scholarly journals Thermodynamics as a Tool for the Optimization of Drug Binding

10.5772/23036 ◽  
2011 ◽  
Author(s):  
Ruth Matesanz ◽  
Benet Pera ◽  
J. Fernando
Keyword(s):  
Drug Binding

Effect of piperacillin on morphology and viability of gram-negative bacteria

1984 ◽  
Vol 42 ◽  
pp. 218-219
Author(s):  
R. H. Liss
Keyword(s):  
Inhibitory Concentration ◽  
Cytoplasmic Membrane ◽  
Drug Binding ◽  
Gram Negative Bacteria ◽  
Bacterial Cells ◽  
Drug Induced ◽  
Penicillin Binding Proteins ◽  
Lactam Antibiotic ◽  
Drug Free ◽  
Tube Dilution

Piperacillip (PIP) is b-[D(-)-α-(4-ethy1-2,3-dioxo-l-piperzinylcar-bonylamino)-α-phenylacetamido]-penicillanate. The broad spectrum semisynthetic β-lactam antibiotic is believed to effect bactericidal activity through its affinity for penicillin-binding proteins (PBPs), enzymes on the bacterial cytoplasmic membrane that control elongation and septation during cell growth and division. The purpose of this study was to correlate penetration and binding of 14C-PIP in bacterial cells with drug-induced lethal changes assessed by microscopic, microbiologic and biochemical methods.The bacteria used were clinical isolates of Escherichia coli and Pseudomonas aeruginosa (Figure 1). Sensitivity to the drug was determined by serial tube dilution in Trypticase Soy Broth (BBL) at an inoculum of 104 organisms/ml; the minimum inhibitory concentration of piperacillin for both bacteria was 1 μg/ml. To assess drug binding to PBPs, the bacteria were incubated with 14C-PIP (5 μg/0.09 μCi/ml); controls, in drug-free medium.

Drug Repurposing Studies Targeting SARS-nCoV2: An Ensemble Docking Approach on Drug Target 3C-like Protease (3CLpro)

2020 ◽  
Author(s):  
Shruti Koulgi ◽  
Vinod Jani ◽  
Mallikarjunachari Uppuladinne ◽  
Uddhavesh Sonavane ◽  
Asheet Kumar Nath ◽  
...  
Keyword(s):  
Drug Repurposing ◽  
Drug Binding ◽  
Ensemble Docking ◽  
Drug Molecules ◽  
Drug Binding Site ◽  
Main Protease ◽  
Docking Approach ◽  
Novel Coronavirus ◽  
Markov State Modeling ◽  
Viral Polyprotein

<p>The COVID-19 pandemic has been responsible for several deaths worldwide. The causative agent behind this disease is the Severe Acute Respiratory Syndrome – novel Coronavirus 2 (SARS-nCoV2). SARS-nCoV2 belongs to the category of RNA viruses. The main protease, responsible for the cleavage of the viral polyprotein is considered as one of the hot targets for treating COVID-19. Earlier reports suggest the use of HIV anti-viral drugs for targeting the main protease of SARS-CoV, which caused SARS in the year 2002-03. Hence, drug repurposing approach may prove to be useful in targeting the main protease of SARS-nCoV2. The high-resolution crystal structure of 3CL<sup>pro</sup> (main protease) of SARS-nCoV2 (PDB ID: 6LU7) was used as the target. The Food and Drug Administration (FDA) approved and SWEETLEAD database of drug molecules were screened. The apo form of the main protease was simulated for a cumulative of 150 ns and 10 μs open source simulation data was used, to obtain conformations for ensemble docking. The representative structures for docking were selected using RMSD-based clustering and Markov State Modeling analysis. This ensemble docking approach for main protease helped in exploring the conformational variation in the drug binding site of the main protease leading to efficient binding of more relevant drug molecules. The drugs obtained as best hits from the ensemble docking possessed anti-bacterial and anti-viral properties. Small molecules with these properties may prove to be useful to treat symptoms exhibited in COVID-19. This <i>in-silico</i> ensemble docking approach would support identification of potential candidates for repurposing against COVID-19.</p>

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

2019 ◽  
Vol 25 (31) ◽  
pp. 3339-3349 ◽  
Author(s):  
Indrani Bera ◽  
Pavan V. Payghan
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Molecular Dynamics Simulations ◽  
Drug Target ◽  
Structural Information ◽  
Drug Binding ◽  
Structure Based Drug Design ◽  
Drug Designing ◽  
Target Binding ◽  
Dynamics Simulations

Background: Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. Objective: The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. Method: This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. Results: This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. Conclusion: The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

scholarly journals The effect of protein mutations on drug binding suggests ensuing personalised drug selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shunzhou Wan ◽  
Deepak Kumar ◽  
Valentin Ilyin ◽  
Ussama Al Homsi ◽  
Gulab Sher ◽  
...  
Keyword(s):  
Estrogen Receptor ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Personalised Medicine ◽  
Clinical Decision ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Breast Cancer Patients ◽  
Natural Ligand ◽  
Affinity Interaction

AbstractThe advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein–ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient’s genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

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