PrankWeb: a web server for ligand binding site prediction and visualization

Jendele, Lukáš; Krivák, Radoslav; Škoda, Petr; Novotný, Marián; Hoksza, David

doi:10.1093/nar/gkz424

Cited by 262 publications

(200 citation statements)

References 35 publications

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“…This binding site of NSP6 is encompassed by helices H3 (residues 61-69), turn and coil region (residues 126-133) joining H6 and H7, H8-H9 (residues 170-179), and C-terminal H11-H12-β1 (residues 221-244) (Supplementary Figure S2 ). We have used the other two web-servers, PrankWeb [ 54 ] and COACH [ 55 ], for predicting the binding sites. These two servers also predict similar residues in the binding pocket, with certain changes.…”

Section: Resultsmentioning

confidence: 99%

Insights into the biased activity of dextromethorphan and haloperidol towards SARS-CoV-2 NSP6: in silico binding mechanistic analysis

et al. 2020

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The outbreak of novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus continually led to infect a large population worldwide. SARS-CoV-2 utilizes its NSP6 and Orf9c proteins to interact with sigma receptors that are implicated in lipid remodeling and ER stress response, to infect cells. The drugs targeting the sigma receptors, sigma-1 and sigma-2, have emerged as effective candidates to reduce viral infectivity, and some of them are in clinical trials against COVID-19. The antipsychotic drug, haloperidol, exerts remarkable antiviral activity, but, at the same time, the sigma-1 benzomorphan agonist, dextromethorphan, showed pro-viral activity. To explore the potential mechanisms of biased binding and activity of the two drugs, haloperidol and dextromethorphan towards NSP6, we herein utilized molecular docking–based molecular dynamics simulation studies. Our extensive analysis of the protein-drug interactions, structural and conformational dynamics, residual frustrations, and molecular switches of NSP6-drug complexes indicates that dextromethorphan binding leads to structural destabilization and increase in conformational dynamics and energetic frustrations. On the other hand, the strong binding of haloperidol leads to minimal structural and dynamical perturbations to NSP6. Thus, the structural insights of stronger binding affinity and favorable molecular interactions of haloperidol towards viral NSP6 suggests that haloperidol can be potentially explored as a candidate drug against COVID-19. Key messages •Inhibitors of sigma receptors are considered as potent drugs against COVID-19. •Antipsychotic drug, haloperidol, binds strongly to NSP6 and induces the minimal changes in structure and dynamics of NSP6. •Dextromethorphan, agonist of sigma receptors, binding leads to overall destabilization of NSP6. •These two drugs bind with NSP6 differently and also induce differences in the structural and conformational changes that explain their different mechanisms of action. •Haloperidol can be explored as a candidate drug against COVID-19. Electronic supplementary material The online version of this article (10.1007/s00109-020-01980-1) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Insights into the biased activity of dextromethorphan and haloperidol towards SARS-CoV-2 NSP6: in silico binding mechanistic analysis

et al. 2020

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“…The S2 domain portion of the protein was extracted to create a new protein entry from Val736 to Gln1071 to sustain the stability within the confined region and was again minimized and superimposed with the native structure with PDB accession number 6VXX for protein structure validation. To assess the binding of FDA-approved drugs with the fusion peptide, a mixed rationale of binding site prediction was employed using the sequence range of previous SARS-CoV FP sequence positions, PrankWeb scores, and the Sitemap web server (22,23). Receptor coordinates for structure-guided docking were created at the mean position for all the top-scoring pocket residues from PrankWeb and Sitemap.…”

Section: Figmentioning

confidence: 99%

In Silico Structure-Based Repositioning of Approved Drugs for Spike Glycoprotein S2 Domain Fusion Peptide of SARS-CoV-2: Rationale from Molecular Dynamics and Binding Free Energy Calculations

et al. 2020

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The membrane-anchored spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a pivotal role in directing the fusion of the virus particle mediated by the host cell receptor angiotensin-converting enzyme 2 (ACE-2). The fusion peptide region of the S protein S2 domain provides SARS-CoV-2 with the biological machinery needed for direct fusion to the host lipid membrane. In our present study, computer-aided drug design strategies were used for the identification of FDA-approved small molecules using the optimal structure of the S2 domain, which exhibits optimal interaction ratios, structural features, and energy variables, which were evaluated based on their performances in molecular docking, molecular dynamics simulations, molecular mechanics/generalized Born model and solvent accessibility binding free energy calculations of molecular dynamics trajectories, and statistical inferences. Among the 2,625 FDA-approved small molecules, chloramphenicol succinate, imipenem, and imidurea turned out to be the molecules that bound the best at the fusion peptide hydrophobic pocket. The principal interactions of the selected molecules suggest that the potential binding site at the fusion peptide region is centralized amid the Lys790, Thr791, Lys795, Asp808, and Gln872 residues. IMPORTANCE The present study provides the structural identification of the viable binding residues of the SARS-CoV-2 S2 fusion peptide region, which holds prime importance in the virus’s host cell fusion and entry mechanism. The classical molecular mechanics simulations were set on values that mimic physiological standards for a good approximation of the dynamic behavior of selected drugs in biological systems. The drug molecules screened and analyzed here have relevant antiviral properties, which are reported here and which might hint toward their utilization in the coronavirus disease 2019 (COVID-19) pandemic owing to their attributes of binding to the fusion protein binding region shown in this study.

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“…The known pocket and near-native pocket are defined by the same method as above. The proteins in the extra test set A and B were subjected to the prediction by the P2Rank with its default parameters for comparison (Krivák & Hoksza, 2018;Jendele et al, 2019).…”

Section: Preparation Of Extra Test Setsmentioning

confidence: 99%

DeepBindPoc: a deep learning method to rank ligand binding pockets using molecular vector representation

Zhang

Saravanan

Lin

et al. 2020

PeerJ

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Accurate identification of ligand-binding pockets in a protein is important for structure-based drug design. In recent years, several deep learning models were developed to learn important physical–chemical and spatial information to predict ligand-binding pockets in a protein. However, ranking the native ligand binding pockets from a pool of predicted pockets is still a hard task for computational molecular biologists using a single web-based tool. Hence, we believe, by using closer to real application data set as training and by providing ligand information, an enhanced model to identify accurate pockets can be obtained. In this article, we propose a new deep learning method called DeepBindPoc for identifying and ranking ligand-binding pockets in proteins. The model is built by using information about the binding pocket and associated ligand. We take advantage of the mol2vec tool to represent both the given ligand and pocket as vectors to construct a densely fully connected layer model. During the training, important features for pocket-ligand binding are automatically extracted and high-level information is preserved appropriately. DeepBindPoc demonstrated a strong complementary advantage for the detection of native-like pockets when combined with traditional popular methods, such as fpocket and P2Rank. The proposed method is extensively tested and validated with standard procedures on multiple datasets, including a dataset with G-protein Coupled receptors. The systematic testing and validation of our method suggest that DeepBindPoc is a valuable tool to rank near-native pockets for theoretically modeled protein with unknown experimental active site but have known ligand. The DeepBindPoc model described in this article is available at GitHub (https://github.com/haiping1010/DeepBindPoc) and the webserver is available at (http://cbblab.siat.ac.cn/DeepBindPoc/index.php).

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PrankWeb: a web server for ligand binding site prediction and visualization

Cited by 262 publications

References 35 publications

Insights into the biased activity of dextromethorphan and haloperidol towards SARS-CoV-2 NSP6: in silico binding mechanistic analysis

Insights into the biased activity of dextromethorphan and haloperidol towards SARS-CoV-2 NSP6: in silico binding mechanistic analysis

In Silico Structure-Based Repositioning of Approved Drugs for Spike Glycoprotein S2 Domain Fusion Peptide of SARS-CoV-2: Rationale from Molecular Dynamics and Binding Free Energy Calculations

DeepBindPoc: a deep learning method to rank ligand binding pockets using molecular vector representation

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