An Improved Weighted-Residue Profile Based Method of Using Protein-Ligand Interaction Information in Increasing Hits Selection from Virtual Screening: A Study on Virtual Screening of Human GPCR A2A Receptor Antagonists

2010 ◽  
Vol 29 (11) ◽  
pp. 781-791 ◽  
Author(s):  
Mohammad Shahid ◽  
Vinod Kasam ◽  
Martin Hofmann-Apitius
Keyword(s):  
Virtual Screening ◽  
Receptor Antagonists ◽  
Ligand Interaction ◽  
Interaction Information ◽  
A2a Receptor ◽  
Protein Ligand Interaction

scholarly journals Protein Ligand Interaction Fingerprints

2017 ◽  
pp. 1072-1091
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

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

Molecules ◽  
2021 ◽  
Vol 26 (9) ◽  
pp. 2452
Author(s):  
Enade P. Istyastono ◽  
Nunung Yuniarti ◽  
Vivitri D. Prasasty ◽  
Sudi Mungkasi
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Ligand Binding ◽  
G Protein Coupled Receptors ◽  
Ligand Interaction ◽  
Molecular Determinants ◽  
Protein Ligand Interaction ◽  
Quality Of Structure ◽  
G Protein Coupled

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.

scholarly journals Identification of Novel Adenosine A2A Receptor Antagonists by Virtual Screening

2012 ◽  
Vol 55 (5) ◽  
pp. 1904-1909 ◽  
Author(s):  
Christopher J. Langmead ◽  
Stephen P. Andrews ◽  
Miles Congreve ◽  
James C. Errey ◽  
Edward Hurrell ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Adenosine A2a Receptor ◽  
Receptor Antagonists ◽  
Adenosine A2a Receptor Antagonists ◽  
A2a Receptor ◽  
Adenosine A2a

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

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
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.

Protein Ligand Interaction Fingerprints

2016 ◽  
pp. 128-147
Author(s):  
Ali HajiEbrahimi ◽  
Hamidreza Ghafouri ◽  
Mohsen Ranjbar ◽  
Amirhossein Sakhteman
Keyword(s):  
Virtual Screening ◽  
Docking Studies ◽  
Scoring Functions ◽  
Ligand Interaction ◽  
Advantages And Disadvantages ◽  
Protein Affinity ◽  
Binding Cavity ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Activity Information

A most challenging part in docking-based virtual screening is the scoring functions implemented in various docking programs in order to evaluate different poses of the ligands inside the binding cavity of the receptor. Precise and trustable measurement of ligand-protein affinity for Structure-Based Virtual Screening (SB-VS) is therefore, an outstanding problem in docking studies. Empirical post-docking filters can be helpful as a way to provide various types of structure-activity information. Different types of interaction have been presented between the ligands and the receptor so far. Based on the diversity and importance of PLIF methods, this chapter will focus on the comparison of different protocols. The advantages and disadvantages of all methods will be discussed explicitly in this chapter as well as future sights for further progress in this field. Different classifications approaches for the protein-ligand interaction fingerprints were also discussed in this chapter.

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