Supporting Information

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1 Supporting Information COMPUTATIONAL DISCOVERY AND EXPERIMENTAL VALIDATION OF INHIBITORS OF THE HUMAN INTESTINAL TRANSPORTER, OATP2B1 Natalia Khuri 1,2,#, Arik A. Zur 2,#, Matthias B. Wittwer 2, Lawrence Lin 2, Sook Wah Yee 2, Andrej Sali 2,3,, and Kathleen M. Giacomini 2,4,* 1 Bioengineering Department, Stanford University, Stanford, California Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco CA Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences (QB3), University of California at San Francisco, San Francisco CA Institute for Human Genetics, University of California San Francisco, San Francisco CA # These authors contributed equally * Corresponding author: Kathleen Giacomini, University of California San Francisco, th Street Box 2911, San Francisco, CA 94158, USA. tel: ; fax: ; kathy.giacomini@ucsf.edu. S 1

2 TABLE OF CONTENTS Figure S1. Training set diversity and inhibition activity. Figure S2. Receiver operating characteristic curves for 100 independent test validation experiments using knn and SVM algorithms. Figure S3. Seven templates utilized for comparative structure modeling and two-way clustering of docking against the seven comparative models. Figure S4. Plot of predicted inhibition scores for 5805 DrugBank compounds versus highest pairwise ECFP Tanimoto similarity to training set. Supplementary Excel File Table S1. Information gain of 219 computed molecular descriptors and correlation between descriptor and inhibition values. Table S2. Results of 100 repeated cross-validation of ligand-based modeling of OATP2B1 inhibitors. Table S3. Predicted inhibitors and their similarity to training set (Tanimoto coefficient). Table S4. Similarity of DrugBank Compounds to Compounds in Training Data Set. Supplementary on-line data file (available after publication). Multiple sequence alignment of OATP2B1 and template sequences, PDB files of comparative models, and SDF file of DrugBank library. S 2

3 Density Density A. Pairwise 2D Tanimoto coefficients B. OATP2B1 Inhibition ( % ) Figure S1. Training set diversity and inhibition activity. A. Distribution of pairwise 2D Tanimoto similarity coefficients between compounds in the training set. B. Distribution of inhibition values reported for compounds in the training set. Compounds with percent inhibition greater than or equal than 50 were labeled as inhibitors of OATP2B1. S 3

4 True positive rate True positive rate knn auroc=0.81 SVM auroc=0.81 False positive rate False positive rate Figure S2. Receiver operating characteristic curves for 100 independent tests using knn and SVM algorithms. Random classifier ROCs are shown as the dotted red lines. S 4

5 Figure S3. Seven templates utilized for comparative structure modeling and two-way clustering of docking against the seven comparative models. A. Seven comparative models of OATP2B1 built using six prokaryotic and one eukaryotic atomic structures (3WDO: Escherichia coli YajR transporter at 3.15 Å resolution in an outwardfacing conformation, 4GBZ: Escherichia coli XylE transporter at 2.8 Å resolution in outwardfacing, partly occluded conformation, 4J05: Piriformospora indica PipT transporter at 2.6 Å resolution in inward-facing occluded conformation, 1PW4: Escherichia coli GlpT transporter at 3.3 Å resolution in inward-facing conformation, 1PV7: Escherichia coli LacY transporter at 3.5 Å resolution in inward-facing conformation, 4IKY: Geobacillus kaustophilus POT transporter at 1.9 Å resolution in inward-facing conformation, and 4LDS: Staphylococcus epidermidis GlcP transporter at 3.2 Å resolution in inward-facing conformation). Predicted binding site used for docking is shown as orange spheres. B. Heatmap of docking scores of 225 known inhibitors and noninhibitors against the seven comparative models. Rows are clustered based on the similarity between docking scores of compounds. Columns are clustered based on similarity S 5

6 between all docked scores against each model. Red denotes favorable docking score and yellow unfavorable. Black dots on the right of the heatmap denote docking scores of known inhibitors. S 6

7 Predicted inhibition probability inhibitor noninhibitor Tanimoto similarity to training set Figure S4. Plot of predicted inhibition scores for 5805 DrugBank compounds versus highest pairwise ECFP Tanimoto similarity to training set. Each compound is shown as a circle. Compounds tested for inhibition in vitro are shown as filled circles (n=10): validated inhibitors are red and noninhibitors blue. Horizontal dotted lines are classification cutoffs (0.3 and 0.5) and the vertical dotted line is a pairwise 2D Tanimoto similarity cutoffs (0.4 and 0.6). S 7