The VEGA suite of programs: an versatile platform for cheminformatics and drug design projects

Pedretti, Alessandro; Mazzolari, Angelica; Gervasoni, Silvia; Fumagalli, Laura; Vistoli, Giulio

doi:10.1093/bioinformatics/btaa774

Cited by 132 publications

(90 citation statements)

References 14 publications

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“…Before descriptor calculation, the 3D structure of the collected molecules was optimized by applying a protocol of standardization, as follows: (i) the protonation state was simulated as compatible with the physiological condition (pH = 7.4) and tautomers were standardized by using the specific tool in the Maestro package, version 9.8.016 (Schrödinger Release 2016-4: Maestro, Schrödinger, LLC, New York, NY, 2016, USA); (ii) the conformational behavior was explored by a clustered procedure based on the Monte Carlo algorithms as implemented in the VEGA ZZ software [25], which generates 1000 conformers by randomly rotating the rotors and selects the lowest energy conformer; (iii) the 3D structures were refined by the semi-empirical method MOPAC 2016, [26] using the Hamiltonian operator PM7 [27] and preserving the existing chirality.…”

Section: Descriptorsmentioning

confidence: 99%

MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

et al. 2021

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(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.

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Section: Descriptorsmentioning

confidence: 99%

MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

et al. 2021

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“…In detail, Table S1 reveals the remarkable role played by the XScore [24] and PLANTS [25] scoring functions (the latter here comprising also the primary scores). Table S1 also evidences the interesting role played by the scores encoding for non-polar interactions as exemplified by molecular lipophilic potential (MLP) [26] and the VEGA-based scores [27], which here correspond to the Lennard-Jones interaction energies as computed by using the CHARMM and CVFF force fields. Concerning the type of score values (in the models including the space descriptors), the best values play a prevailing role, while spread and mean values have a lesser and rather similar incidence.…”

Section: Withoutmentioning

confidence: 87%

“…Briefly, the protein structure was prepared by removing water solvents, crystallization additives, and the covalently bound N3 ligand. The hydrogen atoms were added by using the VEGA program [27] to remain compatible with physiological pH. The protein structure was then minimized using Namd2 [39] and by keeping the backbone atoms fixed to preserve the resolved folding.…”

Section: Library and Protein Structure Preparationmentioning

confidence: 99%

“…All the poses generated by the four docking programs were rescored by ReScore+ [47]. The computed scoring functions comprise (a) the various components of PLANTS [25] and XScore [24] scoring functions; (b) a set of scores computed by the VEGA suite, which encodes for polar and non-polar interaction energies [27]; (c) the MLP interactions scores for hydrophobic contacts [26]; (d) the recently proposed Contacts scores [18], which are simply based on several surrounding residues, and (e) the APBS score for evaluating ionic interactions [48]. Both the primary scores and the values from rescoring calculations were utilized to calculate binding and isomeric spaces as well as their combinations by applying a joining and a merging strategy (see below).…”

Section: Rescoring Calculationsmentioning

confidence: 99%

“…In the so-called joint combination, the consensus equations were developed by simultaneously considering the space parameters as computed for both binding and isomeric spaces. The descriptors for the binding and isomeric spaces were computed by using ad-hoc scripts of the VEGA suite of programs [27].…”

Section: Rescoring Calculationsmentioning

confidence: 99%

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Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

et al. 2021

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The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available.

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Binding of boswellic acids to functional proteins of the SARS‐CoV‐2 virus: Bioinformatic studies

Caliebe

Scior

Ammon

2021

Archiv der Pharmazie

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Boswellic acids (BAs) have been shown to possess antiviral activity. Using bioinformatic methods, it was tested whether or not acetyl‐11‐keto‐β‐boswellic acid (AKBA), 11‐keto‐β‐boswellic acid (KBA), β‐boswellic acid (BBA), and the phosphorylated active metabolite of Remdesivir® (RGS‐P3) bind to functional proteins of SARS‐CoV‐2, that is, the replicase polyprotein P0DTD1, the spike glycoprotein P0DTC2, and the nucleoprotein P0DTC9. Using P0DTD1, AKBA and KBA showed micromolar binding affinity to the RNA‐dependent RNA polymerase (RdRp) and to the main proteinase complex Mpro. Phosphorylated BAs even bond in the nanomolar range. Due to their positive and negative charges, BAs and RGS‐P3 bond to corresponding negative and positive areas of the protein. BAs and RGS‐P3 docked in the tunnel‐like cavity of RdRp. BAs also docked into the elongated surface rim of viral Mpro. In both cases, binding occurred with active site amino acids in the lower micromolecular to upper nanomolar range. KBA, BBA, and RGS‐P3 also bond to P0DTC2 and P0DTC9. The binding energies for BAs were in the range of −5.8 to −6.3 kcal/mol. RGS‐P3 and BAs occluded the centrally located pore of the donut‐like protein structure of P0DTC9 and, in the case of P0DTC2, RGS‐P3 and BAs impacted the double‐wing‐like protein structure. The data of this bioinformatics study clearly show that BAs bind to three functional proteins of the SARS‐CoV‐2 virus responsible for adhesion and replication, as does RGS‐P3, a drug on the market to treat this disease. The binding effectiveness of BAs can be increased through phosphate esterification. Whether or not BAs are druggable against the SARS‐CoV‐2 disease remains to be established.

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The VEGA suite of programs: an versatile platform for cheminformatics and drug design projects

Cited by 132 publications

References 14 publications

MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

Binding of boswellic acids to functional proteins of the SARS‐CoV‐2 virus: Bioinformatic studies

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