PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors

Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.

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Molecular Determinants and Kinetic Parameters of Ligand Binding to G Protein-Coupled Receptors using Markov State Model Analysis

Biophysical Journal ◽

10.1016/j.bpj.2014.11.558 ◽

2015 ◽

Vol 108 (2) ◽

pp. 97a

Author(s):

Sebastian Schneider ◽

Davide Provasi ◽

Marta Filizola

Keyword(s):

Ligand Binding ◽

Kinetic Parameters ◽

G Protein ◽

Model Analysis ◽

G Protein Coupled Receptors ◽

State Model ◽

Markov State Model ◽

Markov State ◽

Molecular Determinants ◽

G Protein Coupled

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The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM

Computational and Mathematical Methods in Medicine ◽

10.1155/2016/4809831 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Meng-yu Wang ◽

Peng Li ◽

Pei-li Qiao

Keyword(s):

Virtual Screening ◽

Ensemble Learning ◽

Cathepsin K ◽

Support Vector ◽

Training Set ◽

Screening Process ◽

Ligand Interaction ◽

Virtual Screening Experiment ◽

Protein Ligand Interaction

Using the theory of machine learning to assist the virtual screening (VS) has been an effective plan. However, the quality of the training set may reduce because of mixing with the wrong docking poses and it will affect the screening efficiencies. To solve this problem, we present a method using the ensemble learning to improve the support vector machine to process the generated protein-ligand interaction fingerprint (IFP). By combining multiple classifiers, ensemble learning is able to avoid the limitations of the single classifier’s performance and obtain better generalization. According to the research of virtual screening experiment with SRC and Cathepsin K as the target, the results show that the ensemble learning method can effectively reduce the error because the sample quality is not high and improve the effect of the whole virtual screening process.

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Novel Insights into Biased Agonism at G Protein-Coupled Receptors and their Potential for Drug Design

Current Pharmaceutical Design ◽

10.2174/1381612811319280014 ◽

2013 ◽

Vol 19 (28) ◽

pp. 5156-5166 ◽

Cited By ~ 21

Author(s):

Maria Marti-Solano ◽

Ramon Guixa-Gonzalez ◽

Ferran Sanz ◽

Manuel Pastor ◽

Jana Selent

Keyword(s):

Drug Design ◽

G Protein ◽

G Protein Coupled Receptors ◽

Biased Agonism ◽

G Protein Coupled

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Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional

Physical Chemistry Chemical Physics ◽

10.1039/d1cp00206f ◽

2021 ◽

Author(s):

Lennart Gundelach ◽

Christofer S Tautermann ◽

Thomas Fox ◽

Chris-Kriton Skylaris

Keyword(s):

Drug Discovery ◽

Ligand Binding ◽

Dft Calculations ◽

Quantum Mechanical ◽

Free Energies ◽

Ligand Interaction ◽

Exchange Correlation ◽

Ligand Free ◽

Protein Ligand Interaction ◽

Binding Free Energies

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...

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