scholarly journals Structural Protein–Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study

2014 ◽  
Vol 54 (9) ◽  
pp. 2555-2561 ◽  
Author(s):  
C. Da ◽  
D. Kireev
Keyword(s):  
Virtual Screening ◽  
Screening Method ◽  
Structural Protein ◽  
Ligand Interaction ◽  
Benchmark Study ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints

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.

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.

Predicting Ligand Binding Modes from Neural Networks Trained on Protein–Ligand Interaction Fingerprints

2013 ◽  
Vol 53 (4) ◽  
pp. 763-772 ◽  
Author(s):  
Vladimir Chupakhin ◽  
Gilles Marcou ◽  
Igor Baskin ◽  
Alexandre Varnek ◽  
Didier Rognan
Keyword(s):  
Neural Networks ◽  
Ligand Binding ◽  
Binding Modes ◽  
Ligand Interaction ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints

Protein–ligand interaction fingerprints for accurate prediction of dissociation rates of p38 MAPK Type II inhibitors

2019 ◽  
Vol 11 (2) ◽  
pp. 53-60 ◽  
Author(s):  
Duo Zhang ◽  
Shuheng Huang ◽  
Hu Mei ◽  
MuliadiYeremia Kevin ◽  
Tingting Shi ◽  
...  
Keyword(s):  
P38 Mapk ◽  
Accurate Prediction ◽  
Type Ii ◽  
Ligand Interaction ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints

scholarly journals Identification of Novel PI3Kδ Selective Inhibitors by SVM-Based Multistage Virtual Screening and Molecular Dynamics Simulations

2019 ◽  
Vol 20 (23) ◽  
pp. 6000 ◽  
Author(s):  
Jing-wei Liang ◽  
Shan Wang ◽  
Ming-yang Wang ◽  
Shi-long Li ◽  
Wan-qiu Li ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Screening Method ◽  
Support Vector ◽  
Selective Inhibitors ◽  
Ligand Interaction ◽  
Lipid Kinase ◽  
Isoform Selectivity ◽  
Phosphoinositide 3 Kinase ◽  
Dynamics Simulations ◽  
Novel Scaffold

Phosphoinositide 3 kinase delta (PI3Kδ) is a lipid kinase that has been implicated in a variety of immune mediated disorders. The research on isoform selectivity was crucial for reducing side effects. In the current study, an optimized hierarchical multistage virtual screening method was utilized for screening the PI3Kδ selective inhibitors. The method sequentially applied a support vector machine (SVM), a protein ligand interaction fingerprint (PLIF) pharmacophore, and a molecular docking approach. The evaluation of the validation set showed a high hit rate and a high enrichment factor of 75.1% and 301.66, respectively. This multistage virtual screening method was then utilized to screen the NCI database. From the final hit list, Compound 10 has great potential as the PI3Kδ inhibitor with micromolar inhibition in the PI3Kδ kinase activity assay. This compound also shows selectivity against PI3Kδ kinase. The method combining SVM, pharmacophore, and docking was capable of screening out the compounds with potential PI3Kδ selective inhibitors. Moreover, structural modification of Compound 10 will contribute to investigating the novel scaffold and designing novel PI3Kδ inhibitors.

scholarly journals Analyzing Multitarget Activity Landscapes Using Protein–Ligand Interaction Fingerprints: Interaction Cliffs

2015 ◽  
Vol 55 (2) ◽  
pp. 251-262 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Albert J. Kooistra ◽  
Chris de Graaf ◽  
Andreas Bender ◽  
José L. Medina-Franco
Keyword(s):  
Ligand Interaction ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints

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.

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