scholarly journals A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0

2020 ◽  
Vol 21 (14) ◽  
pp. 5152 ◽  
Author(s):  
Silvia Gervasoni ◽  
Giulio Vistoli ◽  
Carmine Talarico ◽  
Candida Manelfi ◽  
Andrea R. Beccari ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Repurposing ◽  
Protein Targets ◽  
In Silico Studies ◽  
Allosteric Sites ◽  
Binding Pockets ◽  
Novel Strategy ◽  
Better Than ◽  
Comprehensive Mapping

(1) Background: Virtual screening studies on the therapeutically relevant proteins of the severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) require a detailed characterization of their druggable binding sites, and, more generally, a convenient pocket mapping represents a key step for structure-based in silico studies; (2) Methods: Along with a careful literature search on SARS-CoV-2 protein targets, the study presents a novel strategy for pocket mapping based on the combination of pocket (as performed by the well-known FPocket tool) and docking searches (as performed by PLANTS or AutoDock/Vina engines); such an approach is implemented by the Pockets 2.0 plug-in for the VEGA ZZ suite of programs; (3) Results: The literature analysis allowed the identification of 16 promising binding cavities within the SARS-CoV-2 proteins and the here proposed approach was able to recognize them showing performances clearly better than those reached by the sole pocket detection; and (4) Conclusions: Even though the presented strategy should require more extended validations, this proved successful in precisely characterizing a set of SARS-CoV-2 druggable binding pockets including both orthosteric and allosteric sites, which are clearly amenable for virtual screening campaigns and drug repurposing studies. All results generated by the study and the Pockets 2.0 plug-in are available for download.

scholarly journals Functional characterization of 3D protein structures informed by human genetic diversity

2019 ◽  
Vol 116 (18) ◽  
pp. 8960-8965 ◽  
Author(s):  
Michael Hicks ◽  
Istvan Bartha ◽  
Julia di Iulio ◽  
J. Craig Venter ◽  
Amalio Telenti
Keyword(s):  
Large Scale ◽  
Functional Characterization ◽  
Protein Structures ◽  
Large Scale Data ◽  
Allosteric Sites ◽  
Missense Variation ◽  
Binding Pockets ◽  
Definition Of ◽  
Scale Data

Sequence variation data of the human proteome can be used to analyze 3D protein structures to derive functional insights. We used genetic variant data from nearly 140,000 individuals to analyze 3D positional conservation in 4,715 proteins and 3,951 homology models using 860,292 missense and 465,886 synonymous variants. Sixty percent of protein structures harbor at least one intolerant 3D site as defined by significant depletion of observed over expected missense variation. Structural intolerance data correlated with deep mutational scanning functional readouts for PPARG, MAPK1/ERK2, UBE2I, SUMO1, PTEN, CALM1, CALM2, and TPK1 and with shallow mutagenesis data for 1,026 proteins. The 3D structural intolerance analysis revealed different features for ligand binding pockets and orthosteric and allosteric sites. Large-scale data on human genetic variation support a definition of functional 3D sites proteome-wide.

scholarly journals Machine learning classification can reduce false positives in structure-based virtual screening

2020 ◽  
Vol 117 (31) ◽  
pp. 18477-18488 ◽  
Author(s):  
Yusuf O. Adeshina ◽  
Eric J. Deeds ◽  
John Karanicolas
Keyword(s):  
Virtual Screening ◽  
General Purpose ◽  
Training Dataset ◽  
Scoring Functions ◽  
Protein Targets ◽  
Machine Learning Classification ◽  
Biochemical Assays ◽  
Active Chemical ◽  
Early Drug ◽  
Better Than

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery’s search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC50better than 50 μM. Without any medicinal chemistry optimization, the most potent hit has IC50280 nM, corresponding toKiof 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

scholarly journals Characterization of raloxifene as potential pharmacological agent against SARS-CoV-2 and its variants

2021 ◽  
Author(s):  
Daniela Iaconis ◽  
Carmine Talarico ◽  
Candida Manelfi ◽  
Maria Candida Cesta ◽  
Mara Zippoli ◽  
...  
Keyword(s):  
Drug Repurposing ◽  
Pharmaceutical Products ◽  
Clinical Development ◽  
Pharmacological Agent ◽  
Pharmacological Characterization ◽  
Protein Targets ◽  
Drug Candidates ◽  
Cellular Models ◽  
Receptor Modulator

The new coronavirus that emerged, called SARS-CoV-2, is the causative agent of the COVID-19 pandemic. The identification of potential drug candidates that can rapidly enter clinical trials for the prevention and treatment of COVID-19 is an urgent need, despite the recent introduction of several new vaccines for the prevention and protection of this infectious disease which in many cases becomes severe. Drug repurposing (DR), a process for studying existing pharmaceutical products for new therapeutic indications, represents one of the most effective potential strategies employed to increase the success rate in the development of new drug therapies. We identified raloxifene, a known Selective Estrogen Receptor modulator (SERM), as a potential pharmacological agent for the treatment of COVID-19 patients. Following a virtual screening campaign on the most relevant viral protein targets, in this work we report the results of the first pharmacological characterization of raloxifene in relevant cellular models of COVID-19 infection. The results obtained on all the most common viral variants originating in Europe, United Kingdom, Brazil, South Africa and India, currently in circulation, are also reported, confirming the efficacy of raloxifene and, consequently, the relevance of the proposed approach. Taken together, all the information gathered supports the clinical development of raloxifene and confirms that the drug can be proposed as a viable new option to fight the pandemic in at least some patient populations. The results obtained so far have paved the way for a first clinical study to test the safety and efficacy of raloxifene, just concluded in patients with COVID-19 paucisymptomatic.

scholarly journals Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

2019 ◽  
Vol 9 (21) ◽  
pp. 4538 ◽  
Author(s):  
Tatiana F. Vieira ◽  
Sérgio F. Sousa
Keyword(s):  
Virtual Screening ◽  
Open Source ◽  
Ligand Docking ◽  
Protein Targets ◽  
Starting Point ◽  
First Choice ◽  
Docking Program ◽  
Binding Pockets ◽  
Overall Performance ◽  
Flexible Ligands

AutoDock and Vina are two of the most widely used protein–ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.

scholarly journals Combining Molecular Dynamics and Docking Simulations to Develop Targeted Protocols for Performing Optimized Virtual Screening Campaigns on the hTRPM8 Channel

2020 ◽  
Vol 21 (7) ◽  
pp. 2265 ◽  
Author(s):  
Carmine Talarico ◽  
Silvia Gervasoni ◽  
Candida Manelfi ◽  
Alessandro Pedretti ◽  
Giulio Vistoli ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Rational Design ◽  
Protein Structures ◽  
Homology Model ◽  
General Applicability ◽  
Docking Simulations ◽  
In Silico Studies ◽  
Consensus Models ◽  
Binding Pockets ◽  
Multiple Receptor

Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 μs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen™ provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets.

Characterization of Rh/Si multilayer structure by HREM and HAADF

1992 ◽  
Vol 50 (1) ◽  
pp. 122-123
Author(s):  
Y. Cheng ◽  
J. Liu ◽  
M.B. Stearns ◽  
D.G. Steams
Keyword(s):  
Normal Incidence ◽  
High Resolution Electron Microscopy ◽  
X Rays ◽  
Beam Direction ◽  
Cross Sectional ◽  
Optical Elements ◽  
Resolution Electron ◽  
Point To Point ◽  
Better Than

The Rh/Si multilayer (ML) thin films are promising optical elements for soft x-rays since they have a calculated normal incidence reflectivity of ∼60% at a x-ray wavelength of ∼13 nm. However, a reflectivity of only 28% has been attained to date for ML fabricated by dc magnetron sputtering. In order to determine the cause of this degraded reflectivity the microstructure of this ML was examined on cross-sectional specimens with two high-resolution electron microscopy (HREM and HAADF) techniques.Cross-sectional specimens were made from an as-prepared ML sample and from the same ML annealed at 298 °C for 1 and 100 hours. The specimens were imaged using a JEM-4000EX TEM operating at 400 kV with a point-to-point resolution of better than 0.17 nm. The specimens were viewed along Si [110] projection of the substrate, with the (001) Si surface plane parallel to the beam direction.

Synthesis and characterization of flame-retardant-wrapped carbon nanotubes and its flame retardancy in epoxy nanocomposites

2021 ◽  
pp. 096739112110245
Author(s):  
Jiangbo Wang
Keyword(s):  
Carbon Nanotubes ◽  
Heat Release ◽  
Flame Retardant ◽  
Flame Retardancy ◽  
Total Heat Release ◽  
Pristine Cnts ◽  
Peak Heat Release Rate ◽  
Ep Matrix ◽  
Better Than

A novel phosphorus-silicon containing flame-retardant DOPO-V-PA was used to wrap carbon nanotubes (CNTs). The results of FTIR, XPS, TEM and TGA measurements exhibited that DOPO-V-PA has been successfully grafted onto the surfaces of CNTs, and the CNTs-DOPO-V-PA was obtained. The CNTs-DOPO-V-PA was subsequently incorporated into epoxy resin (EP) for improving the flame retardancy and dispersion. Compared with pure EP, the addition of 2 wt% CNTs-DOPO-V-PA into the EP matrix could achieve better flame retardancy of EP nanocomposites, such as a 30.5% reduction in peak heat release rate (PHRR) and 8.1% reduction in total heat release (THR). Furthermore, DMTA results clearly indicated that the dispersion for CNTs-DOPO-V-PA in EP matrix was better than pristine CNTs.

scholarly journals Discovery of a Bradykinin B2 Partial Agonist Profile of Raloxifene in a Drug Repurposing Campaign

2020 ◽  
Vol 22 (1) ◽  
pp. 257
Author(s):  
Patricia Gomez-Gutierrez ◽  
Juan J. Perez
Keyword(s):  
Virtual Screening ◽  
Molecular Mechanisms ◽  
Partial Agonist ◽  
Drug Repurposing ◽  
Cytokine Storm ◽  
Bradykinin Receptors ◽  
Screening Process ◽  
Bradykinin Antagonists ◽  
Illness Progression

Covid-19 urges a deeper understanding of the underlying molecular mechanisms involved in illness progression to provide a prompt therapeutical response with an adequate use of available drugs, including drug repurposing. Recently, it was suggested that a dysregulated bradykinin signaling can trigger the cytokine storm observed in patients with severe Covid-19. In the scope of a drug repurposing campaign undertaken to identify bradykinin antagonists, raloxifene was identified as prospective compound in a virtual screening process. The pharmacodynamics profile of raloxifene towards bradykinin receptors is reported in the present work, showing a weak selective partial agonist profile at the B2 receptor. In view of this new profile, its possible use as a therapeutical agent for the treatment of severe Covid-19 is discussed.

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