Classification of chemical compounds by protein-compound docking for use in designing a focused library.
We developed a new method for the classification of chemical compounds and protein pockets and applied it to a random screening experiment for macrophage migration inhibitory factor (MIF). The principal component analysis (PCA) method was applied to the protein-compound interaction matrix, which was given by thorough docking calculations between a set of many protein pockets and chemical compounds. Each compound and protein pocket was depicted as a point in the PCA spaces of compounds and proteins, respectively. This method was applied to distinguish active compounds from negative compounds of MIF. A random screening experiment for MIF was performed, and our method revealed that the active compounds were localized in the PCA space of compounds, while the negative compounds showed a wide distribution. Furthermore, protein pockets, which bind similar compounds, were classified and were found to form a cluster in the PCA space.
Fukunishi
Yoshifumi
Y
Biological Information Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Koto-ku, Tokyo 135-0064, Japan. y-fukunishi@jbirc.aist.go.jp
Mikami
Yoshiaki
Y
Takedomi
Kei
K
Yamanouchi
Masaya
M
Shima
Hideaki
H
Nakamura
Haruki
H
D001665:Binding Sites; D020650:Combinatorial Chemistry Techniques; D016208:Databases, Factual; D008263:Macrophage Migration-Inhibitory Factors / Q000737:chemistry; D008958:Models, Molecular; D009930:Organic Chemicals / Q000737:chemistry / Q000145:classification*; D025341:Principal Component Analysis; D011506:Proteins / Q000737:chemistry / Q000145:classification*; D021281:Quantitative Structure-Activity Relationship
D008263:Macrophage Migration-Inhibitory Factors; D009930:Organic Chemicals; D011506:Proteins