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HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors

A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating dr...

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Detalles Bibliográficos
Autores principales: Qureshi, Abid, Rajput, Akanksha, Kaur, Gazaldeep, Kumar, Manoj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5845081/
https://www.ncbi.nlm.nih.gov/pubmed/29524011
http://dx.doi.org/10.1186/s13321-018-0266-y
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author Qureshi, Abid
Rajput, Akanksha
Kaur, Gazaldeep
Kumar, Manoj
author_facet Qureshi, Abid
Rajput, Akanksha
Kaur, Gazaldeep
Kumar, Manoj
author_sort Qureshi, Abid
collection PubMed
description A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC(50) and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-018-0266-y) contains supplementary material, which is available to authorized users.
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spelling pubmed-58450812018-03-14 HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors Qureshi, Abid Rajput, Akanksha Kaur, Gazaldeep Kumar, Manoj J Cheminform Research Article A number of anti-retroviral drugs are being used for treating Human Immunodeficiency Virus (HIV) infection. Due to emergence of drug resistant strains, there is a constant quest to discover more effective anti-HIV compounds. In this endeavor, computational tools have proven useful in accelerating drug discovery. Although methods were published to design a class of compounds against a specific HIV protein, but an integrated web server for the same is lacking. Therefore, we have developed support vector machine based regression models using experimentally validated data from ChEMBL repository. Quantitative structure activity relationship based features were selected for predicting inhibition activity of a compound against HIV proteins namely protease (PR), reverse transcriptase (RT) and integrase (IN). The models presented a maximum Pearson correlation coefficient of 0.78, 0.76, 0.74 and 0.76, 0.68, 0.72 during tenfold cross-validation on IC(50) and percent inhibition datasets of PR, RT, IN respectively. These models performed equally well on the independent datasets. Chemical space mapping, applicability domain analyses and other statistical tests further support robustness of the predictive models. Currently, we have identified a number of chemical descriptors that are imperative in predicting the compound inhibition potential. HIVprotI platform (http://bioinfo.imtech.res.in/manojk/hivproti) would be useful in virtual screening of inhibitors as well as designing of new molecules against the important HIV proteins for therapeutics development. [Image: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-018-0266-y) contains supplementary material, which is available to authorized users. Springer International Publishing 2018-03-09 /pmc/articles/PMC5845081/ /pubmed/29524011 http://dx.doi.org/10.1186/s13321-018-0266-y Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Qureshi, Abid
Rajput, Akanksha
Kaur, Gazaldeep
Kumar, Manoj
HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors
title HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors
title_full HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors
title_fullStr HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors
title_full_unstemmed HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors
title_short HIVprotI: an integrated web based platform for prediction and design of HIV proteins inhibitors
title_sort hivproti: an integrated web based platform for prediction and design of hiv proteins inhibitors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5845081/
https://www.ncbi.nlm.nih.gov/pubmed/29524011
http://dx.doi.org/10.1186/s13321-018-0266-y
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