scholarly journals D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

2020 ◽  
Author(s):  
Zhengdan Zhu ◽  
Xiaoyu Wang ◽  
Yanqing Yang ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Virtual Screening ◽  
Drug Targets ◽  
False Negative ◽  
Three Dimensional ◽  
Target Prediction ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Active Compounds ◽  
Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

scholarly journals D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

2020 ◽  
Author(s):  
Zhengdan Zhu ◽  
Xiaoyu Wang ◽  
Yanqing Yang ◽  
Xinben Zhang ◽  
Kaijie Mu ◽  
...  
Keyword(s):  
Protein Structure ◽  
Virtual Screening ◽  
Drug Targets ◽  
False Negative ◽  
Three Dimensional ◽  
Target Prediction ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Active Compounds ◽  
Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

scholarly journals Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

2019 ◽  
Vol 26 (21) ◽  
pp. 3874-3889 ◽  
Author(s):  
Jelica Vucicevic ◽  
Katarina Nikolic ◽  
John B.O. Mitchell
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
Antineoplastic Agents ◽  
Drug Targets ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Rational Drug Design ◽  
Bioactive Molecules ◽  
Computational Approaches ◽  
Rational Drug

Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

scholarly journals Molecular Docking of Phytochemicals against Streptococcus mutans Virulence Targets: A Proteomic Insight into Drug Planning

2022 ◽  
Author(s):  
Diego Romário da Silva ◽  
Tahyná Duda Deps ◽  
Otavio Akira Souza Sakaguchi ◽  
Edja Maria Melo de Brito Costa ◽  
Carlus Alberto Oliveira dos Santos ◽  
...  
Keyword(s):  
Streptococcus Mutans ◽  
Drug Targets ◽  
Bioactive Molecules ◽  
Target Proteins ◽  
In Silico Study ◽  
Target Action ◽  
Atp Binding Protein ◽  
Insight Into

Streptococcus mutans (S. mutans) is the most prevalent and most associated with dental caries. Here we aim to identify, through an in silico study, potential bioactive molecules against S. mutans. Twenty-four bioactive molecules with proven action against S. mutans were selected: 1-methoxyficifolinol; 5,7,2′,4′-tetrahydroxy-8-lavandulylflavanone (sophoraflavanone G); 6,8-diprenylgenistein; apigenin; artocarpesin; artocarpin; darbergioidin; dihydrobiochanin A; dihydrocajanin (5,2′,4′-trihydroxy-7-methoxyisoflavanone); erycristagallin; Erystagallin; ferreirin; fisetin; kaempferol; licoricidin; licorisoflavan A; licorisoflavan C; licorisoflavan E; luteolin (3′,4′,5,7-tetrahydroxyflavone); malvidin-3,5-diglucoside; myricetin; orientanol B; quercetin; and quercitrin. Moreover, we selected nine important target proteins for the virulence of this microorganism to perform as drug targets: antigen I/II (region V) (PDB: 1JMM); Antigen I/II (carbox-terminal region) (PDB: 3QE5); Spap (PDB: 3OPU); UA159sp signaling peptide (PDB: 2I2J); TCP3 signaling peptide (PDB: 2I2H); ATP-binding protein ComA (PDB: 3VX4); glucanosucrase (PDB: 3AIC); dextranase (PDB: 3VMO), and Hemolysin (PDB: 2RK5). Five molecules were revealed to be the best ligands for at least three target proteins, highlighting the following compounds: 11 (erystagallin), 10 (erycristagallin), 1 (methoxyficifonilol), 20 (malvidin-3,5-diglucoside), and 2 (sophoraflavanone G), which indicates a possible multi-target action of these compounds. Therefore, based on these findings, in vitro and in vivo tests should be performed to validate the effectiveness of these compounds in inhibiting S. mutans virulence factors. Furthermore, the promising results of these assays will allow the incorporation of these phytoconstituents in products for oral use for the control of tooth decay.

scholarly journals Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes

2021 ◽  
Vol 118 (51) ◽  
pp. e2112621118
Author(s):  
Joseph M. Paggi ◽  
Julia A. Belk ◽  
Scott A. Hollingsworth ◽  
Nicolas Villanueva ◽  
Alexander S. Powers ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Targets ◽  
3D Structure ◽  
Three Dimensional ◽  
Rational Drug Design ◽  
Screening Methods ◽  
Pose Prediction ◽  
Learning Approaches ◽  
Sources Of Information ◽  
Drug Candidates

Over the past five decades, tremendous effort has been devoted to computational methods for predicting properties of ligands—i.e., molecules that bind macromolecular targets. Such methods, which are critical to rational drug design, fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target’s three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here, we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand’s pose—the 3D structure of the ligand bound to its target—that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves pose prediction accuracy across all major families of drug targets. Using the same framework, we develop a method for virtual screening of drug candidates, which outperforms standard physics-based and ligand-based virtual screening methods. Our results suggest broad opportunities to improve prediction of various ligand properties by combining diverse sources of information through customized machine-learning approaches.

scholarly journals Greedy 3-Point Search (G3PS)—A Novel Algorithm for Pharmacophore Alignment

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7201
Author(s):  
Christian Permann ◽  
Thomas Seidel ◽  
Thierry Langer
Keyword(s):  
Virtual Screening ◽  
False Negative ◽  
Three Dimensional ◽  
False Negative Rate ◽  
Alignment Algorithm ◽  
Screening Experiments ◽  
Point Search ◽  
Optimal Alignments ◽  
Pharmacophore Models ◽  
Feature Pattern

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.

VSPrep: A KNIME Workflow for the Preparation of Molecular Databases for Virtual Screening

2020 ◽  
Vol 27 (38) ◽  
pp. 6480-6494 ◽  
Author(s):  
José-Manuel Gally ◽  
Stéphane Bourg ◽  
Jade Fogha ◽  
Quoc-Tuan Do ◽  
Samia Aci-Sèche ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Drug Design ◽  
In Silico ◽  
Three Dimensional ◽  
Bioactive Molecules ◽  
Screening Tools ◽  
Computational Time ◽  
In Silico Drug Design ◽  
Molecular Databases ◽  
Design Projects

Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.

scholarly journals The SwissSimilarity 2021 Web Tool: Novel Chemical Libraries and Additional Methods for an Enhanced Ligand-Based Virtual Screening Experience

2022 ◽  
Vol 23 (2) ◽  
pp. 811
Author(s):  
Maiia E. Bragina ◽  
Antoine Daina ◽  
Marta A. S. Perez ◽  
Olivier Michielin ◽  
Vincent Zoete
Keyword(s):  
Virtual Screening ◽  
Molecular Structures ◽  
Bioactive Molecules ◽  
Web Tool ◽  
Chemical Libraries ◽  
Compound Libraries ◽  
Rational Drug ◽  
Approved Drugs ◽  
3D Methods ◽  
2D And 3D

Hit finding, scaffold hopping, and structure–activity relationship studies are important tasks in rational drug discovery. Implementation of these tasks strongly depends on the availability of compounds similar to a known bioactive molecule. SwissSimilarity is a web tool for low-to-high-throughput virtual screening of multiple chemical libraries to find molecules similar to a compound of interest. According to the similarity principle, the output list of molecules generated by SwissSimilarity is expected to be enriched in compounds that are likely to share common protein targets with the query molecule and that can, therefore, be acquired and tested experimentally in priority. Compound libraries available for screening using SwissSimilarity include approved drugs, clinical candidates, known bioactive molecules, commercially available and synthetically accessible compounds. The first version of SwissSimilarity launched in 2015 made use of various 2D and 3D molecular descriptors, including path-based FP2 fingerprints and ElectroShape vectors. However, during the last few years, new fingerprinting methods for molecular description have been developed or have become popular. Here we would like to announce the launch of the new version of the SwissSimilarity web tool, which features additional 2D and 3D methods for estimation of molecular similarity: extended-connectivity, MinHash, 2D pharmacophore, extended reduced graph, and extended 3D fingerprints. Moreover, it is now possible to screen for molecular structures having the same scaffold as the query compound. Additionally, all compound libraries available for screening in SwissSimilarity have been updated, and several new ones have been added to the list. Finally, the interface of the website has been comprehensively rebuilt to provide a better user experience. The new version of SwissSimilarity is freely available starting from December 2021.

Preliminary Study on the Mechanism of Carvacrol Regulating Hepatocellular Carcinoma Based on Network Pharmacology

2019 ◽  
Vol 16 (11) ◽  
pp. 1286-1295
Author(s):  
Sha Li ◽  
Haixia Zhao ◽  
Lidao Bao
Keyword(s):  
Hepatocellular Carcinoma ◽  
Western Blot ◽  
Target Prediction ◽  
Network Pharmacology ◽  
Literature Mining ◽  
Active Compounds ◽  
Prediction Server ◽  
Preliminary Study ◽  
Alignment Analysis ◽  
Endogenous Interactions

Objective: To predict and analyze the target of anti-Hepatocellular Carcinoma (HCC) in the active constituents of Safflower by using network pharmacology. Methods: The active compounds of safflower were collected by TCMSP, TCM-PTD database and literature mining methods. The targets of active compounds were predicted by Swiss Target Prediction server, and the target of anti-HCC drugs was collected by DisGeNET database. The target was subjected to an alignment analysis to screen out Carvacrol, a target of safflower against HCC. The mouse HCC model was established and treated with Carvacrol. The anti-HCC target DAPK1 and PPP2R2A were verified by Western blot and co-immunoprecipitation. Results: A total of 21 safflower active ingredients were predicted. Carvacrol was identified as a possible active ingredient according to the five principles of drug-like medicine. According to Carvacrol's possible targets and possible targets of HCC, three co-targets were identified, including cancer- related are DAPK1 and PPP2R2A. After 20 weeks of Carvacrol treated, Carvacrol group significantly increased on DAPK1 levels and decreased PPP2R2A levels in the model mice by Western blot. Immunoprecipitation confirmed the endogenous interaction between DAPK1 and PPP2R2A. Conclusion: Safflower can regulate the development of HCC through its active component Carvacrol, which can affect the expression of DAPK1 and PPP2R2A proteins, and the endogenous interactions of DAPK1 and PPP2R2A proteins.

Drug repurposing using similarity-based target prediction, docking studies and scaffold hopping of lefamulin

2020 ◽  
Vol 17 ◽  
Author(s):  
Shikha Sharma ◽  
Shweta Sharma ◽  
Vaishali Pathak ◽  
Parwinder Kaur ◽  
Rajesh Kumar Singh
Keyword(s):  
In Silico ◽  
Target Prediction ◽  
Drug Repurposing ◽  
Docking Studies ◽  
Target Protein ◽  
Future Perspective ◽  
Antibacterial Drug ◽  
Scaffold Hopping ◽  
Target Proteins ◽  
Renin Inhibitors

Aim: To investigate and validate the potential target proteins for drug repurposing of newly FDA approved antibacterial drug. Background: Drug repurposing is the process of assigning indications for drugs other than the one(s) that they were initially developed for. Discovery of entirely new indications from already approved drugs is highly lucrative as it minimizes the pipeline of the drug development process by reducing time and cost. In silico driven technologies made it possible to analyze molecules for different target proteins which are not yet explored. Objective: To analyze possible targets proteins for drug repurposing of lefamulin and their validation. Also, in silico prediction of novel scaffolds from lefamulin has been performed for assisting medicinal chemists in future drug design. Methods: A similarity-based prediction tool was employed for predicting target protein and further investigated using docking studies on PDB ID: 2V16. Besides, various in silico tools were employed for prediction of novel scaffolds from lefamulin using scaffold hopping technique followed by evaluation with various in silico parameters viz., ADME, synthetic accessibility and PAINS. Results: Based on the similarity and target prediction studies, renin is found as the most probable target protein for lefamulin. Further, validation studies using docking of lefamulin revealed the significant interactions of lefamulin with the binding pocket of the target protein. Also, three novel scaffolds were predicted using scaffold hopping technique and found to be in the limit to reduce the chances of drug failure in the physiological system during the last stage approval process. Conclusion: To encapsulate the future perspective, lefamulin may assist in the development of the renin inhibitors and, also three possible novel scaffolds with good pharmacokinetic profile can be developed into both as renin inhibitors and for bacterial infections.

scholarly journals Proteomic and Genomic Studies on Lizard Venoms in the Last Decade

2010 ◽  
Vol 3 ◽  
pp. PRI.S3693 ◽  
Author(s):  
Hang Fai Kwok ◽  
Craig Ivanyi ◽  
Andrew Morris ◽  
Chris Shaw
Keyword(s):  
Natural Resources ◽  
Potential Benefit ◽  
Current Knowledge ◽  
The State ◽  
Bioactive Molecules ◽  
Genomic Research ◽  
Active Compounds ◽  
Current Review ◽  
Comprehensive Picture ◽  
Genomic Studies

Traditionally man has looked to nature to provide cures for diseases. This approach still exists today in the form of ‘bio-prospecting’ for therapeutically-active compounds in venoms. For example, the venoms of many reptiles offer a spectacular laboratory of bioactive molecules, including peptides and proteins. In the last 10–15 years, there have been a number of major proteomic and genomic research breakthroughs on lizard venoms. In this current review, the key findings from these proteomic and genomic studies will be critically discussed and suggestions will be offered for future focused investigations. It is our intention that this article will not only provide a comprehensive picture of the state of current knowledge of the components of lizard venoms, but also engender awareness in readers of the need to protect and conserve such uniquely precious natural resources for several reasons, including the potential benefit of humankind.

Sign in / Sign up

Export Citation Format

Share Document