Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).

<h4>Background</h4>In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the v...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Shu Liu, Rao Fu, Li-Hua Zhou, Sheng-Ping Chen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/79f4804104f44866b2ac1a9005802870
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:79f4804104f44866b2ac1a9005802870
record_format	dspace
spelling	oai:doaj.org-article:79f4804104f44866b2ac1a90058028702021-11-18T07:15:52ZApplication of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).1932-620310.1371/journal.pone.0038086https://doaj.org/article/79f4804104f44866b2ac1a90058028702012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22701601/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1.<h4>Method</h4>Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test.<h4>Results</h4>For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay.<h4>Conclusion</h4>The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications.Shu LiuRao FuLi-Hua ZhouSheng-Ping ChenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 6, p e38086 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shu Liu Rao Fu Li-Hua Zhou Sheng-Ping Chen Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).
description	<h4>Background</h4>In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1.<h4>Method</h4>Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test.<h4>Results</h4>For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay.<h4>Conclusion</h4>The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications.
format	article
author	Shu Liu Rao Fu Li-Hua Zhou Sheng-Ping Chen
author_facet	Shu Liu Rao Fu Li-Hua Zhou Sheng-Ping Chen
author_sort	Shu Liu
title	Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).
title_short	Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).
title_full	Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).
title_fullStr	Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).
title_full_unstemmed	Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).
title_sort	application of consensus scoring and principal component analysis for virtual screening against β-secretase (bace-1).
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/79f4804104f44866b2ac1a9005802870
work_keys_str_mv	AT shuliu applicationofconsensusscoringandprincipalcomponentanalysisforvirtualscreeningagainstbsecretasebace1 AT raofu applicationofconsensusscoringandprincipalcomponentanalysisforvirtualscreeningagainstbsecretasebace1 AT lihuazhou applicationofconsensusscoringandprincipalcomponentanalysisforvirtualscreeningagainstbsecretasebace1 AT shengpingchen applicationofconsensusscoringandprincipalcomponentanalysisforvirtualscreeningagainstbsecretasebace1
_version_	1718423734685007872

Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1).

Ejemplares similares