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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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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. |
format | Online Article Text |
id | pubmed-5896307 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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