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Impact of protein binding cavity volume (PCV) and ligand volume (LV) in rigid and flexible docking of protein–ligand complexes
Bioorganic & Medicinal Chemistry Letters 22 (2012) 7593–7597
Contents lists available at SciVerse ScienceDirect
Bioorganic & Medicinal Chemistry Letters journal homepage: www.elsevier.com/locate/bmcl
Impact of protein binding cavity volume (PCV) and ligand volume (LV) in rigid and flexible docking of protein–ligand complexes N. Saranya a, J. Jeyakanthan b, S. Selvaraj a,⇑ a b
Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, India Department of Bioinformatics, Alagappa University, Karaikudi 630003, Tamil Nadu, India
a r t i c l e
i n f o
Article history: Received 29 May 2012 Revised 26 September 2012 Accepted 2 October 2012 Available online 11 October 2012 Keywords: Protein binding cavity volume Protein flexibility Structure-based drug design Docking and scoring
a b s t r a c t The importance of protein binding cavity volume (PCV) and ligand volume (LV) in rigid and flexible docking has been studied in 48 protein–ligand complexes belonging to eight protein families. In continuation of our earlier study on protein flexibility in relationship to PCV and LV, this study analyzes the importance of PCV and LV in the scoring and ranking of ligands in docking experiments. Crystal structures of protein– ligand complexes with varied PCV were chosen for docking ligands of varied volume in each protein family. Docking and scoring accuracy have been evaluated by self and cross docking of ligands to the given protein conformation. Effect of PCV and LV in rigid and flexible docking has been studied both in apo and holo proteins. Rigid docking has performed well when appropriate protein conformation is used. Selecting the proteins with appropriate PCV based on the LV information is suggested for better results in ensemble docking. Ó 2012 Elsevier Ltd. All rights reserved.
With consideration of cost and time in wet lab techniques, in silico approaches favor the fast and effective retrieval of actives from the huge number of compounds in databases for a particular target. Docking and scoring is performed to rank the compounds in appropriate binding mode and pose.1–4 Ranking of compounds accurately from the pool of compounds is of utmost importance. A number of factors such as charge, hydrogen bond interaction, hydrophobic interaction, electrostatic potential, van der Waals forces etc. are involved in the binding of a particular ligand to the protein binding cavity in terms of specificity and potency. Protein flexibility also plays a major role in the accurate scoring and ranking of ligands.5–7 In docking and scoring methods, the input protein conformation may be a holo structure (protein cocrystallized with ligand) or an apo structure (protein crystallized without ligand). In most docking software, flexibility of the ligand alone is considered and protein is kept in rigid conformation. Protein flexibility can be induced by methods such as molecular dynamics simulation methods, graph theoretical and geometry based approaches and harmonic methods.5–11 Incorporation of protein flexibility by ensemble docking and scoring using multiple protein conformations is also possible.12–15 Ensemble docking provided higher enrichments compared to individual receptor docking.14 In addition, if a set of active compounds are found, then induced fit docking was proposed to be a reliable way for construction of ensembles. The performance of
⇑ Corresponding author. E-mail address: [email protected] (S. Selvaraj). 0960-894X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.bmcl.2012.10.018
ensemble docking in virtual screening has been assessed, in which protein structures (from crystal–ligand complexes, molecular dynamics and replica exchange molecular dynamics) were selected based on the clustering of binding site shape.16 In this study of accounting protein flexibility, the authors have emphasized the importance of binding site characteristics in selecting diverse ensemble structures to obtain higher enrichment rates and diverse actives. Erickson et al.17 have performed docking experiments to examine the various factors that influence docking accuracy including ligand and protein flexibility. It has been shown that docking accuracy falls off when average or apo structures were used. Docking performed with protein conformation accommodating largest ligand has resulted in higher selectivity; further, ensembles incorporating 3–5 of these experimental conformations improved the prediction accuracy.18 Similar results of choosing 3 best conformers for ensemble docking based on mean Glide score has also been reported.19 A number of studies have compared and assessed docking and scoring programs to reproduce the experimental results in diverse protein–ligand complexes.20–24 Uncertainty prevails in many docking and scoring programs for the accurate prediction of binding affinity and pose in protein–ligand complexes. In a comparative study of eight docking tools, docking/scoring inaccuracies were discussed based on the protein binding cavity volume, ligand buried surface area and number of rotatable bonds.20 Binding pocket specific scoring functions are suggested for appropriate scoring of ligands in different protein families.25 Simon and co-workers26 have implemented rank, enrichment and affinity tuning approaches in eHITS program to improve the docking and scoring accuracy in
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different protein targets. The accuracy of eighteen scoring functions were evaluated by self and cross docking of ligands into flexible and solvated proteins of different classes.27 The impact of input ligand and protein conformation, and also the protein flexibility and water molecules on the docking accuracy have been reported using Glide, GOLD, SURFLEX, FlexX and Fitted 2.6 programs.28 A single protein binds different ligands and adopts different conformations. Ligand defined conformational change occurs in the binding site of the protein.5 Docking and scoring becomes ineffective using randomly chosen protein conformation. Due to the flexible nature of the protein, its conformation and PCV also changes on binding diverse ligands. When a ligand binds protein, induced fit causes conformation change of both protein and ligand such that the binding cavity volume changes for each ligand of the same protein. A protein crystallized with a ligand adopts a particular conformation. Volume of this cavity fits that particular ligand well. The flexibility of the protein binding cavity is different for different proteins.5 When docking is performed using the structure crystallized with the native ligand, it is likely that it docks and scores better. Thus, PCV and LV are important properties of the protein and ligand such that docking and scoring analysis based on the changes in PCV and LV in different protein families may provide insight for the accurate scoring of ligands. In our earlier study,29 we have reported the variation of PCV and ligand volume (LV) in eight protein families namely Trypsin, Factor Xa, Carbonic anhydrase (CAR), Thrombin, Acetyl choline esterase (ACE), HIV-1 protease, HSP 90 and Cyclin-dependent kinase-2 (CDK-2) given in order of increasing flexibility. This study has provided insight into the extent of variation of PCV and LV in the eight protein families considered. High correlation of atom-atom interactions and ligand volume revealed the presence of ligand defined plasticity in the protein–ligand complexes. In the present work, we have analyzed the impact of PCV and LV changes in docking and scoring of known ligands in eight protein families. Details regarding the major features and biomedical importance of the proteins examined in this study are given in Table 1. For this purpose, protein–ligand complexes observed to have different PCV and LV in each protein were selected. Ligands of different volumes were docked to the protein of different PCV or conformation. Ligands were both self and cross docked with their corresponding proteins. The influence of PCV variation in docking and scoring of cognate and non-cognate ligands in a given protein has been analyzed by performing both rigid and flexible
docking. In addition to holo protein docking, we have also docked (rigid and induced fit docking) the ligands to their corresponding apo protein structures. While a number of docking studies have been reported with regard to evaluation of accuracy of ligand pose and score prediction, the present study mainly focuses on the influence of binding cavity volume and ligand volume in the accurate scoring of ligands. Selection of protein–ligand complexes was performed based on the 200 protein–ligand complexes reported in our earlier study.29 40 protein–ligand complexes from eight protein families were used for present docking study. Five protein–ligand complexes from each protein family were selected in such a way that PCV and LV values varies largely within a given protein family. The atomic coordinates of both apo and holo structures were obtained from RCSB Protein Data Bank (PDB) (www.rcsb.org).30 Experimental binding affinity values have been obtained from the Binding MOAD31 and PDB.30 PCV and LV were computed using Pocket finder server32 and molinspiration server33 respectively. Table S1 provides details regarding the PDB ID, Ligand HETATM ID, PCV, LV, rotatable bonds of ligands and binding affinity data of protein– ligand complexes used in the present study. Glide Extra Precision (XP)34 and Induced Fit (IF) docking35 modules from Schrödinger were used for rigid and flexible docking study. In Glide XP docking, only ligand poses are generated and the receptor remains rigid. In IF docking low energy conformations of receptor are generated for a particular ligand pose. Each protein was prepared for docking using protein preparation wizard available in Schrödinger package. In this task, the proteins were checked for steric clashes, missing loop, side chains and charges were added and minimized using OPLS 2005 force field. Missing side chains were added in HIV-1 protease (1B6J, 1HPO), CDK-2 (1OIQ), HSP 90 (1UY8, 1UYG, 2BYI and 2BT0) ACE (2ACK and 2CKM) and Factor Xa (2J38) proteins using the Prime module in Schrödinger package. Ligands were prepared using LigPrep module. Receptor grid generation was used to set up grid for ligand docking site. An overview of the scheme of computational analysis is given as a flowchart (Fig. 1). In Glide XP and IF docking, top most scored ligands are ranked in particular lowest energy conformation and pose based on their interaction with protein in particular conformation. Rigid and flexible docking accuracy have been evaluated based on the scoring of cognate ligands in a given protein. Efficiency for top scoring of cognate ligands among non-cognates in rigid and flexible docking has been compared and analyzed. Consensus scoring for a given ligand
Table 1 Major features of the proteins examined in the present study Protein
Features
Trypsin
Trypsin is a pancreatic serine protease and is involved in the biological processes such as digestion of proteins, blood coagulation, complement activation, fibrinolysis, kinin formation, reproduction, and phagocytosis. This enzyme acts as a therapeutic target in the variety of disease states including pancreatic diseases, including coronary and cerebral infraction, vascular clotting, pancreatitis, arthritis, and tumor cell invasion. Thrombin is a trypsin-like serine protease involved in a multitude of processes including blood clotting, inflammation and wound healing. Thrombin is a major target for the pharmacological prevention of clot formation in coronary thrombosis. Carbonic anhydrase assists in the rapid inter-conversion of carbon dioxide and water into carbonic acid, protons and bicarbonate ions. Carbonic anhydrase inhibitors are clinically used as diuretic, antiglaucoma, anticonvulsant, and antiobesity drugs and also in the management of hypoxic tumors. CDK-2 is involved in the regulation of cell cycle proliferation and RNA polymerase II (RNAP II) transcription cycle. Deregulation of CDK leads to various disorders such as cancer, diabetes, protozoan infections, nervous disorders, etc. Acetylcholinesterase is found in the synapse between nerve cells and muscle cells and when signal is passed, it breaks down the acetylcholine into its two component parts, acetic acid and choline. This protein is a drug target for Alzheimer’s disease. HSP 90 is an ATP-dependent molecular chaperone that has several oncogenic client proteins involved in signal transduction, cell cycle regulation, and apoptosis and remains a potential anticancer drug target. HIV-1 protease, which is an essential enzyme in the life cycle of HIV for the protein maturation and viral infectivity, is one of the active targets in the treatment of HIV. Factor Xa plays a key role in the blood coagulation cascade in which it converts prothrombin to thrombin and holds a central position linking the extrinsic and intrinsic activation pathways to the final common coagulation pathway. Compared to thrombin, Factor Xa was found to be more advantageous as a molecular target for the development of anticoagulant drugs for the treatment of thromboembolic disorders.
Thrombin Carbonic anhydrase
Cyclin-Dependent kinase-2 (CDK-2) Acetylcholine esterase Heat shock protein 90 (HSP 90) Human Immunodeficiency Virus (HIV)-1 protease Factor Xa
N. Saranya et al. / Bioorg. Med. Chem. Lett. 22 (2012) 7593–7597
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Figure 1. An overview of the computational analysis.
was computed by calculating average of scores obtained in each conformation of a protein. To evaluate the accuracy of prediction, negative logarithm of experimental binding affinity value was correlated with docking score using Pearson correlation method in holo proteins. Induced fit generated protein conformation was compared with experimental protein conformation to evaluate the volume variation in docked protein–ligand structures. Experimental PCV of holo and apo proteins were compared with PCV of IF generated protein structures by self and cross docking of ligands. Pocket finder server32 was used for the computation of PCV of induced fit generated protein structures. Standard deviation was computed to evaluate the deviation of PCV observed in induced fit docked structures on docking different ligands in a given protein conformation. The accuracy of docking and scoring was analyzed based on the ranking of cognate ligands among the non-cognates docked in different conformations of each protein. The rigid and flexible docking scores computed for the eight protein families are given in Table S2. The cognate ligands scored better in rigid docking compared to flexible docking. Rigid docking performed better than flexible docking in thrombin, HIV-1 protease, HSP 90 and CDK-2 proteins (Fig. 2). Equal performance of both rigid and flexible docking was observed in trypsin, CAR, thrombin and ACE proteins. Protein
Figure 2. Percentage accuracy of rigid and flexible docking and scoring of cognate ligands in eight protein families.
binding cavity flexibility (based on PCV) increases from trypsin to CDK-2. Likewise, performance to score and rank the cognate ligand also increases. In Factor Xa, none of the cognate ligands scored high in flexible docking. Out of the 5 ligands docked to different conformations of the same protein, two cognate ligands of HIV-1 protease, HSP 90 and three cognate ligands of thrombin and ACE scored top in flexible docking. In rigid docking, maximum of three cognate ligands scored correctly in Thrombin, ACE, HSP 90 and CDK-2. For HIV-1 protease a maximum of 4 cognate ligands scored correctly in rigid docking. Approximately, 50% and 30% of the cognate ligands best scored in rigid and flexible docking respectively. However, it needs to be pointed out that the objective and dataset of the present study is not to assess the performance of GLIDE XP or IF, but rather to examine the influence of PCV and LV in docking and scoring. Protein binding cavity in bound conformation with a ligand of particular volume scores the same ligand as best only when appropriate input protein conformation is provided for docking. Same protein in different conformation scores ligands variedly. This necessitates the consideration of PCV in docking studies with respect to LV. If appropriate experimental protein conformation is provided, better results could be obtained by rigid docking than flexible docking. The results thus imply the importance of input protein conformation with appropriate PCV in scoring the given ligand in rigid docking. The accuracy of prediction of experimental binding affinity in docking experiments has been evaluated. Pearson correlation was computed to find statistical relationship between docking score and experimental binding affinity value. High correlation was observed for ACE compared to other proteins. In an extensive study to test fourteen scoring functions, trypsin and carbonic anhydrase docking scores had better correlation with experimental binding affinity.36 A negative correlation was observed for holo and apo proteins of trypsin, Factor Xa and carbonic anhydrase in the present study. Similar results of low correlation have been reported between docking score and experimental binding affinity data in comparative docking studies.37–39 Consensus scoring of ligands in all the conformations for a given protein has been correlated with the experimental binding affinity. Figure 3 provides details regarding the correlation of rigid and flexible consensus docking score and experimental binding affinity in the eight protein families. Consensus scoring has yielded better results compared to scoring using individual conformation. Rigid and flexible docking showed
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Figure 3. Correlation of rigid and flexible consensus docking score and experimental binding affinity in eight protein families.
positive correlation for Thrombin (r values 0.53 and 0.46), ACE (0.54 and 0.42) and HIV-1 Protease (0.04 and 0.54) proteins respectively. Correlation between PCV and LV was observed for the 5 protein–ligand complexes from each of the 8 protein families (Fig. 4). A higher correlation value describes the increase or decrease in PCV in correspondence with change in LV. ACE had a correlation of 0.94, ranking top among all other protein families. Trypsin (0.75) had the second highest correlation values followed by the CDK-2 (0.34), CAR (0.27) with positive correlation. Positive correlation represents the increase in PCV as the LV increases. Low correlation was observed in HIV-1 Protease, Thrombin, Factor Xa and HSP 90 proteins. Flexible docking of ligands (cognate and non-cognate) into protein of particular PCV results in structures with different PCV. Tables S3 and S4 provides the details regarding the PCV of induced fit structures for apo and holo proteins. Analysis has been made on the variation of PCV on docking cognate ligands to an experimental conformation. In 1DIF conformation of HIV-1 protease, exactly the same PCV of 700 Å3 was reproduced on docking cognate ligand A85 which has been second best scored (Fig. 5). Standard deviation was observed to be higher of 267 Å3 for the ligand F11 of ACE which has been top scored. Though discrepancies prevail in the analysis of PCV and scoring, approximately 85% of the proteins have PCV standard deviation within 100 Å3 in flexible docking. Flexible proteins have scored cognate ligands accurately compared to rigid proteins. Accuracy of scoring cognate ligands was less observed in rigid proteins like Trypsin, Factor Xa and CAR compared to other flexible proteins (Table S5).
Figure 4. Correlation (r) of PCV versus LV.
Variation of PCV in IF generated structures on docking different ligands to a protein of particular PCV has also been analyzed. For each protein family, standard deviations can be given in the order of Trypsin (20.34 Å3), Factor Xa (39.55 Å3), HSP 90 (43.42 Å3), CAR (44.67 Å3), HIV-1 protease (56.99 Å3), thrombin (88.82 Å3), ACE (90.23 Å3) and CDK-2 (119.37 Å3) for holo proteins. Flexible docking of apo proteins reported the standard deviation of Trypsin (33.98 Å3), Factor Xa (43.24 Å3), CAR (28.80 Å3), Thrombin (97.24 Å3), ACE (86.96 Å3), HIV-1 Protease (57.53 Å3), HSP 90 (49.58 Å3) and CDK-2 (72.73 Å3). Variation of PCV was observed to be low for less flexible proteins like Trypsin, Factor Xa and high for more flexible proteins like CDK-2 and ACE in holo protein flexible docking.29 This trend was not observed in apo proteins where higher variation of PCV was reported in Thrombin and ACE compared to HIV-1 Protease, HSP 90 and CDK-2 proteins. Preference for high scoring of non-cognate ligands which is of nearest volume (greater or smaller) to cognate ligand in rigid and flexible docking was analyzed. Over all analysis of best scoring of nearer volume ligands reported a percentage of 55% and 60% in rigid and flexible docking respectively. In rigid and flexible docking, ligands with higher volume scored well compared to ligands with lesser volume. Irrespective of PCV, higher volume ligands scored better. Approximately, 55% and 65% of the higher volume ligands were scored best in rigid and flexible docking respectively. A number of studies have reported the controversy of scoring larger ligands better as this parameter adds weight to the scoring function.16,36 In most protein families, docking of non-cognate ligands to the same protein but in different conformation remains challenging. In each protein, apo protein docking was able to dock all the ligands in the given conformation compared to holo protein docking in both rigid and flexible docking. Table S6 provides details of the ligands that were not docking to protein of given conformation. As with experimental structures of same PCV bound with different ligands,29 flexible docking has also generated same cavity volume structures with different scores. Ligands 120, BAZ docked to 1AQ7 conformation of HIV-1 protease and UNI, PI1 docked to 1B6J conformation of trypsin have different scores with same PCV of 139 Å3 and 739 Å3 respectively. In summary, the effect of choosing protein conformation in docking different ligands using rigid and induced fit docking has been assessed in eight protein families. Accuracy of scoring cognate ligands was observed to be better for rigid docking compared to flexible docking in holo proteins. Consensus scoring was able to reproduce experimental binding affinity in few proteins which showed positive correlation. Selection of protein input structures based on LV information in the docking experiments may yield better results. In a docking experiment, volume information of ligands to be docked should be matched with the ligands in crystal structures for the selection of protein input conformation. The availability of large number of crystal structures of protein–ligand complexes fulfills this requirement of input conformation selection. Though, it is not possible to find an experimental protein conformation for a given LV (test set to be docked), the closely related protein conformation crystallized with identical or similar volume ligands will remain a suitable input. As ligands with nearest volume to cognate ligands was observed to have high score, selection of ensemble protein structures bound with identical or similar volume ligands will serve the need of incorporating protein flexibility in finding actives when a set of compounds with similar volume and characteristics are available for screening. As some ligands of the same protein were not even docked in different protein conformations, this type of selection of protein ensembles will reduce the problem of false positives and negatives in the screening process. Apo protein structures were able to dock ligands of varied volume in the binding site
N. Saranya et al. / Bioorg. Med. Chem. Lett. 22 (2012) 7593–7597
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Figure 5. Variation of PCV on flexible docking of cognate ligands. (IF PCV—Induced Fit generated structure PCV).
compared to holo protein structures in both rigid and flexible docking. In docking studies, scoring of higher volume ligands has to be carefully addressed, as these score better compared to smaller volume ligands. Family specific tuning of scoring function and pose prediction based on the experimental information of protein and ligand, like protein binding cavity information remains as a possible way to improve the success rate.4,39 Results from this study shed light on the selection of protein structures in docking and scoring studies. Protein conformation plays a very important role in docking and scoring of ligands. Increased success rate is possible if appropriate protein conformation is chosen in docking and scoring studies. PCV consideration is required in all rigid and flexible proteins for reliable scoring of ligands. Consideration of apo structures in docking is also essential as it docks ligands of varied volume in its binding cavity. Ensemble docking approaches apart from conformational variability in protein binding sites should consider the PCV based on the volume of the ligands screened. Choosing of conformers based on the PCV may result in good enrichment rate in retrieving active compounds. Acknowledgements We thank University Grants Commission, New Delhi for the award of RGNFS fellowship to N.S. We gratefully acknowledge Bioinformatics Infrastructure Facility, Alagappa University, Karaikudi for providing software assistance. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.bmcl.2012. 10.018. References and notes 1. Gohlke, H.; Klebe, G. Angew. Chem., Int. Ed. 2002, 41, 2644. 2. Bohm, H. J.; Klebe, G. Angew. Chem., Int. Ed. 1996, 35, 2588. 3. Klebe, G. Drug Discovery Today 2006, 11, 580.
4. Kitchen, D. B.; Decornez, H.; Furr, J. R.; Bajorath, J. Nat. Rev. Drug Disc. 2004, 3, 935. 5. Teague, S. J. Nat. Rev. Drug Disc. 2003, 2, 527. 6. Fradera, X.; Mestres, J. Curr. Top. Med. Chem. 2004, 4, 687. 7. Carlson, H. A.; McCammon, J. A. Mol. Pharmacol. 2000, 57, 213. 8. Zacharias, M. Proteins: Struct., Funct., Bioinf. 2004, 54, 759. 9. Nichols, S. E.; Baron, R.; Ivetac, A.; McCammon, J. A. J. Chem. Inf. Model. 2011, 51, 1439. 10. Armen, R. S.; Chen, J.; Brooks, C. L. J. Chem. Theory Comput. 2009, 5, 2909. 11. Ferrara, P.; Curioni, A.; Vangrevelinghe, E.; Meyer, T.; Mordasini, T.; Andreoni, W.; Acklin, P.; Jacoby, E. J. Chem. Inf. Model. 2005, 46, 254. 12. Totrov, M.; Abagyan, R. Curr. Opin. Struct. Biol. 2008, 18, 178. 13. Jain, A. N. J. Comput. Aided Mol. Des. 2009, 23, 355. 14. Craig, I. R.; Essex, J. W.; Spiegel, K. J. Chem. Inf. Model. 2010, 50, 511. 15. Osguthorpe, D. J.; Sherman, W.; Hagler, A. T. J. Phys. Chem. B 2012, 116, 6952. 16. Osguthorpe, D. J.; Sherman, W.; Hagler, A. T. J. Chem. Biol. Drug Des. 2012, 80, 182. 17. Erickson, J. A.; Jalaie, M.; Robertson, D. H.; Lewis, R. A.; Vieth, M. J. Med. Chem. 2004, 47, 45. 18. Rueda, M.; Bottegoni, G.; Abagyan, R. J. Chem. Inf. Model. 2010, 50, 186. 19. Rao, S.; Sanschagrin, P. C.; Greenwood, J. R.; Repasky, M. P.; Sherman, W.; Farid, R. J. Comput. Aided Mol. Des. 2008, 22, 621. 20. Zhou, Z.; Felts, A. K.; Friesner, R. A.; Levy, R. M. J. Chem. Inf. Model. 2007, 47, 1599. 21. Kellenberger, E.; Rodrigo, J.; Muller, P.; Rognan, D. Proteins: Struct. Funct. Bioinf. 2004, 57, 225. 22. Deng, W.; Verlinde, C. L. M. J. J. Chem. Inf. Model. 2010, 2008, 48. 23. Cole, J. C.; Murray, C. W.; Nissink, J. W. M.; Taylor, R. D.; Taylor, R. Proteins: Struct. Funct. Bioinf. 2005, 60, 325. 24. Perola, E.; Walters, W. P.; Charifson, P. S. Proteins: Struct. Funct. Bioinf. 2004, 56, 235. 25. Perot, S.; Sperandio, O.; Miteva, M. A.; Camproux, A. C.; Villoutreix, B. O. Drug Discovery Today 2010, 15, 656. 26. Ravitz, O.; Zsoldos, Z.; Simon, A. J. Comput. Aided Mol. Des. 2011, 1033, 25. 27. Englebienne, P.; Moitessier, N. J. Chem. Inf. Model. 2009, 2009, 49. 28. Corbeil, C. R.; Moitessier, N. J. Chem. Inf. Model. 2009, 49, 997. 29. Saranya, N.; Selvaraj, S. Bioorg. Med. Chem. Lett. 2009, 19, 5769. 30. Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. Nucleic Acids Res. 2000, 28, 235. 31. Hu, L.; Benson, M. L.; Smith, R. D.; Lerner, M. G.; Carlson, H. A. Proteins 2005, 60, 333. 32. Laurie, A. T.; Jackson, R. M. Bioinformatics 1908, 2005, 21. 33. www.molinspiration.com. 34. Glide, version 5.6, Schrödinger, LLC, New York, NY, 2010. 35. Sherman, W.; Day, T.; Jacobson, M. P.; Friesner, R. A.; Farid, R. J. Med. Chem. 2006, 49, 534. 36. Wang, R.; Lu, Y.; Fang, X.; Wang, S. J. Chem. Inf. Comput. Sci. 2004, 44, 2114. 37. Plewczynski, D.; Łaz´niewski, M.; Augustyniak, R.; Ginalski, K. J. Comput. Chem. 2011, 32, 742. 38. Li, X.; Li, Y.; Cheng, T.; Liu, Z.; Wang, R. J. Comput. Chem. 2010, 31, 2109. 39. Cheng, T.; Li, X.; Li, Y.; Liu, Z.; Wang, R. J. Chem. Inf. Model. 2009, 1079, 49.
Contents lists available at SciVerse ScienceDirect
Bioorganic & Medicinal Chemistry Letters journal homepage: www.elsevier.com/locate/bmcl
Impact of protein binding cavity volume (PCV) and ligand volume (LV) in rigid and flexible docking of protein–ligand complexes N. Saranya a, J. Jeyakanthan b, S. Selvaraj a,⇑ a b
Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli 620024, Tamil Nadu, India Department of Bioinformatics, Alagappa University, Karaikudi 630003, Tamil Nadu, India
a r t i c l e
i n f o
Article history: Received 29 May 2012 Revised 26 September 2012 Accepted 2 October 2012 Available online 11 October 2012 Keywords: Protein binding cavity volume Protein flexibility Structure-based drug design Docking and scoring
a b s t r a c t The importance of protein binding cavity volume (PCV) and ligand volume (LV) in rigid and flexible docking has been studied in 48 protein–ligand complexes belonging to eight protein families. In continuation of our earlier study on protein flexibility in relationship to PCV and LV, this study analyzes the importance of PCV and LV in the scoring and ranking of ligands in docking experiments. Crystal structures of protein– ligand complexes with varied PCV were chosen for docking ligands of varied volume in each protein family. Docking and scoring accuracy have been evaluated by self and cross docking of ligands to the given protein conformation. Effect of PCV and LV in rigid and flexible docking has been studied both in apo and holo proteins. Rigid docking has performed well when appropriate protein conformation is used. Selecting the proteins with appropriate PCV based on the LV information is suggested for better results in ensemble docking. Ó 2012 Elsevier Ltd. All rights reserved.
With consideration of cost and time in wet lab techniques, in silico approaches favor the fast and effective retrieval of actives from the huge number of compounds in databases for a particular target. Docking and scoring is performed to rank the compounds in appropriate binding mode and pose.1–4 Ranking of compounds accurately from the pool of compounds is of utmost importance. A number of factors such as charge, hydrogen bond interaction, hydrophobic interaction, electrostatic potential, van der Waals forces etc. are involved in the binding of a particular ligand to the protein binding cavity in terms of specificity and potency. Protein flexibility also plays a major role in the accurate scoring and ranking of ligands.5–7 In docking and scoring methods, the input protein conformation may be a holo structure (protein cocrystallized with ligand) or an apo structure (protein crystallized without ligand). In most docking software, flexibility of the ligand alone is considered and protein is kept in rigid conformation. Protein flexibility can be induced by methods such as molecular dynamics simulation methods, graph theoretical and geometry based approaches and harmonic methods.5–11 Incorporation of protein flexibility by ensemble docking and scoring using multiple protein conformations is also possible.12–15 Ensemble docking provided higher enrichments compared to individual receptor docking.14 In addition, if a set of active compounds are found, then induced fit docking was proposed to be a reliable way for construction of ensembles. The performance of
⇑ Corresponding author. E-mail address: [email protected] (S. Selvaraj). 0960-894X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.bmcl.2012.10.018
ensemble docking in virtual screening has been assessed, in which protein structures (from crystal–ligand complexes, molecular dynamics and replica exchange molecular dynamics) were selected based on the clustering of binding site shape.16 In this study of accounting protein flexibility, the authors have emphasized the importance of binding site characteristics in selecting diverse ensemble structures to obtain higher enrichment rates and diverse actives. Erickson et al.17 have performed docking experiments to examine the various factors that influence docking accuracy including ligand and protein flexibility. It has been shown that docking accuracy falls off when average or apo structures were used. Docking performed with protein conformation accommodating largest ligand has resulted in higher selectivity; further, ensembles incorporating 3–5 of these experimental conformations improved the prediction accuracy.18 Similar results of choosing 3 best conformers for ensemble docking based on mean Glide score has also been reported.19 A number of studies have compared and assessed docking and scoring programs to reproduce the experimental results in diverse protein–ligand complexes.20–24 Uncertainty prevails in many docking and scoring programs for the accurate prediction of binding affinity and pose in protein–ligand complexes. In a comparative study of eight docking tools, docking/scoring inaccuracies were discussed based on the protein binding cavity volume, ligand buried surface area and number of rotatable bonds.20 Binding pocket specific scoring functions are suggested for appropriate scoring of ligands in different protein families.25 Simon and co-workers26 have implemented rank, enrichment and affinity tuning approaches in eHITS program to improve the docking and scoring accuracy in
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different protein targets. The accuracy of eighteen scoring functions were evaluated by self and cross docking of ligands into flexible and solvated proteins of different classes.27 The impact of input ligand and protein conformation, and also the protein flexibility and water molecules on the docking accuracy have been reported using Glide, GOLD, SURFLEX, FlexX and Fitted 2.6 programs.28 A single protein binds different ligands and adopts different conformations. Ligand defined conformational change occurs in the binding site of the protein.5 Docking and scoring becomes ineffective using randomly chosen protein conformation. Due to the flexible nature of the protein, its conformation and PCV also changes on binding diverse ligands. When a ligand binds protein, induced fit causes conformation change of both protein and ligand such that the binding cavity volume changes for each ligand of the same protein. A protein crystallized with a ligand adopts a particular conformation. Volume of this cavity fits that particular ligand well. The flexibility of the protein binding cavity is different for different proteins.5 When docking is performed using the structure crystallized with the native ligand, it is likely that it docks and scores better. Thus, PCV and LV are important properties of the protein and ligand such that docking and scoring analysis based on the changes in PCV and LV in different protein families may provide insight for the accurate scoring of ligands. In our earlier study,29 we have reported the variation of PCV and ligand volume (LV) in eight protein families namely Trypsin, Factor Xa, Carbonic anhydrase (CAR), Thrombin, Acetyl choline esterase (ACE), HIV-1 protease, HSP 90 and Cyclin-dependent kinase-2 (CDK-2) given in order of increasing flexibility. This study has provided insight into the extent of variation of PCV and LV in the eight protein families considered. High correlation of atom-atom interactions and ligand volume revealed the presence of ligand defined plasticity in the protein–ligand complexes. In the present work, we have analyzed the impact of PCV and LV changes in docking and scoring of known ligands in eight protein families. Details regarding the major features and biomedical importance of the proteins examined in this study are given in Table 1. For this purpose, protein–ligand complexes observed to have different PCV and LV in each protein were selected. Ligands of different volumes were docked to the protein of different PCV or conformation. Ligands were both self and cross docked with their corresponding proteins. The influence of PCV variation in docking and scoring of cognate and non-cognate ligands in a given protein has been analyzed by performing both rigid and flexible
docking. In addition to holo protein docking, we have also docked (rigid and induced fit docking) the ligands to their corresponding apo protein structures. While a number of docking studies have been reported with regard to evaluation of accuracy of ligand pose and score prediction, the present study mainly focuses on the influence of binding cavity volume and ligand volume in the accurate scoring of ligands. Selection of protein–ligand complexes was performed based on the 200 protein–ligand complexes reported in our earlier study.29 40 protein–ligand complexes from eight protein families were used for present docking study. Five protein–ligand complexes from each protein family were selected in such a way that PCV and LV values varies largely within a given protein family. The atomic coordinates of both apo and holo structures were obtained from RCSB Protein Data Bank (PDB) (www.rcsb.org).30 Experimental binding affinity values have been obtained from the Binding MOAD31 and PDB.30 PCV and LV were computed using Pocket finder server32 and molinspiration server33 respectively. Table S1 provides details regarding the PDB ID, Ligand HETATM ID, PCV, LV, rotatable bonds of ligands and binding affinity data of protein– ligand complexes used in the present study. Glide Extra Precision (XP)34 and Induced Fit (IF) docking35 modules from Schrödinger were used for rigid and flexible docking study. In Glide XP docking, only ligand poses are generated and the receptor remains rigid. In IF docking low energy conformations of receptor are generated for a particular ligand pose. Each protein was prepared for docking using protein preparation wizard available in Schrödinger package. In this task, the proteins were checked for steric clashes, missing loop, side chains and charges were added and minimized using OPLS 2005 force field. Missing side chains were added in HIV-1 protease (1B6J, 1HPO), CDK-2 (1OIQ), HSP 90 (1UY8, 1UYG, 2BYI and 2BT0) ACE (2ACK and 2CKM) and Factor Xa (2J38) proteins using the Prime module in Schrödinger package. Ligands were prepared using LigPrep module. Receptor grid generation was used to set up grid for ligand docking site. An overview of the scheme of computational analysis is given as a flowchart (Fig. 1). In Glide XP and IF docking, top most scored ligands are ranked in particular lowest energy conformation and pose based on their interaction with protein in particular conformation. Rigid and flexible docking accuracy have been evaluated based on the scoring of cognate ligands in a given protein. Efficiency for top scoring of cognate ligands among non-cognates in rigid and flexible docking has been compared and analyzed. Consensus scoring for a given ligand
Table 1 Major features of the proteins examined in the present study Protein
Features
Trypsin
Trypsin is a pancreatic serine protease and is involved in the biological processes such as digestion of proteins, blood coagulation, complement activation, fibrinolysis, kinin formation, reproduction, and phagocytosis. This enzyme acts as a therapeutic target in the variety of disease states including pancreatic diseases, including coronary and cerebral infraction, vascular clotting, pancreatitis, arthritis, and tumor cell invasion. Thrombin is a trypsin-like serine protease involved in a multitude of processes including blood clotting, inflammation and wound healing. Thrombin is a major target for the pharmacological prevention of clot formation in coronary thrombosis. Carbonic anhydrase assists in the rapid inter-conversion of carbon dioxide and water into carbonic acid, protons and bicarbonate ions. Carbonic anhydrase inhibitors are clinically used as diuretic, antiglaucoma, anticonvulsant, and antiobesity drugs and also in the management of hypoxic tumors. CDK-2 is involved in the regulation of cell cycle proliferation and RNA polymerase II (RNAP II) transcription cycle. Deregulation of CDK leads to various disorders such as cancer, diabetes, protozoan infections, nervous disorders, etc. Acetylcholinesterase is found in the synapse between nerve cells and muscle cells and when signal is passed, it breaks down the acetylcholine into its two component parts, acetic acid and choline. This protein is a drug target for Alzheimer’s disease. HSP 90 is an ATP-dependent molecular chaperone that has several oncogenic client proteins involved in signal transduction, cell cycle regulation, and apoptosis and remains a potential anticancer drug target. HIV-1 protease, which is an essential enzyme in the life cycle of HIV for the protein maturation and viral infectivity, is one of the active targets in the treatment of HIV. Factor Xa plays a key role in the blood coagulation cascade in which it converts prothrombin to thrombin and holds a central position linking the extrinsic and intrinsic activation pathways to the final common coagulation pathway. Compared to thrombin, Factor Xa was found to be more advantageous as a molecular target for the development of anticoagulant drugs for the treatment of thromboembolic disorders.
Thrombin Carbonic anhydrase
Cyclin-Dependent kinase-2 (CDK-2) Acetylcholine esterase Heat shock protein 90 (HSP 90) Human Immunodeficiency Virus (HIV)-1 protease Factor Xa
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Figure 1. An overview of the computational analysis.
was computed by calculating average of scores obtained in each conformation of a protein. To evaluate the accuracy of prediction, negative logarithm of experimental binding affinity value was correlated with docking score using Pearson correlation method in holo proteins. Induced fit generated protein conformation was compared with experimental protein conformation to evaluate the volume variation in docked protein–ligand structures. Experimental PCV of holo and apo proteins were compared with PCV of IF generated protein structures by self and cross docking of ligands. Pocket finder server32 was used for the computation of PCV of induced fit generated protein structures. Standard deviation was computed to evaluate the deviation of PCV observed in induced fit docked structures on docking different ligands in a given protein conformation. The accuracy of docking and scoring was analyzed based on the ranking of cognate ligands among the non-cognates docked in different conformations of each protein. The rigid and flexible docking scores computed for the eight protein families are given in Table S2. The cognate ligands scored better in rigid docking compared to flexible docking. Rigid docking performed better than flexible docking in thrombin, HIV-1 protease, HSP 90 and CDK-2 proteins (Fig. 2). Equal performance of both rigid and flexible docking was observed in trypsin, CAR, thrombin and ACE proteins. Protein
Figure 2. Percentage accuracy of rigid and flexible docking and scoring of cognate ligands in eight protein families.
binding cavity flexibility (based on PCV) increases from trypsin to CDK-2. Likewise, performance to score and rank the cognate ligand also increases. In Factor Xa, none of the cognate ligands scored high in flexible docking. Out of the 5 ligands docked to different conformations of the same protein, two cognate ligands of HIV-1 protease, HSP 90 and three cognate ligands of thrombin and ACE scored top in flexible docking. In rigid docking, maximum of three cognate ligands scored correctly in Thrombin, ACE, HSP 90 and CDK-2. For HIV-1 protease a maximum of 4 cognate ligands scored correctly in rigid docking. Approximately, 50% and 30% of the cognate ligands best scored in rigid and flexible docking respectively. However, it needs to be pointed out that the objective and dataset of the present study is not to assess the performance of GLIDE XP or IF, but rather to examine the influence of PCV and LV in docking and scoring. Protein binding cavity in bound conformation with a ligand of particular volume scores the same ligand as best only when appropriate input protein conformation is provided for docking. Same protein in different conformation scores ligands variedly. This necessitates the consideration of PCV in docking studies with respect to LV. If appropriate experimental protein conformation is provided, better results could be obtained by rigid docking than flexible docking. The results thus imply the importance of input protein conformation with appropriate PCV in scoring the given ligand in rigid docking. The accuracy of prediction of experimental binding affinity in docking experiments has been evaluated. Pearson correlation was computed to find statistical relationship between docking score and experimental binding affinity value. High correlation was observed for ACE compared to other proteins. In an extensive study to test fourteen scoring functions, trypsin and carbonic anhydrase docking scores had better correlation with experimental binding affinity.36 A negative correlation was observed for holo and apo proteins of trypsin, Factor Xa and carbonic anhydrase in the present study. Similar results of low correlation have been reported between docking score and experimental binding affinity data in comparative docking studies.37–39 Consensus scoring of ligands in all the conformations for a given protein has been correlated with the experimental binding affinity. Figure 3 provides details regarding the correlation of rigid and flexible consensus docking score and experimental binding affinity in the eight protein families. Consensus scoring has yielded better results compared to scoring using individual conformation. Rigid and flexible docking showed
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Figure 3. Correlation of rigid and flexible consensus docking score and experimental binding affinity in eight protein families.
positive correlation for Thrombin (r values 0.53 and 0.46), ACE (0.54 and 0.42) and HIV-1 Protease (0.04 and 0.54) proteins respectively. Correlation between PCV and LV was observed for the 5 protein–ligand complexes from each of the 8 protein families (Fig. 4). A higher correlation value describes the increase or decrease in PCV in correspondence with change in LV. ACE had a correlation of 0.94, ranking top among all other protein families. Trypsin (0.75) had the second highest correlation values followed by the CDK-2 (0.34), CAR (0.27) with positive correlation. Positive correlation represents the increase in PCV as the LV increases. Low correlation was observed in HIV-1 Protease, Thrombin, Factor Xa and HSP 90 proteins. Flexible docking of ligands (cognate and non-cognate) into protein of particular PCV results in structures with different PCV. Tables S3 and S4 provides the details regarding the PCV of induced fit structures for apo and holo proteins. Analysis has been made on the variation of PCV on docking cognate ligands to an experimental conformation. In 1DIF conformation of HIV-1 protease, exactly the same PCV of 700 Å3 was reproduced on docking cognate ligand A85 which has been second best scored (Fig. 5). Standard deviation was observed to be higher of 267 Å3 for the ligand F11 of ACE which has been top scored. Though discrepancies prevail in the analysis of PCV and scoring, approximately 85% of the proteins have PCV standard deviation within 100 Å3 in flexible docking. Flexible proteins have scored cognate ligands accurately compared to rigid proteins. Accuracy of scoring cognate ligands was less observed in rigid proteins like Trypsin, Factor Xa and CAR compared to other flexible proteins (Table S5).
Figure 4. Correlation (r) of PCV versus LV.
Variation of PCV in IF generated structures on docking different ligands to a protein of particular PCV has also been analyzed. For each protein family, standard deviations can be given in the order of Trypsin (20.34 Å3), Factor Xa (39.55 Å3), HSP 90 (43.42 Å3), CAR (44.67 Å3), HIV-1 protease (56.99 Å3), thrombin (88.82 Å3), ACE (90.23 Å3) and CDK-2 (119.37 Å3) for holo proteins. Flexible docking of apo proteins reported the standard deviation of Trypsin (33.98 Å3), Factor Xa (43.24 Å3), CAR (28.80 Å3), Thrombin (97.24 Å3), ACE (86.96 Å3), HIV-1 Protease (57.53 Å3), HSP 90 (49.58 Å3) and CDK-2 (72.73 Å3). Variation of PCV was observed to be low for less flexible proteins like Trypsin, Factor Xa and high for more flexible proteins like CDK-2 and ACE in holo protein flexible docking.29 This trend was not observed in apo proteins where higher variation of PCV was reported in Thrombin and ACE compared to HIV-1 Protease, HSP 90 and CDK-2 proteins. Preference for high scoring of non-cognate ligands which is of nearest volume (greater or smaller) to cognate ligand in rigid and flexible docking was analyzed. Over all analysis of best scoring of nearer volume ligands reported a percentage of 55% and 60% in rigid and flexible docking respectively. In rigid and flexible docking, ligands with higher volume scored well compared to ligands with lesser volume. Irrespective of PCV, higher volume ligands scored better. Approximately, 55% and 65% of the higher volume ligands were scored best in rigid and flexible docking respectively. A number of studies have reported the controversy of scoring larger ligands better as this parameter adds weight to the scoring function.16,36 In most protein families, docking of non-cognate ligands to the same protein but in different conformation remains challenging. In each protein, apo protein docking was able to dock all the ligands in the given conformation compared to holo protein docking in both rigid and flexible docking. Table S6 provides details of the ligands that were not docking to protein of given conformation. As with experimental structures of same PCV bound with different ligands,29 flexible docking has also generated same cavity volume structures with different scores. Ligands 120, BAZ docked to 1AQ7 conformation of HIV-1 protease and UNI, PI1 docked to 1B6J conformation of trypsin have different scores with same PCV of 139 Å3 and 739 Å3 respectively. In summary, the effect of choosing protein conformation in docking different ligands using rigid and induced fit docking has been assessed in eight protein families. Accuracy of scoring cognate ligands was observed to be better for rigid docking compared to flexible docking in holo proteins. Consensus scoring was able to reproduce experimental binding affinity in few proteins which showed positive correlation. Selection of protein input structures based on LV information in the docking experiments may yield better results. In a docking experiment, volume information of ligands to be docked should be matched with the ligands in crystal structures for the selection of protein input conformation. The availability of large number of crystal structures of protein–ligand complexes fulfills this requirement of input conformation selection. Though, it is not possible to find an experimental protein conformation for a given LV (test set to be docked), the closely related protein conformation crystallized with identical or similar volume ligands will remain a suitable input. As ligands with nearest volume to cognate ligands was observed to have high score, selection of ensemble protein structures bound with identical or similar volume ligands will serve the need of incorporating protein flexibility in finding actives when a set of compounds with similar volume and characteristics are available for screening. As some ligands of the same protein were not even docked in different protein conformations, this type of selection of protein ensembles will reduce the problem of false positives and negatives in the screening process. Apo protein structures were able to dock ligands of varied volume in the binding site
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Figure 5. Variation of PCV on flexible docking of cognate ligands. (IF PCV—Induced Fit generated structure PCV).
compared to holo protein structures in both rigid and flexible docking. In docking studies, scoring of higher volume ligands has to be carefully addressed, as these score better compared to smaller volume ligands. Family specific tuning of scoring function and pose prediction based on the experimental information of protein and ligand, like protein binding cavity information remains as a possible way to improve the success rate.4,39 Results from this study shed light on the selection of protein structures in docking and scoring studies. Protein conformation plays a very important role in docking and scoring of ligands. Increased success rate is possible if appropriate protein conformation is chosen in docking and scoring studies. PCV consideration is required in all rigid and flexible proteins for reliable scoring of ligands. Consideration of apo structures in docking is also essential as it docks ligands of varied volume in its binding cavity. Ensemble docking approaches apart from conformational variability in protein binding sites should consider the PCV based on the volume of the ligands screened. Choosing of conformers based on the PCV may result in good enrichment rate in retrieving active compounds. Acknowledgements We thank University Grants Commission, New Delhi for the award of RGNFS fellowship to N.S. We gratefully acknowledge Bioinformatics Infrastructure Facility, Alagappa University, Karaikudi for providing software assistance. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.bmcl.2012. 10.018. References and notes 1. Gohlke, H.; Klebe, G. Angew. Chem., Int. Ed. 2002, 41, 2644. 2. Bohm, H. J.; Klebe, G. Angew. Chem., Int. Ed. 1996, 35, 2588. 3. Klebe, G. Drug Discovery Today 2006, 11, 580.
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