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In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the developme...

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Autores principales: Oyelade, Jelili, Isewon, Itunuoluwa, Uwoghiren, Efosa, Aromolaran, Olufemi, Oladipupo, Olufunke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896307/
https://www.ncbi.nlm.nih.gov/pubmed/29789805
http://dx.doi.org/10.1155/2018/8985718
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author Oyelade, Jelili
Isewon, Itunuoluwa
Uwoghiren, Efosa
Aromolaran, Olufemi
Oladipupo, Olufunke
author_facet Oyelade, Jelili
Isewon, Itunuoluwa
Uwoghiren, Efosa
Aromolaran, Olufemi
Oladipupo, Olufunke
author_sort Oyelade, Jelili
collection PubMed
description Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.
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spelling pubmed-58963072018-05-22 In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network Oyelade, Jelili Isewon, Itunuoluwa Uwoghiren, Efosa Aromolaran, Olufemi Oladipupo, Olufunke Biomed Res Int Research Article Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets. Hindawi 2018-03-29 /pmc/articles/PMC5896307/ /pubmed/29789805 http://dx.doi.org/10.1155/2018/8985718 Text en Copyright © 2018 Jelili Oyelade et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Oyelade, Jelili
Isewon, Itunuoluwa
Uwoghiren, Efosa
Aromolaran, Olufemi
Oladipupo, Olufunke
In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network
title In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network
title_full In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network
title_fullStr In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network
title_full_unstemmed In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network
title_short In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network
title_sort in silico knockout screening of plasmodium falciparum reactions and prediction of novel essential reactions by analysing the metabolic network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896307/
https://www.ncbi.nlm.nih.gov/pubmed/29789805
http://dx.doi.org/10.1155/2018/8985718
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