scholarly journals CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials

2018 ◽  
Author(s):  
Jonathan A Fine ◽  
Janez Konc ◽  
Ram Samudrala ◽  
Gaurav Chopra
Keyword(s):  
Metal Ions ◽  
Binding Pocket ◽  
Binding Mode ◽  
Potential Functions ◽  
Chemical Environment ◽  
Conformational Space ◽  
Binding Affinities ◽  
Flexible Docking ◽  
Docking Algorithm ◽  
Essential Components

Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, improper treatment of the interactions of essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and the inability to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample biologically relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.

scholarly journals CANDOCK: Chemical Atomic Network Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

2018 ◽  
Author(s):  
Jonathan Fine ◽  
Janez Konc ◽  
Ram Samudralal ◽  
Gaurav Chopra
Keyword(s):  
Metal Ions ◽  
Binding Pocket ◽  
Binding Mode ◽  
Potential Functions ◽  
Chemical Environment ◽  
Conformational Space ◽  
Binding Affinities ◽  
Flexible Docking ◽  
Docking Algorithm ◽  
Essential Components

Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, ignoring interactions with essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and they are unable to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample chemical relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.

scholarly journals CANDOCK: Chemical Atomic Network Based Hierarchical Flexible Docking Algorithm Using Generalized Statistical Potentials

2018 ◽  
Author(s):  
Jonathan Fine ◽  
Janez Konc ◽  
Ram Samudralal ◽  
Gaurav Chopra
Keyword(s):  
Metal Ions ◽  
Binding Pocket ◽  
Binding Mode ◽  
Potential Functions ◽  
Chemical Environment ◽  
Conformational Space ◽  
Binding Affinities ◽  
Flexible Docking ◽  
Docking Algorithm ◽  
Essential Components

Small molecule docking has proven to be invaluable for drug design and discovery. However, existing docking methods have several limitations, such as, ignoring interactions with essential components in the chemical environment of the binding pocket (e.g. cofactors, metal-ions, etc.), incomplete sampling of chemically relevant ligand conformational space, and they are unable to consistently correlate docking scores of the best binding pose with experimental binding affinities. We present CANDOCK, a novel docking algorithm that utilizes a hierarchical approach to reconstruct ligands from an atomic grid using graph theory and generalized statistical potential functions to sample chemical relevant ligand conformations. Our algorithm accounts for protein flexibility, solvent, metal ions and cofactors interactions in the binding pocket that are traditionally ignored by current methods. We evaluate the algorithm on the PDBbind and Astex proteins to show its ability to reproduce the binding mode of the ligands that is independent of the initial ligand conformation in these benchmarks. Finally, we identify the best selector and ranker potential functions, such that, the statistical score of best selected docked pose correlates with the experimental binding affinities of the ligands for any given protein target. Our results indicate that CANDOCK is a generalized flexible docking method that addresses several limitations of current docking methods by considering all interactions in the chemical environment of a binding pocket for correlating the best docked pose with biological activity.

scholarly journals Exploring aromatic cage flexibility of the histone methyllysine reader protein Spindlin1 and its impact on binding mode prediction: an in silico study

2021 ◽  
Author(s):  
Chiara Luise ◽  
Dina Robaa ◽  
Wolfgang Sippl
Keyword(s):  
Molecular Dynamic ◽  
In Silico ◽  
Binding Pocket ◽  
Binding Mode ◽  
Molecular Dynamic Simulations ◽  
Flexible Docking ◽  
Dynamic Simulations ◽  
Binding Mode Prediction ◽  
Aromatic Cage ◽  
The Right

AbstractSome of the main challenges faced in drug discovery are pocket flexibility and binding mode prediction. In this work, we explored the aromatic cage flexibility of the histone methyllysine reader protein Spindlin1 and its impact on binding mode prediction by means of in silico approaches. We first investigated the Spindlin1 aromatic cage plasticity by analyzing the available crystal structures and through molecular dynamic simulations. Then we assessed the ability of rigid docking and flexible docking to rightly reproduce the binding mode of a known ligand into Spindlin1, as an example of a reader protein displaying flexibility in the binding pocket. The ability of induced fit docking was further probed to test if the right ligand binding mode could be obtained through flexible docking regardless of the initial protein conformation. Finally, the stability of generated docking poses was verified by molecular dynamic simulations. Accurate binding mode prediction was obtained showing that the herein reported approach is a highly promising combination of in silico methods able to rightly predict the binding mode of small molecule ligands in flexible binding pockets, such as those observed in some reader proteins.

Clustering and Sampling of the c-Met Conformational Space: A Computational Drug Discovery Study

2020 ◽  
Vol 22 (9) ◽  
pp. 635-648 ◽  
Author(s):  
Korosh Mashayekh ◽  
Shahrzad Sharifi ◽  
Tahereh Damghani ◽  
Maryam Elyasi ◽  
Mohammad S. Avestan ◽  
...  
Keyword(s):  
Critical Role ◽  
Scientific Work ◽  
Binding Mode ◽  
Docking Studies ◽  
Conformational Space ◽  
Hydrogen Bond Interactions ◽  
Docking Program ◽  
Dynamics Simulations ◽  
Computational Drug Discovery ◽  
Dynamic Ensembles

Background: c-Met kinase plays a critical role in a myriad of human cancers, and a massive scientific work was devoted to design more potent inhibitors. Objective: In this study, 16 molecular dynamics simulations of different complexes of potent c-Met inhibitors with U-shaped binding mode were carried out regarding the dynamic ensembles to design novel potent inhibitors. Methods: A cluster analysis was performed, and the most representative frame of each complex was subjected to the structure-based pharmacophore screening. The GOLD docking program investigated the interaction energy and pattern of output hits from the virtual screening. The most promising hits with the highest scoring values that showed critical interactions with c-Met were presented for ADME/Tox analysis. Results: The screening yielded 45,324 hits that all of them were subjected to the docking studies and 10 of them with the highest-scoring values having diverse structures were presented for ADME/Tox analyses. Conclusion: The results indicated that all the hits shared critical Pi-Pi stacked and hydrogen bond interactions with Tyr1230 and Met1160 respectively.

scholarly journals Crystal structure and RNA-binding properties of an Hfq homolog from the deep-branching Aquificae: conservation of the lateral RNA-binding mode

2017 ◽  
Vol 73 (4) ◽  
pp. 294-315 ◽  
Author(s):  
Kimberly A. Stanek ◽  
Jennifer Patterson-West ◽  
Peter S. Randolph ◽  
Cameron Mura
Keyword(s):  
Rna Binding ◽  
Binding Pocket ◽  
Binding Mode ◽  
Evolutionary Conservation ◽  
Aquifex Aeolicus ◽  
Binding Properties ◽  
Bacterial Phyla ◽  
Mrna Targets ◽  
Small Regulatory Rnas ◽  
And Function

The host factor Hfq, as the bacterial branch of the Sm family, is an RNA-binding protein involved in the post-transcriptional regulation of mRNA expression and turnover. Hfq facilitates pairing between small regulatory RNAs (sRNAs) and their corresponding mRNA targets by binding both RNAs and bringing them into close proximity. Hfq homologs self-assemble into homo-hexameric rings with at least two distinct surfaces that bind RNA. Recently, another binding site, dubbed the `lateral rim', has been implicated in sRNA·mRNA annealing; the RNA-binding properties of this site appear to be rather subtle, and its degree of evolutionary conservation is unknown. An Hfq homolog has been identified in the phylogenetically deep-branching thermophileAquifex aeolicus(Aae), but little is known about the structure and function of Hfq from basal bacterial lineages such as the Aquificae. Therefore,AaeHfq was cloned, overexpressed, purified, crystallized and biochemically characterized. Structures ofAaeHfq were determined in space groupsP1 andP6, both to 1.5 Å resolution, and nanomolar-scale binding affinities for uridine- and adenosine-rich RNAs were discovered. Co-crystallization with U6RNA reveals that the outer rim of theAaeHfq hexamer features a well defined binding pocket that is selective for uracil. ThisAaeHfq structure, combined with biochemical and biophysical characterization of the homolog, reveals deep evolutionary conservation of the lateral RNA-binding mode, and lays a foundation for further studies of Hfq-associated RNA biology in ancient bacterial phyla.

scholarly journals The Crystal Structure of Shikimate Dehydrogenase (AroE) Reveals a Unique NADPH Binding Mode

2003 ◽  
Vol 185 (14) ◽  
pp. 4144-4151 ◽  
Author(s):  
Sheng Ye ◽  
Frank von Delft ◽  
Alexei Brooun ◽  
Mark W. Knuth ◽  
Ronald V. Swanson ◽  
...  
Keyword(s):  
Catalytic Domain ◽  
Binding Pocket ◽  
Binding Mode ◽  
Sequence Motif ◽  
Binary Complex ◽  
Shikimate Dehydrogenase ◽  
Conserved Residues ◽  
Conserved Sequence ◽  
Nicotinamide Mononucleotide ◽  
Reversible Reduction

ABSTRACT Shikimate dehydrogenase catalyzes the NADPH-dependent reversible reduction of 3-dehydroshikimate to shikimate. We report the first X-ray structure of shikimate dehydrogenase from Haemophilus influenzae to 2.4-Å resolution and its complex with NADPH to 1.95-Å resolution. The molecule contains two domains, a catalytic domain with a novel open twisted α/β motif and an NADPH binding domain with a typical Rossmann fold. The enzyme contains a unique glycine-rich P-loop with a conserved sequence motif, GAGGXX, that results in NADPH adopting a nonstandard binding mode with the nicotinamide and ribose moieties disordered in the binary complex. A deep pocket with a narrow entrance between the two domains, containing strictly conserved residues primarily contributed by the catalytic domain, is identified as a potential 3-dehydroshikimate binding pocket. The flexibility of the nicotinamide mononucleotide portion of NADPH may be necessary for the substrate 3-dehydroshikimate to enter the pocket and for the release of the product shikimate.

scholarly journals In Silico Screening for Natural Ligands to Non-Structural Nsp7 Conformers of Coronaviruses

2020 ◽  
Author(s):  
Rafael Blasco ◽  
Julio Coll
Keyword(s):  
In Silico ◽  
Chemical Properties ◽  
Structural Similarity ◽  
Binding Pocket ◽  
Binding Affinities ◽  
Structural Protein ◽  
Autodock Vina ◽  
Natural Ligands ◽  
Final Optimization

<p>The non-structural protein 7 (nsp7) of Severe Acute Respiratory Syndrome (SARS) coronaviruses was selected as a new target to potentially interfere with viral replication. The nsp7s are one of the most conserved, unique and small coronavirus proteins having a critical, yet intriguing participation on the replication of the long viral RNA genome after complexing with nsp8 and nsp12. Despite the difficulties of having no previous binding pocket, two high-throughput virtual blind screening of 158240 natural compounds > 400 Da by AutoDock Vina against nsp7.1ysy identified 655 leads displaying predicted binding affinities between 10 to 1100 nM. The leads were then screened against 14 available conformations of nsp7 by both AutoDock Vina and seeSAR programs employing different binding score algorithms, to identify 20 consensus top-leads. Further <i>in silico</i> predictive analysis of physiological and toxicity ADMET criteria (chemical properties, adsorption, metabolism, toxicity) narrowed top-leads to a few drug-like ligands many of them showing steroid-like structures. A final optimization by search for structural similarity to the top drug-like ligand that were also commercially available, yielded a collection of predicted novel ligands with ~100-fold higher-affinity whose antiviral activity may be experimentally validated. Additionally, these novel nsp7-interacting ligands and/or their further optimized derivatives, may offer new tools to investigate the intriguing role of nsp7 on replication of coronaviruses.</p>

scholarly journals Pharmacophore Modelling-Based Drug Repurposing Approaches for SARS-CoV-2 Therapeutics

2021 ◽  
Vol 9 ◽  
Author(s):  
Shailima Rampogu ◽  
Keun Woo Lee
Keyword(s):  
Effective Treatment ◽  
Pharmacophore Model ◽  
Drug Repurposing ◽  
Binding Pocket ◽  
Binding Mode ◽  
Computational Method ◽  
Dynamics Simulation ◽  
Potential Candidate ◽  
Pharmacophore Modelling ◽  
Discovery Studio

The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a devastating effect globally with no effective treatment. The swift strategy to find effective treatment against coronavirus disease 2019 (COVID-19) is to repurpose the approved drugs. In this pursuit, an exhaustive computational method has been used on the DrugBank compounds targeting nsp16/nsp10 complex (PDB code: 6W4H). A structure-based pharmacophore model was generated, and the selected model was escalated to screen DrugBank database, resulting in three compounds. These compounds were subjected to molecular docking studies at the protein-binding pocket employing the CDOCKER module available with the Discovery Studio v18. In order to discover potential candidate compounds, the co-crystallized compound S-adenosyl methionine (SAM) was used as the reference compound. Additionally, the compounds remdesivir and hydroxycholoroquine were employed for comparative docking. The results have shown that the three compounds have demonstrated a higher dock score than the reference compounds and were upgraded to molecular dynamics simulation (MDS) studies. The MDS results demonstrated that the three compounds, framycetin, kanamycin, and tobramycin, are promising candidate compounds. They have represented a stable binding mode at the targets binding pocket with an average protein backbone root mean square deviation below 0.3 nm. Additionally, they have prompted the hydrogen bonds during the entire simulations, inferring that the compounds have occupied the active site firmly. Taken together, our findings propose framycetin, kanamycin, and tobramycin as potent putative inhibitors for COVID-19 therapeutics.

scholarly journals STUDY OF MAO-B INHIBITOR ANALOUGES FOR PARKINSON’S DISEASE THROUGH CADD APPROACHES

2018 ◽  
Vol 8 (5-s) ◽  
pp. 240-250
Author(s):  
Manish Bachhar ◽  
BK Singh
Keyword(s):  
Molecular Design ◽  
Neurodegenerative Disorder ◽  
Binding Mode ◽  
Docking Studies ◽  
Target Protein ◽  
Binding Affinities ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Mao B ◽  
Pharmacokinetic Properties

New derivatives are designed as target directed MAO-B Inhibitors for medical care of the patients for neurodegenerative disorder. Molecular design and estimated pharmacokinetic properties have been evaluated by using Inventus v 1.1 software. The binding mode of the proposed compounds with target protein i.e. 1S2Q was evaluated and the resulting data from docking studies explained that newly designed derivatives have high and better affinity towards target protein. Based on these properties, the binding affinities are used for speeding up drug discovery process by eliminating less potent compounds from synthesis. Keywords: MAO-B, Inventus, Target protein, Neurodegenerative, Docking.

scholarly journals A Complete Assessment of Dopamine Receptor-Ligand Interactions through Computational Methods

2019 ◽  
Author(s):  
Beatriz Bueschbell ◽  
Carlos A.V. Barreto ◽  
Antonio J. Preto ◽  
Anke C. Schiedel ◽  
Irina S. Moreira
Keyword(s):  
Dopamine Receptor ◽  
Electrostatic Interactions ◽  
Specific Binding ◽  
Binding Pocket ◽  
Binding Mode ◽  
Receptor Subtypes ◽  
Receptor Ligand ◽  
Pi Stacking ◽  
Ligand Interactions ◽  
New Treatments

Background: Selectively targeting dopamine receptors has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases evolving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated. Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D1-like and D2-like receptors). Fifteen structurally diverse ligands were docked to these models. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for DR sub-type specificity. Results: We showed that the number of conformations for a receptor:ligand complex was associated to unspecific interactions &gt; 2.5 &Aring; and hydrophobic contacts, while the decoys binding energy was influenced by specific electrostatic interactions. Known residues such as 3.32Asp, the serine microdomain and the aromatic microdomain were found interacting in a variety of modes (HB, SB, &pi;-stacking). Purposed TM2-TM3-TM7 microdomain was found to form a hydrophobic network involving Orthosteric Binding Pocket (OBP) and Secondary Binding Pocket (SBP). T-stacking interactions revealed as especially relevant for some large ligands such as apomorphine, risperidone or aripiprazole. Conclusions: This in silico approach was successful in showing known receptor-ligand interactions as well as in determining unique combinations of interactions, key for the design of more specific ligands.

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