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.

scholarly journals PyPLIF-ASSISTED REDOCKING INDOMETHACIN-(R)-ALPHA-ETHYL-ETHANOLAMIDE INTO CYCLOOXYGENASE-1

2013 ◽  
Vol 13 (3) ◽  
pp. 283-286 ◽  
Author(s):  
Muhammad Radifar ◽  
Nunung Yuniarti ◽  
Enade Perdana Istyastono
Keyword(s):  
Initial Step ◽  
Binding Pocket ◽  
Scoring Functions ◽  
Amino Acid Residues ◽  
Ligand Interaction ◽  
Cyclooxygenase 1 ◽  
External Software ◽  
Protein Ligand Interaction ◽  
Interaction Fingerprints ◽  
Cox 1

Identification of Protein-Ligand Interaction Fingerprints (PLIF) has been performed as the rescoring strategy to identify the best pose for the docked poses of indomethacin-(R)-α-ethyl-etanolamide (IMM) in the binding site of cyclooxygenase-1 (COX-1) from simulations using PLANTS molecular docking software version 1.2 (PLANTS1.2). Instead of using the scoring functions included in the docking software, the strategy presented in this article used external software called PyPLIF that could identify the interactions of the ligand to the amino acid residues in the binding pocket and presents them as binary bitstrings, which subsequently were compared to the interaction bitstrings of the co-crystal ligand pose. The results show that PyPLIF-assisted redocking strategy could select the correct pose much better compared to the pose selection without rescoring. Out of 1000 iterative attempts, PyPLIF-assisted redocking simulations could identify 971 correct poses (more than 95%), while the redocking simulations without PyPLIF could only identify 500 correct poses (50%).These works have also provided us with the initial step of the construction of a valid Structure-Based Virtual Screening (SBVS) protocol to identify COX-1 inhibitors.

Spiro-acridine inhibiting tyrosinase enzyme: Kinetic, protein-ligand interaction and molecular docking studies

2019 ◽  
Vol 122 ◽  
pp. 289-297 ◽  
Author(s):  
Thaís Meira Menezes ◽  
Sinara Mônica Vitalino de Almeida ◽  
Ricardo Olímpio de Moura ◽  
Gustavo Seabra ◽  
Maria do Carmo Alves de Lima ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Docking Studies ◽  
Enzyme Kinetic ◽  
Ligand Interaction ◽  
Molecular Docking Studies ◽  
Protein Ligand Interaction ◽  
Tyrosinase Enzyme

scholarly journals SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses

2019 ◽  
Author(s):  
Susan Leung ◽  
Michael Bodkin ◽  
Frank von Delft ◽  
Paul Brennan ◽  
Garrett Morris
Keyword(s):  
Binding Mode ◽  
Docking Studies ◽  
Binding Modes ◽  
False Negatives ◽  
Ligand Interaction ◽  
Cross Docking ◽  
Protein Ligand Interaction ◽  
Feature Overlap ◽  
Better Than ◽  
Fragment Based Drug Discovery

One of the fundamental assumptions of fragment-based drug discovery is that the fragment’s binding mode will be conserved upon elaboration into larger compounds. The most common way of quantifying binding mode similarity is Root Mean Square Deviation (RMSD), but Protein Ligand Interaction Fingerprint (PLIF) similarity and shape-based metrics are sometimes used. We introduce SuCOS, an open-source shape and chemical feature overlap metric. We explore the strengths and weaknesses of RMSD, PLIF similarity, and SuCOS on a dataset of X-ray crystal structures of paired elaborated larger and smaller molecules bound to the same protein. Our redocking and cross-docking studies show that SuCOS is superior to RMSD and PLIF similarity. When redocking, SuCOS produces fewer false positives and false negatives than RMSD and PLIF similarity; and in cross-docking, SuCOS is better at differentiating experimentally-observed binding modes of an elaborated molecule given the pose of its non-elaborated counterpart. Finally we show that SuCOS performs better than AutoDock Vina at differentiating actives from decoy ligands using the DUD-E dataset. SuCOS is available at https://github.com/susanhleung/SuCOS . <br>

scholarly journals SuCOS is Better than RMSD for Evaluating Fragment Elaboration and Docking Poses

2019 ◽  
Author(s):  
Susan Leung ◽  
Michael Bodkin ◽  
Frank von Delft ◽  
Paul Brennan ◽  
Garrett Morris
Keyword(s):  
Binding Mode ◽  
Docking Studies ◽  
Binding Modes ◽  
False Negatives ◽  
Ligand Interaction ◽  
Cross Docking ◽  
Protein Ligand Interaction ◽  
Feature Overlap ◽  
Better Than ◽  
Fragment Based Drug Discovery

One of the fundamental assumptions of fragment-based drug discovery is that the fragment’s binding mode will be conserved upon elaboration into larger compounds. The most common way of quantifying binding mode similarity is Root Mean Square Deviation (RMSD), but Protein Ligand Interaction Fingerprint (PLIF) similarity and shape-based metrics are sometimes used. We introduce SuCOS, an open-source shape and chemical feature overlap metric. We explore the strengths and weaknesses of RMSD, PLIF similarity, and SuCOS on a dataset of X-ray crystal structures of paired elaborated larger and smaller molecules bound to the same protein. Our redocking and cross-docking studies show that SuCOS is superior to RMSD and PLIF similarity. When redocking, SuCOS produces fewer false positives and false negatives than RMSD and PLIF similarity; and in cross-docking, SuCOS is better at differentiating experimentally-observed binding modes of an elaborated molecule given the pose of its non-elaborated counterpart. Finally we show that SuCOS performs better than AutoDock Vina at differentiating actives from decoy ligands using the DUD-E dataset. SuCOS is available at https://github.com/susanhleung/SuCOS . <br>

Docking, Binding Free Energy Estimation, and MD Simulation of Newly Designed CQ and HCQ Analogues Against the Spike-ACE2 Complex of SARS-CoV-2

2021 ◽  
Vol 6 (4) ◽  
pp. 77-89
Author(s):  
Anjoomaara H. Patel ◽  
Riya B. Patel ◽  
MahammadHussain J. Memon ◽  
Samiya S. Patel ◽  
Sharav A. Desai ◽  
...  
Keyword(s):  
Free Energy ◽  
Md Simulation ◽  
Binding Free Energy ◽  
Docking Studies ◽  
Radius Of Gyration ◽  
Ligand Interaction ◽  
Energy Estimation ◽  
Efficacious Treatment ◽  
Protein Ligand Interaction ◽  
Derivatives Of

The coronavirus disease 2019 (COVID-19) virus has been spreading rapidly, and scientists are endeavouring to discover drugs for its efficacious treatment. Chloroquine phosphate, an old drug for treatment of malaria, has shown to have apparent efficacy and acceptable safety against COVID-19. As a part of Drug Discovery Hackathon-2020, in this study, the authors have tried making the derivatives of CQ and HCQ using MarvinSketch by ChemAxon. Molecular docking studies of these ligands were performed using Glide by Schrodinger, and ADME profiles were obtained by using QikProp. The obtained results after data analysis demonstrated that ligands HCQ_imidazoll, choloroquine_3c, HCQ_pyrrolC had good binding affinity and complied with all the ADME parameters. The molecular dynamic simulation of these ligands in complex with the 2019-nCoV RBD/ACE-2-B0AT1 complex PDB ID: 6M17 were carried out, and the parameters like RMSD, RMSF, and radius of gyration were observed to understand the fluctuations and protein-ligand interaction.

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