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


METHODS
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.


RESULTS
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.


CONCLUSIONS
The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications.
Department of Anatomy, Zhong Shan School of Medicine, Sun Yat-Sen University, Guangzhou, People's Republic of China. chenshp@mail.sysu.edu.cn
Shu
S
Liu
Rao
R
Fu
Li-Hua
LH
Zhou
Sheng-Ping
SP
Chen
D053829:Amyloid Precursor Protein Secretases / Q000037:antagonists & inhibitors* / Q000737:chemistry* / Q000378:metabolism*; D016282:Aspartic Acid Endopeptidases / Q000037:antagonists & inhibitors* / Q000737:chemistry* / Q000378:metabolism*; D020134:Catalytic Domain / Q000235:genetics; D055808:Drug Discovery / Q000379:methods*; D031541:Fluorescence Resonance Energy Transfer; D006801:Humans; D008958:Models, Molecular; D025341:Principal Component Analysis / Q000379:methods*; D011485:Protein Binding; D012107:Research Design
D053829:Amyloid Precursor Protein Secretases; D016282:Aspartic Acid Endopeptidases; C509280:BACE1 protein, human