In silico prediction of antimalarial drug target candidates

Ludin, Philipp; Woodcroft, Ben J.; Ralph, Stuart A.; Mäser, Pascal

doi:10.1016/j.ijpddr.2012.07.002

Cited by 29 publications

(19 citation statements)

References 99 publications

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“…During the course of time, this strategy was proposed; many scientists have successfully applied it, for mining the new therapeutic candidates. Some of the successful studies that included this strategy to establish novel therapeutics in Plasmodium falciparum [35], Mycoplasma hyopneumoniae [36], Clostridium perfringens [37], Salmonella typhi [38], Neisseria species [39], Aeromonas hydrophila [40], Helicobacter pylori [41], and in many other organisms.…”

Section: Resultsmentioning

confidence: 99%

Screening of Putative Therapeutic Candidates in Superbug (Staphylococcus Aureus): A Systematic in Silico Approach

Patnala

Zaveri²

2016

Asian J Pharm Clin Res

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Objective: Staphylococcus aureus, a superbug and antibiotic resistant pathogen, is one of the most infection causing organism, ranging from skin allergies to severe lethal conditions. The prolonged use of different antibiotics and lack of optimal treatment over the antibiotic resistant species, led to the identification of new, better and promising therapeutic candidates.Methods: A systematic in silico filtration process was employed, which includes subtractive channels and reverse vaccinology techniques.Results: Here, we report 12 possible drug targets and two vaccine candidates based on essentiality, non-human homolog, virulent and localization, commonly in all the strains. Further characterization studies such as pathway analysis, chokepoint and structure prediction revealed, two proteins as the best drug targets one being novel and the other druggable. Only one protein has shown the characteristic feature of vaccine candidate, having antigenic property and an IgG binding domain. Conclusion:Two best drug targets were commonly identified in all the strains of S. aureus namely UDP-N-acetylmuramoyl-L-alanyl-D-glutamate--L-lysine ligase (MurE) and cell division protein FtsA, whereas the best common vaccine candidate includes Peptidoglycan binding protein. The therapeutic candidates reported in the present study might facilitate screening of new and better antimicrobial compounds, for an optimal treatment of S. aureus infections.

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Section: Resultsmentioning

confidence: 99%

Screening of Putative Therapeutic Candidates in Superbug (Staphylococcus Aureus): A Systematic in Silico Approach

Patnala

Zaveri²

2016

Asian J Pharm Clin Res

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“…2). The web server classifies 88 proteins as transmembrane proteins (27), lipid-binding proteins (13), zinc-binding proteins(13), ironbinding proteins (12), electrochemical potential driven transporter proteins (10), DNA-binding proteins (1), repressorproteins (1), metal-binding proteins(1), transferases (2), hydrolases (1),G protein coupled receptor (1), repressor (1), lyases (1), oxidoreductase (1), structural proteins(1) and two proteins classified with very low p value (58.6%) among which one protein involved in photosystem 1 other in calcium, magnesium-binding. Metabolic pathway analysis of these 136 non-human homologous essential proteins revealed that 91 proteins are involved in metabolic pathway, among these 4 pathways were unique to pathogen these unique pathways were Methane metabolism, Styrene metabolism, Carbon fixation and photosynthetic organisms and two component system.…”

Section: Fig 1 Percentage Distribution Of Sub-cellular Locations In mentioning

confidence: 99%

“…By a similar homology search against these databases aligned targets were considered as significant homologs. All the other targets showing no similarity with these drug target databases were differentiated as novel targets, which should be further validated experimentally [27]. In order to recognize the important role of chemical accessibility in ranking of putative drug targets we looked for druggability information on the predicted essential targets.…”

Section: Fig 1 Percentage Distribution Of Sub-cellular Locations In mentioning

confidence: 99%

In Silico Identification and Characterization of Potent Drug Targets in Fungal Pathogen causing Keratitis

Rashmi¹

2018

IJRASET

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Availability of genome sequences of pathogens has provided a tremendous amount of information that can be useful in drug target identification. Subtractive proteomics approach is being used to mine the list of proteins present in different Aspergillus species which are non-homologous to human and essential for the survival of the pathogen. Current study is based on complete proteome information of Aspergillus terreus strain NIH 2624available in biological databases to identify putative protein targets. Subjecting the total set of pathogen non homologous proteins (10402) against the Database of Essential Genes 2030 proteins were screened out as essential proteins of the Aspergillus terreus,out of which 136 are found to be essential in the pathogen with no human homolog. Among 136 proteins; around 88 proteins were found to be putative uncharacterized using CELLO. The hypothetical essential proteins were functionally annotated through SVMProt server. Druggability of each of the identified drug targets was also evaluated by the Drug Bank database.

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“…However, the overview was limited to the studied collection and did not include several critical antimalarial drugs for validation and verification. Nevertheless, the assay data has been used in several works for both clustering and prediction of biologically relevant antimalarial targets and SAR information (by genetic methods and network graph analysis).…”

Section: Introductionmentioning

confidence: 99%

AntiMalarial Mode of Action (AMMA) Database: Data Selection, Verification and Chemical Space Analysis

Sidorov

Davioud‐Charvet²,

Marcou

et al. 2018

Molecular Informatics

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This paper presents the effort of collecting and curating a data set of 15461 molecules tested against the malaria parasite, with robust activity and mode of action annotations. The set is compiled from in-house experimental data and the public ChEMBL database subsets. We illustrate the usefulness of the dataset by building QSAR models for antimalarial activity and QSPR models for modes of actions, as well as by the analysis of the chemical space with the Generative Topographic Mapping method. The GTM models perform well in prediction of both activity and mode of actions, on par with the classical SVM methods. The visualization of obtained maps helps to understand the distribution of molecules corresponding to different modes of action: molecules with similar targets are located close to each other on the map. Therefore, this analysis may suggest new modes of action for non-annotated or even annotated compounds. In perspective, this can be used as a tool for prediction of both antimalarial activity and target for novel, untested compounds.

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In silico prediction of antimalarial drug target candidates

Cited by 29 publications

References 99 publications

Screening of Putative Therapeutic Candidates in Superbug (Staphylococcus Aureus): A Systematic in Silico Approach

Screening of Putative Therapeutic Candidates in Superbug (Staphylococcus Aureus): A Systematic in Silico Approach

In Silico Identification and Characterization of Potent Drug Targets in Fungal Pathogen causing Keratitis

AntiMalarial Mode of Action (AMMA) Database: Data Selection, Verification and Chemical Space Analysis

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