Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.

The increasing resistance to current therapeutic agents for HIV drug regiment remains a major problem for effective acquired immune deficiency syndrome (AIDS) therapy. Many potential inhibitors have today been developed which inhibits key cellular pathways in the HIV cycle. Inhibition of HIV-1 rever...

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Autores principales:	Vasanthanathan Poongavanam, Jacob Kongsted
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/84269d3c014041e0be86253cb4cb92c4
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spelling	oai:doaj.org-article:84269d3c014041e0be86253cb4cb92c42021-11-18T08:55:13ZVirtual screening models for prediction of HIV-1 RT associated RNase H inhibition.1932-620310.1371/journal.pone.0073478https://doaj.org/article/84269d3c014041e0be86253cb4cb92c42013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24066050/?tool=EBIhttps://doaj.org/toc/1932-6203The increasing resistance to current therapeutic agents for HIV drug regiment remains a major problem for effective acquired immune deficiency syndrome (AIDS) therapy. Many potential inhibitors have today been developed which inhibits key cellular pathways in the HIV cycle. Inhibition of HIV-1 reverse transcriptase associated ribonuclease H (RNase H) function provides a novel target for anti-HIV chemotherapy. Here we report on the applicability of conceptually different in silico approaches as virtual screening (VS) tools in order to efficiently identify RNase H inhibitors from large chemical databases. The methods used here include machine-learning algorithms (e.g. support vector machine, random forest and kappa nearest neighbor), shape similarity (rapid overlay of chemical structures), pharmacophore, molecular interaction fields-based fingerprints for ligands and protein (FLAP) and flexible ligand docking methods. The results show that receptor-based flexible docking experiments provides good enrichment (80-90%) compared to ligand-based approaches such as FLAP (74%), shape similarity (75%) and random forest (72%). Thus, this study suggests that flexible docking experiments is the model of choice in terms of best retrieval of active from inactive compounds and efficiency and efficacy schemes. Moreover, shape similarity, machine learning and FLAP models could also be used for further validation or filtration in virtual screening processes. The best models could potentially be use for identifying structurally diverse and selective RNase H inhibitors from large chemical databases. In addition, pharmacophore models suggest that the inter-distance between hydrogen bond acceptors play a key role in inhibition of the RNase H domain through metal chelation.Vasanthanathan PoongavanamJacob KongstedPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e73478 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vasanthanathan Poongavanam Jacob Kongsted Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.
description	The increasing resistance to current therapeutic agents for HIV drug regiment remains a major problem for effective acquired immune deficiency syndrome (AIDS) therapy. Many potential inhibitors have today been developed which inhibits key cellular pathways in the HIV cycle. Inhibition of HIV-1 reverse transcriptase associated ribonuclease H (RNase H) function provides a novel target for anti-HIV chemotherapy. Here we report on the applicability of conceptually different in silico approaches as virtual screening (VS) tools in order to efficiently identify RNase H inhibitors from large chemical databases. The methods used here include machine-learning algorithms (e.g. support vector machine, random forest and kappa nearest neighbor), shape similarity (rapid overlay of chemical structures), pharmacophore, molecular interaction fields-based fingerprints for ligands and protein (FLAP) and flexible ligand docking methods. The results show that receptor-based flexible docking experiments provides good enrichment (80-90%) compared to ligand-based approaches such as FLAP (74%), shape similarity (75%) and random forest (72%). Thus, this study suggests that flexible docking experiments is the model of choice in terms of best retrieval of active from inactive compounds and efficiency and efficacy schemes. Moreover, shape similarity, machine learning and FLAP models could also be used for further validation or filtration in virtual screening processes. The best models could potentially be use for identifying structurally diverse and selective RNase H inhibitors from large chemical databases. In addition, pharmacophore models suggest that the inter-distance between hydrogen bond acceptors play a key role in inhibition of the RNase H domain through metal chelation.
format	article
author	Vasanthanathan Poongavanam Jacob Kongsted
author_facet	Vasanthanathan Poongavanam Jacob Kongsted
author_sort	Vasanthanathan Poongavanam
title	Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.
title_short	Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.
title_full	Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.
title_fullStr	Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.
title_full_unstemmed	Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.
title_sort	virtual screening models for prediction of hiv-1 rt associated rnase h inhibition.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/84269d3c014041e0be86253cb4cb92c4
work_keys_str_mv	AT vasanthanathanpoongavanam virtualscreeningmodelsforpredictionofhiv1rtassociatedrnasehinhibition AT jacobkongsted virtualscreeningmodelsforpredictionofhiv1rtassociatedrnasehinhibition
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Virtual screening models for prediction of HIV-1 RT associated RNase H inhibition.

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