239 Genome Informatics 17(2): 239{247 (2006) A Large-Scale Computational Approach to Drug Repositioning Yvonne Y. Li Jianghong An yli@bcgsc.ca jan@bcgsc.ca Steven J.M. Jones sjones@bcgsc.ca Canada's Michael Smith Genome Sciences Centre, 570 West 7th Avenue, Vancouver, British Columbia, V5Z 4S6, Canada Abstract We have developed a computational pipeline for the prediction of protein-small molecule interactions and have applied it to the drug repositioning problem through a large-scale analysis of known drug targets and small molecule drugs. Our pipeline combines forward and inverse docking, the latter of which is a twist on the conventional docking procedure used in drug discovery: instead of docking many compounds against a speci c target to look for potential inhibitors, one compound is docked against many proteins to search for potential targets. We collected an extensive set of 1,055 approved small molecule drugs and 1,548 drug target binding pockets (representing 78 unique human protein therapeutic targets) and performed a large-scale docking using ICM software to both validate our method and predict novel protein-drug interactions. For the 37 known protein-drug interactions in our data set that have a known structure complex, all docked conformations were within 2.0 A of the solved conformation, and 30 of these had a docking score passing the typical ICM score threshold. Out of the 237 known protein-drug interactions annotated by DrugBank, 74 passed the score threshold, and 52 showed the drug docking to another protein with a better docking score than to its known target. These protein targets are implicated in human diseases, so novel protein-drug interactions discovered represent potential novel indications for the drugs. Our results highlight the promising nature of the inverse docking method for identifying potential novel therapeutic uses for existing drugs. Keywords: drug repositioning, computational drug discovery, inverse docking, docking 1 Introduction An integral part of recent computational drug discovery research focuses on the high-throughput screening of chemical databases to nd inhibitors of speci c protein targets. However, drugs designed to interact speci cally with a certain target frequently also interact with other proteins. These o target interactions may cause harmful drug side e ects; however, they may also lead to new therapeutic opportunities. Understanding the potential o -target interactions of existing drugs is of major interest to pharmaceutical research, not only for providing insight into drug side e ects, but also for discovering novel therapeutic uses of drugs. Finding new indications for existing drugs, also known as drug repositioning, represents an ecient approach to drug discovery, since existing drugs already have clinical history and thus require much less time and money to develop into a drug speci c for the new disease [3]. Molecular docking is a computational method used to predict how a ligand molecule interacts with a protein binding site, both in terms of binding conformation and binding anity. This method is well established as a virtual screening method in drug discovery [13], where typically a large compound database is docked against a speci c protein binding site, in order to discover novel inhibitors Li et al. 240 of that target. The inverse scenario, whereby a speci c small molecule is docked to a panel of protein targets - so called `inverse docking' - has been little studied. This method was rst used by Chen and Zhi [9] in 2001 to predict potential o -target protein-drug interactions. Since then, a few studies have used it for predicting unanticipated kinase targets of several kinase inhibitors [17]. The combination of forward and inverse docking has been used in cross-docking studies for investigating enzyme-metabolite selectivity [15] and used in creating protein-ligand docking anity matrices for improving the enrichment factor of virtual screening [11]. However, inverse docking is also suited to computational drug repositioning analysis. By docking an existing drug to a panel of known therapeutic targets, the potential of the drug to bind targets other than its intended can be assessed, thus leading to potential drug therapies for other diseases. Such a method would be particularly valuable for improving the eciency of current drug discovery pipelines. Here, we present a computational approach that combines forward and inverse docking for the drug repositioning problem. ✘✘✙ ✙ ✝ ✚ ✛ ✜✠✠✂✁✂✝ ✁✡✛ ✍✗✔✒✜✎✍✂✄✡✔✑☞☎✒✎✂✓✠✏✂☎✒✝ ✌ ✁☞✎✍✄✖✁✂✔✔✡✡✑✄✕✕☛☞☎☎✒✗✆✒✎✂✍✓ ✆☎☞✚ ✞ ✟ ✄✄✢ ☎ ☞✔☞✖✕✒☎✡✍✁☛✄✤✡✁☛✏✝ ✏ ☎☞✡✕✠✁✔✄✂✏✠✏✌ ✡ ☛ ☞✣ ✧ ✚ ✛ ✝ ✂☛✒✎✄✍✓✦✡✆☞✁☎ ✜✥ 2 Method and Results 2.1 Molecular Docking Pipeline The automated molecular docking pipeline was implemented using the ICM software package [1] and is illustrated in Figure 1. Figure 1: The molecular docking pipeline. Given a list of Protein Data Bank (PDB) identi ers, the protein 3D structures are retrieved from PDB and are rst required to be X-ray crystal structures with a minimum resolution of 2.5 A. The structures are then separated into chains as annotated in PDB. For structures with multiple chains, its chains are grouped into a set of non-redundant sequences, based on PDB's chain redundancy analysis at the 95% sequence identity level [5]. The PDB structures are prepared for docking by adding and optimizing hydrogens and by removing water molecules, metal ions, and other solvent molecules. Pockets are predicted in the structures using the PocketFinder algorithm, which is based on a transformation of the van der Waals potential [2]. This algorithm has been shown to have excellent predictive capability for human pockets: when applied to a large and comprehensive data set of 17,126 human protein pockets, PocketFinder correctly predicted 96.8% of holo pockets and 95% of apo pockets [2]. Predicted pockets are then further ltered for quality. First, they are de ned at a cut-o of minimum 150  A3 volume. Second, when multiple pockets are predicted in a protein, the two largest A Large-Scale Computational Approach to Drug Repositioning 241 are retained. Though this will inevitably omit real druggable sites, the PocketFinder method has shown that its two largest predicted pockets may cover as many as 92.7% of real binding sites [2]. Third, pockets that have residues within 3.5 A with occupancy values less than 1 or temperature factors (B-factors) greater than 60 are removed - these are non-stringent cut-o s used only to detect the most unreliable residues. Finally, the protein's observed PDB sequence is aligned to its known SwissProt sequence and pockets that have residues within 3.5 A which are engineered mutations or missing in the observed sequence are removed. An example of predicted pockets for the Hemoglobin alpha chain protein is shown in Figure 2. For each pocket, the receptor is de ned as a box with a 3.5 A margin around the outermost points of the pocket. The small molecule database is prepared as a multi-object sdf le and docked to each pocket. ICM performs grid-based rigid-receptor exible-ligand docking through a modi ed Monte-Carlo searching procedure and a rigorous empirical scoring function. ICM has performed well in recent assessments of popular docking packages (including, Glide, GOLD, Autodock, DOCK) [6, 8], and has shown topranking pose prediction accuracies of 45% [16] and 50% [8] in studies using small protein benchmark sets. After docking, the scores and conformations of high scoring compounds are gathered for further analysis. For proteins with multiple pockets, the docking score of the best scoring pocket is used to represent the protein-small molecule interaction. These time-intensive pocket prediction, preparation, and docking steps are rendered feasible by using a 400-processor cluster, on which we have ICM licensed for 200 processors. 2.2 Data Set: Binding Pocket Database and Small Molecule Drug Database We drew known protein-drug interactions from DrugBank [18], a manually curated database holding detailed information on drugs and protein targets. DrugBank contains therapeutically useful interactions for over 4,000 drugs, including FDA-approved, experimental, biotech, and nutriceutical drugs, corresponding to over 6,000 drug targets. For our analysis, we rst focused on the 1,055 FDA approved drugs and their corresponding 510 human proteins targets. 447 SwissProt identi ers of therapeutic targets corresponding to 1,055 known small molecule drugs were retrieved from DrugBank. 1,345 X-ray PDB structures with resolution better than 2.5 A were obtained for 165 of these proteins for which 246 known drugs corresponded. This resulted in 1,836 nonredundant PDB chains with at least one chain per structure. PocketFinder predicted 2,272 pockets when the two largest pockets over 150 A3 were retained. After the B-factor and occupancy ltering step, 2,082 pockets remained. After removing pockets with nearby engineered mutations or gaps, 1,548 pockets remained, representing 78 unique human proteins. The 1,055 approved small molecule drugs with sdf representation were downloaded from DrugBank. Known protein-drug interactions used to assess docking results were obtained from DrugBank's Approved DrugCards ( le dated 2005/6/27). 237 of them are represented by proteins and drugs in our data set. 2.3 Re-Docking Known Protein-Drug Complexes Of the 237 known protein-drug interactions in the data set, 37 interactions (representing 19 unique proteins) have already been solved in a PDB structure complex. We rst veri ed whether these interactions could be predicted using our pipeline. The advantage of this benchmark data set is that both the binding site and binding conformations for the ligand are known. We docked cognate ligands to predicted protein pockets for these 37 interactions. For this smaller data set, we performed a more rigorous pocket prediction in that pockets containing metal ions were prepared with the metal ion present. In addition, for multi-chain proteins, we also predicted inter-chain pockets (one example is shown in Figure 2). There were 101 pockets in total. The results are summarized in Table 1, listing for each interaction the docking score and the root mean square deviation (RMSD) value between the docked ligand and the PDB conformation. All 37 Li et al. 242 Figure 2: Left: The two pockets predicted for PDB entry 1R1X (Hemoglobin alpha chain) overlap well with known ligand positions. Right: The binding pocket (darker glob) of PDB entry 1TCO is comprised of three separately colored chains. complexes exhibit a RMSD value less than 2.0 A which is the standard cuto used to de ne a successful molecular docking. 31 of the 37 complexes exhibit an RMSD value less than 1.0 A which is a much more rigorous measure of successful docking [8]. A couple of docked examples in Figure 3 illustrate that even large exible ligands are docked correctly. The low RMSD values also show that despite retaining two pockets for each protein, the best scoring ligand conformation always docked to the correct pocket. However, though the RMSDs were all well predicted, docking scores in only 29 of the 37 complexes were smaller (better) than the default ICM score cuto (which is -32). Further investigation shows the poorly scoring complexes all involve pockets with a metal ion present. It should be noted that we also tried docking without the metal in the pocket, and though the scores were more reasonable (around -25), the RMSDs were greater than 2.0 A. Thus it seems, at the moment, we are not able to predict reliable scores for pockets containing metals. Overall, these results show that the docking and scoring algorithms are quite accurate predictors of actual protein-drug binding when the protein is already in a correct conformation. Table 1: Docking score and RMSD values for the re-docking of the 37 structure complexes. PDB PDB RMSD Dock PDB PDB RMSD Dock PDB PDB RMSD Dock protein ligand ( A) score protein ligand ( A) score protein ligand ( A) score 1K74 REA 0.8 -71.0 1BRP RTL 0.5 -48.7 1UHO VDN 1.3 -35.7 1FM6 REA 0.4 -70.1 3ERD DES 0.3 -48.4 1FKJ FK5 0.8 -35.5 1FM9 REA 0.5 -69.7 1SQN NDR 0.4 -45.9 1BKF FK5 0.9 -35.1 1FBY REA 0.6 -67.9 1HWI MMM2 0.5 -45.7 1MMK BH4 0.4 -33.1 1XZX T3 0.5 -60.6 1RBP MMM2 0.7 -58.8 1Z95 2LBD REA 0.4 -58.0 1XP0 1T46 STI 0.4 -56.4 1HWK RTL 0.4 -44.0 1KW0 BH4 0.5 -29.8 0.4 -42.8 1XOZ CIA 0.7 -25.1 VDN 1.3 -39.6 1XLX CIO 1.5 -19.4 1FKB RAP 0.7 -39.6 1UZF 0.5 -14.9 MMMM AMCO 3LBD REA 0.4 -55.9 1NB9 RBF 0.8 -39.0 1J8U THB 1.7 -12.5 1HWJ MMM2 0.4 -54.7 1TBF VIA 0.8 -38.9 1CIL ETS 0.6 -9.9 1TCO FK5 0.8 -52.9 1FKF FK5 0.7 -36.7 1A42 BZO 1.7 -7.7 1A28 ASTR 0.3 -50.4 1HWL FBI 0.5 -36.0 1AZM AZM 1.4 -7.7 1M2Z DEX 0.4 -49.4 A Large-Scale Computational Approach to Drug Repositioning 243 Figure 3: Left: 1HWK (HMDH HUMAN) is shown with complexed ligand Atorvastatin and its docked conformation. Right: The ligand Alitretinoin of 2LBD (RARG2 HUMAN) is shown in its predicted pocket along with the docked conformation. 2.4 Large-Scale Docking A large scale docking using ICM was carried out for the 1,055 approved drugs against the 1,548 pockets. Proteins represented by multiple PDB structures (i.e. multiple pockets) use the best scoring drug-pocket interaction as the protein-drug docking score. Results are illustrated in Figure 4 with the 1,055 drugs along the horizontal and 31 of the 78 proteins along the vertical. Though ICM recommends a docking score cut-o of -32, this value should ideally be tailored to each receptor. For example, a study using ICM docking to EGFR discovered novel inhibitors with a cut-o value of -28 [7]. We chose to apply a simple receptor-speci c threshold system. For proteins belonging to the 37 known complexes in the re-docking analysis, we used those docking scores as cut-o s. If protein is known to bind several drugs, as is often the case in our data set, the cut-o was the poorest known-drug-score (but still better than the default -32). These cut-o s are the most reliable since it is known that the drug docked to the correct binding site with a correct conformation. For proteins that are not in a known structure complex but showed a good docking score (better than -32) with a known interacting drug, we used this docking score as the cut-o . These cut-o s are less reliable since without a 3D structure, we cannot be absolutely sure that the drug docked to the correct protein pocket with a correct conformation. The remaining 47 proteins of the 78 did not have any known interactions predicted with a good score and are not shown in Figure 4. Of the 237 known protein-drug interactions, 74 show a docking score better than -32. In such cases, it is interesting to examine how a protein ranks when the drug is docked to all proteins (`ProteinRank' or inverse docking rank) as well as how the drug ranks when all drugs are docked to the protein (`DrugRank' or docking rank). Thus, ProteinRank ranges from 1 to 78 whereas DrugRank ranges from 1 to 1,055. The ranks for a few of the 74 good-scoring known interactions are shown in Table 2, illustrating several di erent rank combinations. When both ranks are high, the protein and drug are speci c to each other. A ProteinRank that is not 1 suggests that the drug may bind to another target as well as the primary target. A DrugRank that is not 1 suggests that other drugs may have potential to bind this protein. In total, 52 of the 74 interactions do not have a ProteinRank of 1, and are thus interesting for further investigation. Li et al. 244 Figure 4: Map of large-scale docking results, with speci c scoring thresholds applied for each protein. The 1,055 drugs are represented on the horizontal and the 31 proteins having a good docking score with a known drug are on the vertical. Protein-drug interactions whose docking score does not surpass their designated cut-o are shown as black and those that do are shown in gray (where brighter shades of gray represent better scores). For example, in the case of Acitretin and Retinoic acid receptor gamma-1 (RARG1 HUMAN) for treatment of severe adult psoriasis, some top targets for Acitretin are shown in Table 3. DrugBank annotated 7 targets, 2 of which do not have a solved human structure, leaving 5 targets in our input data set. All ve targets (shaded rows) show good docking scores with this drug, despite that there are no solved structures of this drug in complex with any protein. The top ranking target is serum albumin, and though it was not annotated in DrugBank as a drug target, it was noted that in the systemic circulation, over 98% of Acitretin is bound to lipoproteins or albumin in the blood [10]. The remaining targets are novel potential targets of this drug, and should be the rst to be experimentally tested for a laboratory interested in Acitretin repositioning. In Table 2, it can be seen that Acitretin is the 9th ranked drug for RARG1 HUMAN. There are thus 8 other known drugs that have potential to be repositioned for severe psoriasis. Table 2: The ProteinRank and DrugRank for several known protein-drug interactions. Proteins are denoted by their SwissProt identi ers. Protein Drug RARG1 HUMAN Alitretinoin KIT HUMAN Imatinib PRGR HUMAN Medroxyprogesterone RETBP HUMAN Vitamin A RARG1 HUMAN Acitretin ICM Score Protein Rank Drug Rank -59.1 -49.0 -47.8 -52.6 -46.3 1 1 1 3 2 1 1 14 1 9 When both ranks are 1, there is no drug in the database that docks better to the protein target, and there is no other protein to which the drug docks better. This means that the protein-drug combination is ideal within the scope of the data set, and no better target can be found for the drug. This situation may still be interesting for drug repositioning in two scenarios: rst, if more proteins are included in the data set, the rankings may change; second, ICM docking does not purport to rank results by binding anity - thus, docking scores lower than a known interaction (though they should still be close to or better than the default -32 score) may also re ect potential binding interactions. For example, imatinib is a well-known inhibitor of the Bcr-Abl fusion protein for the treatment of chronic myeloid leukemia. In our docking analysis, the ABL-imatinib interaction was docked with a score of -31.6, not passing the score threshold. However, closer inspection of the human ABL structure, A Large-Scale Computational Approach to Drug Repositioning 245 Table 3: Inverse docking results for Acitretin. Proteins are denoted by their SwissProt identi ers. Known targets are shaded. The remaining proteins are candidates for further drug repositioning analysis. Rank 1 2 3 4 5 6 7 8 9 10 16 17 18 Protein ALBU HUMAN RARG1 HUMAN DYR HUMAN PTN1 HUMAN RARA HUMAN NOS3 HUMAN HBB HUMAN GSHR HUMAN RXRA HUMAN THB1 HUMAN FA10 HUMAN RARG2 HUMAN RARB HUMAN Docking score to Acitretin -48 -46 -46 -45 -42 -42 -40 -39 -39 -39 -36 -36 -35 1OPL, shows that it is bound to a di erent inhibitor, and is in a conformation not ideal for binding imatinib due to steric clashes. We then added mouse ABL PDB structures (with 98% sequence identity to human ABL) into our pipeline, and they showed an ABL-imatinib interaction score of -58.4. This is higher than the KIT-imatinib interaction in our current analysis and thus mouse-ABL would become the top ranking target for imatinib instead of KIT. Imatinib is known to inhibit c-KIT, a protein kinase linked to gastrointestinal stromal tumor, and was shown to be e ective for this cancer [12]. Thus, this KIT-imatinib interaction was correctly predicted in our docking analysis. 3 Discussion & Conclusion Conceptually, inverse docking models a more realistic biological environment - once a drug enters the body or speci c cells, which of the multitude of di erent proteins will it bind to? Inverse docking methods have potential in many important areas of drug discovery, such as for predicting the unwanted side e ects for known and candidate drugs, determining druggable protein targets, and as we particularly address in this study, predicting potential bene cial therapeutic uses for known drugs. To our knowledge, this represents the rst time that inverse docking has been used for drug repositioning analysis. Given the tremendous time and cost of drug discovery today, last estimated at 12 years and $900 million US to discover and develop one new drug [14], inverse docking may allow for more ecient drug discovery pipelines to be implemented. However, there are still many challenges to be overcome for this type of analysis, which have been especially noted during our study. Important implications arise from the limitations of current molecular docking methods. First, docking is heavily dependent on the input protein structure. A strong point of this study was to carefully lter for the quality of pockets prior to docking. For example, poorly resolved structures cannot be reliably used in docking as the protein atom positions are already imprecise. Similarly, pockets having nearby residues with low occupancy values or high temperature factor values are also unreliable for docking. Pockets with nearby engineered mutations are not representative of wild-type binding pockets, and pockets with gaps in their nearby observed sequence may be missing key residues. Second, docking methods today do not easily handle protein exibility, presence of water and metal ions, and accurate scoring. Protein exibility is partially compensated for when multiple structures 246 Li et al. have been solved for the same protein in PDB. However, this a very small corner of protein conformational space, so it is expected that the predictive ability of inverse docking will be much improved with the development of scalable exible-receptor exible-ligand docking methods. For protein-drug complexes with metals, our docking accurately predicted drug binding conformations but failed to predict reasonable scores. We have tried to minimize scoring issues by only further analyzing proteins that docked to known drugs with good scores, since these proteins are more likely to be in conformations amenable for small molecule inhibition. These proteins are also more likely to be scored appropriately. We are currently evaluating di erent scoring methods to better represent metal ions. In the future, we plan to explore exible-protein exible-ligand docking with ICM and with other docking software. One major challenge in inverse docking is comparing docking scores across di erent pockets, as the score threshold should ideally be speci c to each receptor. In a best case scenario, a large set of known inhibitors with known binding anities would be docked to each protein pocket, to determine an appropriate docking score cut-o for expected small molecule binders. Such a task, however, would require an immense amount of literature search, and is often impossible as many protein pockets have not yet been characterized using diverse ligands. We have used a simple initial approximation by using the docking scores of known protein-drug complexes from PDB and interactions from DrugBank as a reference point for our receptor-speci c docking score cut-o s. In addition, we limit our investigation of novel protein-drug interactions to only the dataset where known protein-drug interactions could be reproduced. We are currently gathering more binding anity information from the literature to better tailor the docking score cut-o s. Finally, our study is limited by the number of known proteins and drugs in the input data set. Having only 78 unique protein targets limits the utility of ProteinRank, as some of top-ranking targets are likely to change when more proteins are added to the data set. By using only human protein structures in our analysis, many informative structures were omitted - for example, the mouse ABL structures. Thus, we are currently incorporating structures from other organisms aside from human, when their sequence identity to the human protein is above 95%. In addition, we are including known protein targets for experimental drugs as annotated in DrugBank - for example the LCK protein which is a therapeutic target critical in T-cell receptor signaling [4] - but no drugs have yet been approved for it. Despite the limitations discussed, inverse docking opens a new avenue for computational drug discovery, with the potential to discover more about both adverse e ects and novel therapeutic targets of existing drugs. Our analysis has begun to address some limitations by applying stringent pocket quality lters, requiring targets to have a well-docked known drug before further analysis, and introducing receptor-speci c docking score cut-o s. By docking 1,055 known drugs to 1,548 pockets of 78 proteins we have found 52 interactions with potential pharmacological relevance, thus showing the ability of the inverse docking method as a tool for drug repositioning. The analysis has also pointed out many limitations of a large scale inverse docking analysis, which we are currently addressing, and we anticipate that an improved pipeline will be able to better predict known and novel protein-drug interactions. 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