scholarly journals Flavonoids as Putative epi-modulators: Insights into their Binding Mode with BRD4 Bromodomain using Molecular Docking and Dynamics.

2018 ◽  
Author(s):  
Fernando Daniel Prieto-Martínez ◽  
José Luis Medina-Franco
Keyword(s):  
Kinase Inhibitors ◽  
Binding Mode ◽  
Ligand Interaction ◽  
Bet Inhibitors ◽  
Protein Ligand Interaction ◽  
Brd4 Inhibition ◽  
Molecular Dynamics Results ◽  
Molecular Docking And Dynamics ◽  
Micromolar Range

Flavonoids are widely recognized as natural polydrugs, given their anti-inflammatory, antioxidant, sedative and antineoplastic activities. Recently, different studies have shown that flavonoid have the potential to inhibit BET bromodomains. Previous reports suggest that flavonoids are putative inhibitors of the ZA channel due to their orientation and interactions with P86, V87, L92, L94 and N140. Herein, a comprehensive characterization of the binding mode of the biflavonoid amentoflavone and fisetin is discussed. To this end, both compounds were docked with BRD4 using four docking programs. Results were post-processed with protein-ligand interaction fingerprints. To gain further insights into the binding mode of the two natural products, docking results were further analyzed with molecular dynamics. Results showed that amentoflavone makes numerous contacts in the ZA channel, as previously described for flavonoids and kinase inhibitors. It was also found that amentoflavone can potentially make contacts with non-canonical residues for BET inhibition. Most of these contacts were not observed with fisetin. Based on these results, amentoflavone was tested for BRD4 inhibition, showing activity in the micromolar range. This work may serve as basis for scaffold optimization and further characterization of flavonoids as BET inhibitors.

scholarly journals Flavonoids as Putative Epi-Modulators: Insight into Their Binding Mode with BRD4 Bromodomains Using Molecular Docking and Dynamics

Biomolecules ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 61 ◽  
Author(s):  
Fernando Prieto-Martínez ◽  
José Medina-Franco
Keyword(s):  
Kinase Inhibitors ◽  
Binding Mode ◽  
Binding Modes ◽  
Ligand Interaction ◽  
Bet Inhibitors ◽  
Protein Ligand Interaction ◽  
Brd4 Inhibition ◽  
Dynamics Simulations ◽  
Insight Into

Flavonoids are widely recognized as natural polydrugs, given their anti-inflammatory, antioxidant, sedative, and antineoplastic activities. Recently, different studies showed that flavonoids have the potential to inhibit bromodomain and extraterminal (BET) bromodomains. Previous reports suggested that flavonoids bind between the Z and A loops of the bromodomain (ZA channel) due to their orientation and interactions with P86, V87, L92, L94, and N140. Herein, a comprehensive characterization of the binding modes of fisetin and the biflavonoid, amentoflavone, is discussed. To this end, both compounds were docked with BET bromodomain 4 (BRD4) using four docking programs. The results were post-processed with protein–ligand interaction fingerprints. To gain further insight into the binding mode of the two natural products, the docking results were further analyzed with molecular dynamics simulations. The results showed that amentoflavone makes numerous contacts in the ZA channel, as previously described for flavonoids and kinase inhibitors. It was also found that amentoflavone can potentially make contacts with non-canonical residues for BET inhibition. Most of these contacts were not observed with fisetin. Based on these results, amentoflavone was experimentally tested for BRD4 inhibition, showing activity in the micromolar range. This work may serve as the basis for scaffold optimization and the further characterization of flavonoids as BET inhibitors.

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>

High-resolution crystal structure of Trypanosoma brucei pteridine reductase 1 in complex with an innovative tricyclic-based inhibitor

2020 ◽  
Vol 76 (6) ◽  
pp. 558-564
Author(s):  
Giacomo Landi ◽  
Pasquale Linciano ◽  
Giusy Tassone ◽  
Maria Paola Costi ◽  
Stefano Mangani ◽  
...  
Keyword(s):  
Trypanosoma Brucei ◽  
Neglected Tropical Diseases ◽  
Protozoan Parasite ◽  
Binding Mode ◽  
New Class ◽  
Hydrogen Bond Interactions ◽  
Promising Strategy ◽  
Trypanosomatid Parasites ◽  
Micromolar Range

The protozoan parasite Trypanosoma brucei is the etiological agent of human African trypanosomiasis (HAT). HAT, together with other neglected tropical diseases, causes serious health and economic issues, especially in tropical and subtropical areas. The classical antifolates targeting dihydrofolate reductase (DHFR) are ineffective towards trypanosomatid parasites owing to a metabolic bypass by the expression of pteridine reductase 1 (PTR1). The combined inhibition of PTR1 and DHFR activities in Trypanosoma parasites represents a promising strategy for the development of new effective treatments for HAT. To date, only monocyclic and bicyclic aromatic systems have been proposed as inhibitors of T. brucei PTR1 (TbPTR1); nevertheless, the size of the catalytic cavity allows the accommodation of expanded molecular cores. Here, an innovative tricyclic-based compound has been explored as a TbPTR1-targeting molecule and its potential application for the development of a new class of PTR1 inhibitors has been evaluated. 2,4-Diaminopyrimido[4,5-b]indol-6-ol (1) was designed and synthesized, and was found to be effective in blocking TbPTR1 activity, with a K i in the low-micromolar range. The binding mode of 1 was clarified through the structural characterization of its ternary complex with TbPTR1 and the cofactor NADP(H), which was determined to 1.30 Å resolution. The compound adopts a substrate-like orientation inside the cavity that maximizes the binding contributions of hydrophobic and hydrogen-bond interactions. The binding mode of 1 was compared with those of previously reported bicyclic inhibitors, providing new insights for the design of innovative tricyclic-based molecules targeting TbPTR1.

scholarly journals Estimation of the protein–ligand interaction energy for model building and validation

2017 ◽  
Vol 73 (3) ◽  
pp. 195-202 ◽  
Author(s):  
Daria A. Beshnova ◽  
Joana Pereira ◽  
Victor S. Lamzin
Keyword(s):  
Model Building ◽  
Data Bank ◽  
Binding Mode ◽  
Automated Identification ◽  
Ligand Interaction ◽  
New Approach ◽  
X Ray Crystallography ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Protein Ligand Interaction

Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein–ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein–ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site withARP/wARP. The applicability of the method to the validation of protein–ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.

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