relevant to the biological activity of vasopressin V2 receptor (V2R) antagonist and eventually to predict the expected binding mode to V2R. The obtained Quantitative Structure Activity Relationship (QSAR) model showed a correlation of the antagonist activity with the hydration energy (EH2O) , the polarizability (P) and the calculated partial charge on atom N7 (q6) of the common substructure. The first two descriptors showed a positive contribution to antagonist activity, while the third one had a negative contribution. V2R was modeled and further relaxed on a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocoline (POPC) membrane by molecular dynamics simulations. The receptor antagonist complexes were guessed by molecular docking, and the stability of the most relevant structures were also evaluated by molecular dynamics simulations. As a result, amino acid residues Q96, W99, F105, K116, F178, A194, F307, and M311 were identified with the probably most relevant antagonist-receptor interactions on the studied complexes. The proposed QSAR model could explain the molecular properties relevant to the antagonist activity. The contributions to the antagonist-receptor interaction appeared also in agreement with the binding mode of the complexes obtained by molecular docking and Molecular Dynamics. These models will be used in further studies to look for new V2R potential antagonist molecules.

Flavin oxidation state impacts on nitrofuran antibiotic binding orientation in nitroreductases

2021 ◽  
Vol 478 (18) ◽  
pp. 3423-3428
Author(s):  
Helen S. Toogood ◽  
Nigel S. Scrutton
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Molecular Dynamics Simulations ◽  
Oxidation State ◽  
Binding Mode ◽  
Kinetic Mechanism ◽  
Binding Orientation ◽  
Nitrofuran Antibiotics ◽  
Dynamics Simulations ◽  
Binding Orientations

Nitroreductases catalyse the NAD(P)H-dependent nitro reduction in nitrofuran antibiotics, which activates them into cytotoxic molecules leading to cell death. The design of new effective nitrofuran antibiotics relies on knowledge of the kinetic mechanism and nitrofuran binding mode of microbial nitroreductases NfsA and NfsB. This has been hampered by multiple co-crystallisation studies revealing ligand binding in non-electron transfer competent states. In a recent study by Day et al. (2021) the authors investigated the likely reaction mechanism and mode of nitrofurantoin binding to NfsA using potentiometry, global kinetics analysis, crystallography and molecular dynamics simulations. Their findings suggest nitrofurantoin reduction proceeds via a direct hydride transfer from reduced FMN, while the crystallographic binding orientation is an inhibitory complex. Molecular dynamics simulations suggest ligand binding orientations is dependent on the oxidation state of the FMN. This study highlights the importance of utilising computational studies alongside traditional crystallographic approaches, when multiple stable ligand binding orientations can occur.

The Binding Mode of Progesterone to Its Receptor Deduced from Molecular Dynamics Simulations

ChemBioChem ◽  
2003 ◽  
Vol 4 (2-3) ◽  
pp. 155-161 ◽  
Author(s):  
Tiziana Mordasini ◽  
Alessandro Curioni ◽  
Roberta Bursi ◽  
Wanda Andreoni
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Binding Mode ◽  
Dynamics Simulations
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