Publikationsserver der Universitätsbibliothek Marburg

Titel:Methods for the Efficient Comparison of Protein Binding Sites and for the Assessment of Protein-Ligand Complexes
Autor:Krotzky, Timo
Weitere Beteiligte: Klebe, Gerhard (Prof. Dr.)
Veröffentlicht:2015
URI:https://archiv.ub.uni-marburg.de/diss/z2015/0384
DOI: https://doi.org/10.17192/z2015.0384
URN: urn:nbn:de:hebis:04-z2015-03848
DDC: Medizin
Titel (trans.):Methoden für den effizienten Vergleich von Proteinbindetaschen und für die Bewertung von Protein-Ligand Komplexen
Publikationsdatum:2015-09-10
Lizenz:https://rightsstatements.org/vocab/InC-NC/1.0/

Dokument

Schlagwörter:
Graph, Cavbase, Bindestelle, Classification, Proteine, Cavbase, Klassifizierung, Histogramm, Wirkstoff, Clique

Summary:
In the present work, accelerated methods for the comparison of protein binding sites as well as an extended procedure for the assessment of ligand poses in protein binding sites are presented. Protein binding site comparisons are frequently used receptor-based techniques in early stages of the drug development process. Binding sites of other proteins which are similar to the binding site of the target protein can offer hints for possible side effects of a new drug prior to clinical studies. Moreover, binding site comparisons are used as an idea generator for bioisosteric replacements of individual functional groups of the newly developed drug and to unravel the function of hitherto orphan proteins. The structural comparison of binding sites is especially useful when applied on distantly related proteins as a comparison solely based on the amino acid sequence is not sufficient in such cases. Methods for the assessment of ligand poses in protein binding sites are also used in the early phase of drug development within docking programs. These programs are utilized to screen entire libraries of molecules for a possible ligand of a binding site and to furthermore estimate in which conformation the ligand will most likely bind. By employing this information, molecule libraries can be filtered for subsequent affinity assays and molecular structures can be refined with regard to affinity and selectivity.

Bibliographie / References

  1. X. Pennec and N. Ayache. A Geometric Algorithm to Find Small but Highly Similar 3D Substructures in Proteins. Bioinformatics, 14:516–522, 1998.
  2. J. A. Barker and J. M. Thornton. An algorithm for constraint-based structural tem- plate matching: application to 3D templates with statistical analysis. Bioinformatics, 19(13):1644–1649, 2003. [9] H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N.
  3. G. Neudert and G. Klebe. fconv: Format Conversion, Manipulation and Feature Computation of Molecular Data. Bioinformatics, 27:1021–1022, 2011.
  4. Linus Pauling. The nature of the chemical bond and the structure of molecules and crystals. Cornell University Press, Ithaca, NY, USA, 1938.
  5. M. Moll and L.E. Kavraki. Matching of structural motifs using hashing on residue labels and geometric filtering for protein function prediction. In CSB'08: 7th Conference on Computational Systems Bioinformatics. Proceedings, pages 157–169, Palo Alto, USA, August 2008.
  6. E. Kellenberger, C. Schalon, and D. Rognan. How to Measure the Similarity Between Protein Ligand-binding Sites. Current Computer-Aided Drug Design, 4:209, 2008.
  7. Thomas Fober, Marco Mernberger, Gerhard Klebe, and Eyke Hüllermeier. Efficient Similarity Retrieval for Protein Binding Sites based on Histogram Comparison. In German Conference on Bioinformatics, pages 51–60, Braunschweig, Germany, 2010.
  8. K. P. Peters, J. Fauck, and C. Frömmel. The Automatic Search for Ligand Binding Sites in Proteins of Known Three-dimensional Structure Using only Geometric Criteria. Journal of Molecular Biology, 256:201–213, 1996.
  9. H. F. Velec, H. Gohlke, and G. Klebe. DrugScore(CSD)-knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. Journal of Medicinal Chemistry, 48(20):6296–6303, 2005.
  10. J. An, M. Totrov, and R. Abagyan. Pocketome via Comprehensive Identification and Classification of Ligand Binding Envelopes. Molecular & Cellular Proteomics, 4: 752–761, 2005.
  11. T. Krotzky. Analyse und algorithmische Erweiterung von Methoden zum strukturellen Vergleich von Proteinbindestellen. Grin Verlag GmbH, München, Germany, 2010. ISBN 3640946200.
  12. R. Osada, T. Funkhouser, B. Chazelle, and D. Dobkin. Shape Distributions. ACM Transactions on Graphics, 21(4):807–832, 2002.
  13. G. R. Desiraju. A Bond by Any Other Name. Angewandte Chemie International Edition, 50(1):52–59, 2010. doi: 10.1002/anie.201002960.
  14. C. Schalon, J. S. Surgand, E. Kellenberger, and D. Rognan. A simple and fuzzy method to align and compare druggable ligand-binding sites. Proteins, 71(4):1755–78, 2008. doi: 10.1002/prot.21858.
  15. Mark McGann. FRED Pose Prediction and Virtual Screening Accuracy. Journal of Chemical Information and Modeling, 51(3):578–596, 2011. doi: 10.1021/ci100436p.
  16. M. Zheng, B. Xiong, C. Luo, S. Li, X. Liu, Q. Shen, J. Li, W. Zhu, X. Luo, and H. Jiang. Knowledge-based scoring functions in drug design: 3. A two-dimensional knowledge-based hydrogen-bonding potential for the prediction of protein-ligand interactions. Journal of Chemical Information and Modeling, 51(11):2994–3004, Nov 2011. doi: 10.1021/ci2003939.
  17. T. Cheng, X. Li, Y. Li, Z. Liu, and R. Wang. Comparative assessment of scoring functions on a diverse test set. Journal of Chemical Information and Modeling, 49(4): 1079–1093, 2009. doi: 10.1021/ci9000053.
  18. N. Weill and D. Rognan. Alignment-free ultra-high-throughput comparison of drug- gable protein-ligand binding sites. Journal of Chemical Information and Modeling, 50 (1):123–35, 2010. doi: 10.1021/ci900349y.
  19. Y. Lu, T. Shi, Y. Wang, H. Yang, X. Yan, X. Luo, H. Jiang, and W. Zhu. Halogen Bonding -A Novel Interaction for Rational Drug Design? Journal of Medicinal Chemistry, 52(9):2854–2862, 2009. doi: 10.1021/jm9000133.
  20. Alan R. Fersht, Jian-Ping Shi, Jack Knill-Jones, Denise M. Lowe, Anthony J. Wilkin- son, David M. Blow, Peter Brick, Paul Carter, Mary M. Y. Waye, and Greg Winter. Hydrogen bonding and biological specificity analysed by protein engineering. Nature, 314:235–238, 1985. doi: 10.1038/314235a0.
  21. Robin Taylor. Which intermolecular interactions have a significant influence on crystal packing? CrystEngComm, 16(30):6852–6865, 2014. doi: 10.1039/C4CE00452C.
  22. Guoli Wang and R. L. Dunbrack. PISCES: a protein sequence culling server. Bioin- formatics, 19(12):1589–1591, 2003. doi: 10.1093/bioinformatics/btg224. BIBLIOGRAPHY
  23. J. Meslamani, D. Rognan, and E. Kellenberger. sc-PDB: a database for identifying variations and multiplicity of 'druggable' binding sites in proteins. Bioinformatics, 27 (9):1324–6, 2011. doi: 10.1093/bioinformatics/btr120.
  24. Irina Kufareva, Andrey V. Ilatovskiy, and Ruben Abagyan. Pocketome: an encyclope- dia of small-molecule binding sites in 4D. Nucleic acids research, 40(Database issue): D535–40, 2012. doi: 10.1093/nar/gkr825.
  25. N. D. Rawlings, A. J. Barrett, and A. Bateman. MEROPS: the database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Research, 40:D343–50, 2012. doi: 10.1093/nar/gkr987.
  26. I. J. Bruno, J. C. Cole, P. R. Edgington, M. Kessler, C. F. Macrae, P. McCabe, J. Pearson, and R. Taylor. New software for searching the Cambridge Structural Database and visualising crystal structures. Acta Crystallographica Section B, 58: 389–397, 2002. doi: 10.1107/S0108768102003324.
  27. N. Weskamp, E. Hüllermeier, D. Kuhn, and G. Klebe. Multiple Graph Alignment for the Structural Analysis of Protein Active Sites. IEEE Transactions on Computational Biology and Bioinformatics, 4(2):310–320, 2007. ISSN 1545-5963. doi: 10.1109/TCBB. 2007.358301.
  28. T. Krotzky, T. Fober, E. Hüllermeier, and G. Klebe. Extended Graph-based Models for Enhanced Similarity Search in Cavbase. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(5):878–890, 2014. doi: 10.1109/TCBB.2014.2325020.
  29. S. Kullback and R. A. Leibler. On Information and Sufficiency. Annals of Mathematical Statistics, 22(1):79–86, 1951. doi: 10.1214/aoms/1177729694.
  30. N. Smirnov. Table for estimating the goodness of fit of empirical distributions. Annals of Mathematical Statistics, 19:279–281, 1948. doi: 10.1214/aoms/1177730256.
  31. Clary, R. H. Crabtree, J. J. Dannenberg, P. Hobza, H. G. Kjaergaard, A. C. Legon, B. Mennucci, and D. J. Nesbitt. Definition of the Hydrogen Bond. Pure and Applied Chemistry, 83(8):1637–1641, 2011. doi: 10.1351/PAC-REC-10-01-02.
  32. A. Vulpetti, T. Kalliokoski, and F. Milletti. Chemogenomics in Drug Discovery: Computational Methods Based on the Comparison of Binding Sites. Future Medicinal Chemistry, 4(15):1971–1979, 2012. doi: 10.4155/fmc.12.147.
  33. Pedro J. Ballester and W. Graham Richards. Ultrafast shape recognition to search compound databases for similar molecular shapes. Journal of Computational Chem- istry, 28(10):1711–1723, 2007. ISSN 1096-987X. doi: 10.1002/jcc.20681. URL http://dx.doi.org/10.1002/jcc.20681. BIBLIOGRAPHY [8]
  34. Nils Weskamp, Eyke Hüllermeier, and Gerhard Klebe. Merging chemical and biological space: Structural mapping of enzyme binding pocket space. Proteins: Structure, Function, and Bioinformatics, 76(2):317–330, 2009. ISSN 1097-0134. doi: 10.1002/ prot.22345. URL http://dx.doi.org/10.1002/prot.22345.
  35. Britta Nisius, Fan Sha, and Holger Gohlke. Structure-based computational analysis of protein binding sites for function and druggability prediction. Journal of Biotechnology, 159(3):123–134, 2012. doi: http://dx.doi.org/10.1016/j.jbiotec.2011.12.005. URL http://www.sciencedirect.com/science/article/pii/S0168165611006614.
  36. M. Wagener and J. P. Lommerse. The Quest for Bioisosteric Replacements. Journal of Chemical Information and Modeling, 46:677–685, 2006.
  37. S. Glinca and G. Klebe. Cavities Tell More than Sequences: Exploring Functional Relationships of Proteases via Binding Pockets. Journal of Chemical Information and Modeling, 53(8):2082–92, 2013. doi: http://dx.doi.org/10.1021/ci300550a.
  38. C. Bissantz, B. Kuhn, and M. Stahl. A Medicinal Chemist's Guide to Molecular Interactions. Journal of Medicinal Chemistry, 53(14):5061–5084, 2010. doi: 10.1021/ jm100112j. URL http://dx.doi.org/10.1021/jm100112j.
  39. R. J. Gillespie and R. S. Nyholm. Inorganic stereochemistry. Q. Rev. Chem. Soc., 11:339–380, 1957. doi: 10.1039/QR9571100339. URL http://dx.doi.org/10.1039/ QR9571100339.
  40. Andreas Klamt, Jens Reinisch, Frank Eckert, Arnim Hellweg, and Michael Diedenhofen. Polarization charge densities provide a predictive quantification of hydrogen bond energies. Physical Chemistry Chemical Physics, 14:955–963, 2012. doi: 10.1039/ C1CP22640A. URL http://dx.doi.org/10.1039/C1CP22640A.
  41. Mikael P. Johansson and Marcel Swart. Intramolecular halogen-halogen bonds? Phys- ical Chemistry Chemical Physics, 15:11543–11553, 2013. doi: 10.1039/C3CP50962A. URL http://dx.doi.org/10.1039/C3CP50962A.
  42. T. Fober, M. Mernberger, G. Klebe, and E. Hüllermeier. Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. Oxford Bioinformatics, 25(16):2110–2117, August 2009.
  43. M. Rosen, S. L. Lin, H. Wolfson, and R. Nussinov. Molecular shape comparisons in searches for active sites and functional similarity. Protein Engineering, 11(4):263–77, 1998.
  44. O. Bachar, D. Fischer, R. Nussinov, and H. Wolfson. A Computer Vision Based Technique for 3-D Sequence-independent Structural Comparison of Proteins. Protein Engineering, 6:279–287, 1993. [7]
  45. Frank H. Allen. The Cambridge Structural Database: a quarter of a million crys- tal structures and rising. Acta Crystallographica Section B, 58(3 Part 1):380–388, Jun 2002. doi: 10.1107/S0108768102003890. URL http://dx.doi.org/10.1107/ S0108768102003890. [2] S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. Basic local alignment search tool. Journal of Molecular Biology, 215(3):403–10, 1990. [3] R. C. Amorim and B. Mirkin. Minkowski Metric, Feature Weighting and Anomalous Cluster Initialisation in K-Means Clustering. Pattern Recognition, 45:1061–1075, 2012. [4]
  46. S. Dambhare, S. A. Soman, and M. C. Chandorkar. Current Differential Protection of Transmission Line Using the Moving Window Averaging Technique. IEEE Transactions on Power Delivery, 25(2):610–620, 2010.
  47. O. C. Redfern, A. Harrison, T. Dallman, F. M. Pearl, and C. A. Orengo. CATHEDRAL: a fast and effective algorithm to predict folds and domain boundaries from multidomain protein structures. PLoS Computational Biology, 3(11):2334–2347, 2007.
  48. Sven Siggelkow and Hans Burkhardt. Improvement of Histogram-Based Image Retrieval and Classification. In Proceedings of the 16 th International Confer- ence on Pattern Recognition, volume 3 of ICPR '02, page 30367, Washington, DC, USA, 2002. IEEE Computer Society. ISBN 0-7695-1695-X. URL http: //dl.acm.org/citation.cfm?id=839291.842844.
  49. I. Boukhris, Z. Elouedi, T. Fober, M. Mernberger, and E. Hüllermeier. Similarity analysis of protein binding sites: a generalization of the maximum common subgraph measure based on quasi-clique detection. In ISDA'09: International Conference on Intelligent Systems Design and Applications. Proceedings, pages 1245–1250, Pisa, Italy, November 2009.
  50. T. Fober and E. Hüllermeier. Similarity Measures for Protein Structures based on Fuzzy Histogram Comparison. In World Congress on Computational Intelligence, pages 2808–2814, Barcelona, Spain, 2010.
  51. T. Fober, S. Glinca, G. Klebe, and E. Hüllermeier. Superposition and Alignment of Labeled Point Clouds. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6):1653–1666, November/December 2011.
  52. A.G. Murzin, S.E. Brenner, T. Hubbard, and C. Chothia. SCOP: a structural classification of proteins database for the investigation of sequences and structures. Journal of Molecular Biology, 247(4):536–540, 1995.
  53. J. C. Whisstock and A. M. Lesk. Prediction of protein function from protein sequence and structure. Quarterly Reviews of Biophysics, 36(3):307–340, 2004.
  54. J. E. Mills and P. M. Dean. Three-dimensional hydrogen-bond geometry and probabil- ity information from a crystal survey. Journal of Computer-Aided Molecular Design, 10(6):607–622, 1996.
  55. E. A. Kennewell, P. Willett, P. Ducrot, and C. Luttmann. Identification of Target- specific Bioisosteric Fragments from Ligand-protein Crystallographic Data. Journal of Computer-Aided Molecular Design, 20:385–394, 2006.
  56. D. Ghersi and R. Sanchez. Beyond structural genomics: computational approaches for the identification of ligand binding sites in protein structures. Journal of Structural and Functional Genomics, 12(2):109–17, 2011.
  57. Verdonk. IsoStar: a library of information about nonbonded interactions. Journal of Computer-Aided Molecular Design, 11(6):525–537, 1997.
  58. G. P. Brady and P. F. W. Stouten. Fast Prediction and Visualization of Protein Binding Pockets with PASS. Journal of Computer-Aided Molecular Design, 14:383–401, 2000.
  59. T. J. Ewing, S. Makino, A. G. Skillman, and I. D. Kuntz. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. Journal of Computer-Aided Molecular Design, 15(5):411–428, 2001.
  60. G. Liu and L. Wong. Effective pruning techniques for mining quasi-cliques. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, part II, pages 33–49, Antwerp, Belgium, 2008.
  61. M. M. Deza and E. Deza. Encyclopedia of Distances. Springer, Heidelberg, Germany, 2009.
  62. O.3oh_N.2n (3512), O.am_O.ph (3472), O.ph_O.am (3472), O.2es_N.ams (3460), O.co2_N.mih (3459), N.ams_O.2es (3456), N.mih_O.co2 (3449), N.2n_O.3oh (3422), N.4h_O.ph (3348), N.amp_O.3oh (3161), O.3oh_N.amp (3161), O.3oh_S.3 (3117), O.n_O.h2o (3103), N.aap_O.co2 (3086), O.co2_N.aap (3086), O.3eta_O.3oh (2987), O.3oh_O.3eta (2987), S.3_O.3oh (2927), O.noh_O.carb (2832), O.3oh_N.guh (2800), N.guh_O.3oh (2797), N.ams_S.3 (2755), S.3_N.ams (2749), O.3oh_O.r3 (2676), N.4h_N.ar2 (2671), O.r3_O.3oh (2633), N.ar3h_O.3et (2619), N.guh_O.h2o (2593), N.ar3h_P.3 (2585), O.3et_N.ar3h (2578), O.3oh_O.n (2514), O.n_O.3oh (2514), O.carb_N.ar3h (2507), O.3oh_N.mih (2481), N.mih_O.3oh (2480), O.3oh_N.1 (2440), O.o_O.h2o (2360), N.3s_O.co2 (2347), O.co2_N.3s (2347), O.co2_O.noh (2315), O.noh_O.co2 (2315), N.amp_S.3 (2312),
  63. W. Kabsch. A solution of the best rotation to relate two sets of vectors. Acta Crystallographica, 32:922–923, 1976. BIBLIOGRAPHY [70] A. Kahraman, R. J. Morris, R. A. Laskowski, and J. M. Thornton. Shape variation in protein binding pockets and their ligands. Journal of Molecular Biology, 368(1): 283–301, 2007.
  64. C. Bron and J. Kerbosch. Finding all cliques of an undirected graph. Communications of the ACM, 16(9):575–577, 1973.
  65. S. R. Eddy. Hidden Markov models. Current Opinion in Structural Biology, 6(3): 361–365, 1996.
  66. Lloyd, S. Arbilla, B. Zivkovic, and E. T. MacKenzie. Ifenprodil and SL 82.0715 as cerebral anti-ischemic agents. II. Evidence for N-methyl-D-aspartate receptor antagonist properties. Journal of Pharmacology and Experimental Therapeutics, 247 (3):1222–1232, 1988.
  67. M. J. Sippl. Knowledge-based potentials for proteins. Current Opinion in Structural Biology, 5(2):229–235, 1995.
  68. P. Labute and M. Santavy. Locating Binding Sites in Protein Structures. 2007. URL https://www.chemcomp.com/journal/sitefind.htm.
  69. G. N. Lance and W. T. Williams. Mixed-data classificatory programs, I.) Agglomera- tive Systems. Australian Computer Journal, 1:15–20, 1967.
  70. G. Schneider and K. H. Baringhaus. Molecular Design. Wiley-Vch, 2008.
  71. 8121), N.guh_O.co2 (8116), N.4h_O.2p (8049), S.3_N.4h (7804), O.carb_O.h2o (7482), O.2p_O.h2o (7126), N.oh_O.noh (7076), N.4h_S.3 (6898), N.4h_N.1 (6796), O.n_N.4h (6752), N.2n_N.4h (6686), O.2s_N.guh (6147), N.guh_O.2s (6146), O.co2_N.ams (6096), N.ams_O.co2 (6079), O.3oh_N.ams (5796), N.ams_O.3oh (5786), N.4h_O.am (5616), N.ar3h_O.carb (5528), O.ph_N.4h (5221), O.am_N.ims (5123), N.ims_O.am (5107), N.4h_N.2n (5031), N.4h_O.n (4895), O.2es_O.3oh (4638), O.3oh_O.2es (4638), O.co2_N.amp (4614), N.amp_O.co2 (4612), N.3p_N.4h (4497), N.ar3h_O.h2o (4374), O.r3_N.4h (4229), N.ar2_N.4h (4111), N.ams_O.carb (4098), N.ams_O.h2o (3900), N.4h_N.3p (3825), N.4h_P.3 (3797), O.ph_O.3et (3763), O.3et_O.ph (3710), O.carb_O.ph (3655), P.3_N.4h (3654), N.4h_O.r3 (3591), N.ar3h_O.3oh (3561), O.3oh_N.ar3h (3561),
  72. S.3_N.amp (2308), N.oh_N.4h (2283), O.ph_N.2n (2247), O.o_N.4h (2214), O.am_N.mih (2201), N.mih_O.am (2198), O.ph_P.3 (2173), N.2n_O.ph (2168), O.3oh_O.o (2143), O.3oh_O.2po (2121), O.2po_O.3oh (2114), O.ph_N.ar2 (2098), N.ar2_O.ph (2096), N.guh_O.carb (2065), O.n_N.guh (2026), N.guh_O.n (2025), S.3_O.h2o (2015), O.am_N.guh (2011), N.guh_O.am (2008) Bibliography [1]
  73. C. L. Perrin and J. B. Nielson. " Strong " hydrogen bonds in chemistry and biology. Annual Review of Physical Chemistry, 48:511–544, 1997.
  74. A. Kolmogorov. Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari, 4:83–91, 1933.
  75. P. Jaccard. Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles, 37:547–579, 1901.
  76. L. Holm and C. Sander. The FSSP database: fold classification based on structure- structure alignment of proteins. Nucleic Acids Research, 24(1):206–209, 1996.
  77. L. Holm and C. Sander. Dali/FSSP classification of three-dimensional protein folds. Nucleic Acids Research, 25(1):231–234, 1997.
  78. Shindyalov, and P. E. Bourne. The protein data bank. Nucleic Acids Research, 28(1): 235–242, 2000.
  79. A. Stark and R. B. Russell. Annotation in three dimensions. PINTS: patterns in non-homologous tertiary structures. Nucleic Acids Research, 31(13):3341–3344, 2003.
  80. Y. Chen, T. Kortemme, T. Robertson, D. Baker, and G. Varani. A new hydrogen- bonding potential for the design of protein-RNA interactions predicts specific contacts and discriminates decoys. Nucleic Acids Research, 32(17):5147–5162, 2004.
  81. C. Guda, S. Lu, E. D. Scheeff, P. E. Bourne, and I. N. Shindyalov. CE-MC: a multiple protein structure alignment server. Nucleic Acids Research, 32(Web Server Issue): W100–W103, 2004.
  82. Y. Ye and A. Godzik. FATCAT: a web server for flexible structure comparison and structure similarity searching. Nucleic Acids Research, 32:582–5, 2004.
  83. S. Schmitt, M. Hendlich, and G. Klebe. From structure to function: a new approach to detect functional similarity among proteins independent from sequence and fold homology. Angewandte Chemie International Edition, 40(17):3141–3146, 2001.
  84. Holger Gohlke and Gerhard Klebe. Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angewandte Chemie International Edition Engl., 41(15):2644–2676, 2002.
  85. D. Kuhn, N. Weskamp, E. Hüllermeier, and G. Klebe. Functional classification of protein kinase binding sites using Cavbase. Journal of Medical Chemistry, 2(10): 1432–47, 2007.
  86. H. Choi, H. Kang, and H. Park. New angle-dependent potential energy function for backbone-backbone hydrogen bond in protein-protein interactions. Journal of Computational Chemistry, 31(5):897–903, 2010.
  87. D. Plewczynski, M. Łaźniewski, R. Augustyniak, and K. Ginalski. Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database. Journal of Computational Chemistry, 32(4):742–755, 2011.
  88. X. Jalencas and J. Mestres. Identification of Similar Binding Sites to Detect Distant Polypharmacology. Molecular Informatics, 32:976–990, 2013.
  89. S. Sarkhel and G. R. Desiraju. N-H...O, O-H...O, and C-H...O hydrogen bonds in protein-ligand complexes: strong and weak interactions in molecular recognition. Proteins, 54(2):247–259, 2004.
  90. A. Brakoulias and R. M. Jackson. Towards a Structural Classification of Phos- phate Binding Sites in Protein-nucleotide Complexes: An Automated All-Against-All Structural Comparison Using Geometric Matching. Proteins, 56:250–260, 2004.
  91. Chikhi, L. Sael, and D. Kihara. Real-time Ligand Binding Pocket Database Search Using Local Surface Descriptors. Proteins, 78:2007–2028, 2010.
  92. B. Stegemann and G. Klebe. Cofactor-binding sites in proteins of deviating sequence: Comparative analysis and clustering in torsion angle, cavity, and fold space. Proteins, 80(2):626–648, 2011.
  93. L. Sael and D. J. Kihara. Detecting local ligand-binding site similarity in nonhomolo- gous proteins by surface patch comparison. Proteins, 80(4):1177–1195, 2012.
  94. D. S. Goodsell and A. J. Olson. Automated docking of substrates to proteins by simulated annealing. Proteins, 8(3):195–202, 1990.
  95. Ö. D. Ekici, M. Paetzel, and R. E. Dalbey. Unconventional serine proteases: Variations on the catalytic Ser/His/Asp triad configuration. Protein Science, 17:2023–2037, 2008.
  96. K. Kinoshita and H. Nakamura. Identification of protein biochemical functions by similarity search using the molecular surface database eF-site. Protein Science, 12(8): 1589–1595, 2003.
  97. O. Dym and D. Eisenberg. Sequence-structure analysis of FAD-containing proteins. Protein Science, 10(9):1712–1728, September 2001.
  98. L. Mony, J. N. Kew, M. J. Gunthorpe, and P. Paoletti. Allosteric modulators of NR2B-containing NMDA receptors: molecular mechanisms and therapeutic potential. British Journal of Pharmacology, 157(8):1301–1317, 2009.
  99. B. Rost. Twilight zone of protein sequence alignments. Protein Engineering, 12(2): 85–94, 1999.
  100. R. Wang and S. Wang. How does consensus scoring work for virtual library screening? An idealized computer experiment. Journal of Chemical Information and Computer Sciences, 41(5):1422–6, 2001.
  101. Taylor, and P. Watson. Virtual screening using protein-ligand docking: avoiding artificial enrichment. Journal of Chemical Information and Computer Sciences, 44(3): 793–806, 2004.
  102. P. Schmidtke, C. Souaille, F. Estienne, N. Baurin, and R. T. Kroemer. Large-scale comparison of four binding site detection algorithms. Journal of Chemical Information and Modeling, 50(12):2191–200, 2010.
  103. H. J. Feldman and P. Labute. Pocket Similarity: Are α Carbons Enough? Journal of Chemical Information and Modeling, 50:1466–1475, 2010.
  104. A. Volkamer, A. Griewel, T. Grombacher, and M. Rarey. Analyzing the topology of active sites: on the prediction of pockets and subpockets. Journal of Chemical Information and Modeling, 50:2041–2052, 2010.
  105. I. R. Craig, C. Pfleger, H. Gohlke, J. W. Essex, and K. Spiegel. Pocket-Space Maps To Identify Novel Binding-Site Conformations in Proteins. Journal of Chemical Information and Modeling, 51(10):2666–2679, 2011. BIBLIOGRAPHY [28] Ido Dagan, Lillian Lee, and Fernando Pereira. Similarity-Based Methods For Word Sense Disambiguation. In Proceedings of the Thirty-Fifth Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pages 56–63, 1997.
  106. G. Neudert and G. Klebe. DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes. Journal of Chemical Information and Modeling, 51(10): 2731–2745, 2011.
  107. A. Volkamer, D. Kuhn, T. Grombacher, F. Rippmann, and M. Rarey. Combining Global and Local Measures for Structure-Based Druggability Predictions. Journal of Chemical Information and Modeling, 52:360–372, 2012.
  108. D. J. Wood, J. de Vlieg, M. Wagener, and T. Ritschel. Pharmacophore Fingerprint- Based Approach to Binding Site Subpocket Similarity and its Application to Bioisostere Replacement. Journal of Chemical Information and Modeling, 52:2031–2043, 2012.
  109. J. Desaphy, K. Azdimousa, E. Kellenberger, and D. Rognan. Comparison and druggability prediction of protein-ligand binding sites from pharmacophore-annotated cavity shapes. Journal of Chemical Information and Modeling, 52(8):2287–2299, 2012.
  110. M. M. von Behren, A. Volkamer, A. M. Henzler, K. T. Schomburg, S. Urbaczek, and M. Rarey. Fast Protein Binding Site Comparison via an Index-Based Screening Technology. Journal of Chemical Information and Modeling, 53:411–422, 2013.
  111. K. T. Schomburg and M. Rarey. Benchmark data sets for structure-based computa- tional target prediction. Journal of Chemical Information and Modeling, 54:2261–2274, 2014. BIBLIOGRAPHY [135] Schrödinger, LLC. The PyMOL Molecular Graphics System, Version 1.3r1. August 2010.
  112. A. Grishaev and A. Bax. An empirical backbone-backbone hydrogen-bonding potential in proteins and its applications to NMR structure refinement and validation. Journal of the American Chemical Society, 126(23):7281–7292, 2004.
  113. R. J. Zauhar, G. Moyna, L. Tian, Z. Li, and W. J. Welsh. Shape signatures: a new approach to computer-aided ligand-and receptor-based drug design. Journal of Medical Chemistry, 46(26):5674–90, 2003.
  114. R. Wang, X. Fang, Y. Lu, and S. Wang. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. Journal of Medicinal Chemistry, 47(12):2977–2980, 2004.
  115. R. A. Friesner, J. L. Banks, R. B. Murphy, T. A. Halgren, J. J. Klicic, D. T. Mainz, M. P. Repasky, E. H. Knoll, M. Shelley, J. K. Perry, D. E. Shaw, P. Francis, and P. S. Shenkin. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. Journal of Medicinal Chemistry, 47(7): 1739–1749, 2004.
  116. A. J. Chalk, C. L. Worth, J. P. Overington, and A. W. Chan. PDBLIG: classification of small molecular protein binding in the Protein Data Bank. Journal of Medical Chemistry, 47(15):3807–16, 2004.
  117. R. Wang, X. Fang, Y. Lu, C. Y. Yang, and S. Wang. The PDBbind database: methodologies and updates. Journal of Medicinal Chemistry, 48(12):4111–4119, 2005.
  118. N. Howard, C. Abell, W. Blakemore, G. Chessari, M. Congreve, S. Howard, H. Jhoti, C. W. Murray, L. C. A. Seavers, and R. L. M. van Montfort. Application for Fragment Screening and Fragment Linking to the Discovery of Novel Thrombin Inhibitors. Journal of Medicinal Chemistry, 49:1346–1355, 2006.
  119. J. Mestres, E. Gregori-Puigjané, S. Valverde, and R. V. Solé. The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Molecular BioSystems, 5(9):1051–7, 2009.
  120. J. A. Hanley and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1):29–36, 1982.
  121. E. Hellinger. Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen. Journal für die reine und angewandte Mathematik, 136:210–271, 1909.
  122. Le Guilloux, P. Schmidtke, and P. Tuffery. Fpocket: an Open Source Platform for Ligand Pocket Detection. BMC Bioinformatics, 10:168, 2009.
  123. B. Xiong, J. Wu, D. L. Burk, M. Xue, H. Jiang, and J. Shen. BSSF: a fingerprint based ultrafast binding site similarity search and function analysis server. BMC BIBLIOGRAPHY 193
  124. B. Hoffmann, M. Zaslavskiy, J. Vert, and V. Stoven. A New Protein Binding Pocket Similarity Measure Based on Comparison of Clouds of Atoms in 3D: Application to Ligand Prediction. BMC Bioinformatics, 11:99, 2010.
  125. F. Teichert, U. Bastolla, and M. Porto. SABERTOOTH: protein structural alignment based on a vectorial structure representation. BMC Bioinformatics, 8(1):425–442, 2007. BIBLIOGRAPHY [147] J. M. Thornton. From genome to function. Science, 292(5524):2095–2097, 2001.
  126. K. Yeturu and N. Chandra. PocketMatch: a new algorithm to compare binding sites in protein structures. BMC Bioinformatics, 9(543), 2008.
  127. T. A. Binkowski and A. Joachimiak. Protein Functional Surfaces: Global Shape Matching and Local Spatial Alignments of Ligand Binding Sites. BMC Structural Biology, 8(45), 2008.
  128. M. Weisel, E. Proschak, and G. Schneider. PocketPicker: Analysis of Ligand Binding- Sites with Shape Descriptors. Chemistry Central Journal, 1:7, 2007.
  129. U. Feige, S. Goldwasser, L. Lovasz, S. Safra, and M. Szegedy. Approximating clique is almost NP-complete. In Proc. 32nd IEEE Symp. on Foundations of Computer Science, pages 2–12, 1991.
  130. M. Mernberger, G. Klebe, and E. Hüllermeier. SEGA -A Semi-Global Approach to Graph Alignment for Approximate Molecular Structure Comparison. IEEE/ACM Transactions on Computational Biology and Bioinformatics, PrePrints, 2011.
  131. S. Leis, S. Schneider, and M. Zacharias. In silico prediction of binding sites on proteins. Current Medicinal Chemistry, 17:1550–1562, 2010.
  132. E. Karakas, N. Simorowski, and H. Furukawa. Subunit arrangement and phenylethanolamine binding in GluN1/GluN2B NMDA receptors. Nature, 475(7355): 249–253, 2011.
  133. C.-H. Lee, W. Lü, J. C. Michel, A. Goehring, X. Song J. Du, and E. Gouaux. NMDA receptor structures reveal subunit arrangement and pore architecture. Nature, 511 (7508):191–197, 2014.
  134. D. Lee, O. Redfern, and C. Orengo. Predicting protein function from sequence and structure. Nature Reviews Molecular Cell Biology, 8(12):995–1005, 2007.
  135. S. Das, A. Kokardekar, and C. M. Breneman. Rapid Comparison of Protein Binding Site Surfaces with Property Encoded Shape Distributions. Journal of Chemical Information and Modeling, 49:2863–2872, 2009.
  136. A. V. Morozov, T. Kortemme, K. Tsemekhman, and D. Baker. Close agreement between the orientation dependence of hydrogen bonds observed in protein structures and quantum mechanical calculations. PNAS, Proceedings of the National Academy of Sciences, 101(18):6946–6951, 2004.
  137. M. Debela, P. Hess, V. Magdolen, N. M. Schechter, T. Steiner, R. Huber, W. Bode, and P. Goettig. Chymotryptic specificity determinants in the 1.0 A structure of the zinc-inhibited human tissue kallikrein 7. Proceedings of the National Academy of Sciences, 104(41):16086–91, 2007.
  138. P. D. Dobson and A. J. Doig. Predicting Enzyme Class From Protein Structure Without Alignments. Journal of Molecular Biology, 345(1):187–199, 2005. ISSN 0022-2836. doi: 10.1016/j.jmb.2004.10.02. URL http://www.sciencedirect.com/ science/article/pii/S0022283604013166.
  139. S. G. Needleman and C. D. Wunsch. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48:443–453, 1970.
  140. Temple F. Smith and Michael S. Waterman. Identification of Common Molecular Subsequences. Journal of Molecular Biology, 147:195–197, 1981.
  141. I. D. Kuntz, J. M. Blaney, S. J. Oatley, R. Langridge, and T. E. Ferrin. A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology, 161(2): 269–288, 1982.
  142. R. A. Laskowski. SURFNET: A Program for Visualizing Molecular Surfaces, Cavities, and Intermolecular Interactions. Journal of Molecular Graphics, 13:323–330, 1995. BIBLIOGRAPHY
  143. A. E. Todd, C. A. Orengo, and J. M. Thornton. Evolution of function in protein superfamilies, from a structural perspective. Journal of Molecular Biology, 307(4): 1113–1143, 2001.
  144. S. Schmitt, D. Kuhn, and G. Klebe. A new method to detect related function among proteins independent of sequence and fold homology. Journal of Molecular Biology, 323(2):387–406, 2002.
  145. M. Hendlich, A. Bergner, J. Günther, and G. Klebe. Relibase: design and development of a database for comprehensive analysis of protein-ligand interactions. Journal of Molecular Biology, 326(2):607–620, 2003.
  146. J. Günther, A. Bergner, M. Hendlich, and G. Klebe. Utilising structural knowledge in drug design strategies: applications using Relibase. Journal of Molecular Biology, 326(2):621–636, 2003.
  147. T. Kortemme, A. V. Morozov, and D. Baker. An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes. Journal of Molecular Biology, 326(4):1239–1259, 2003.
  148. T. A. Binkowski, L. Adamian, and J. Liang. Inferring functional relationships of proteins from local sequence and spatial surface patterns. Journal of Molecular Biology, 332(2):505–526, 2003.
  149. A. Shulman-Peleg, R. Nussinov, and H. J. Wolfson. Recognition of functional sites in protein structures. Journal of Molecular Biology, 339(3):607–633, 2004.
  150. G. R. Stockwell and J. M. Thornton. Conformational Diversity of Ligands Bound to Proteins. Journal of Molecular Biology, 356:928–944, 2006.
  151. I. K. McDonald and J. M. Thornton. Satisfying hydrogen bonding potential in proteins. Journal of Molecular Biology, 238(5):777–793, 1994.
  152. M. Rarey, B. Kramer, T. Lengauer, and G. Klebe. A fast flexible docking method using an incremental construction algorithm. Journal of Molecular Biology, 261(3): 470–489, 1996.
  153. R. B. Russell. Detection of protein three-dimensional side-chain patterns: new examples of convergent evolution. Journal of Molecular Biology, 279:1211–1227, 1998.
  154. M. L. Verdonk, J. C. Cole, and R. Taylor. SuperStar: A Knowledge-based Approach for Identifying Interaction Sites in Proteins. Journal of Molecular Biology, 289: 1093–1108, 1999.
  155. H. Gohlke, M. Hendlich, and G. Klebe. Knowledge-based scoring function to predict protein-ligand interactions. Journal of Molecular Biology, 2(295):337–356, 2000.
  156. Andrew P. Bradley. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30:1145–1159, 1997.
  157. S. C. Sim and M. Ingelman-Sundberg. Pharmacogenomic biomarkers: new tools in current and future drug therapy. Trends in Pharmacological Sciences, 32(2):72–81, 2011.
  158. H. Bunke and K. Shearer. A Graph Distance Metric Based on the Maximal Common Subgraph. Pattern Recognition Letters, 19(3-4):255–259, 1998.
  159. Christopher A. Lipinski, Franco Lombardo, Beryl W. Dominy, and Paul J. Feeney. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23 (1–3):3–25, 1997.
  160. C.A. Orengo, A.D. Michie, S. Jones, D.T. Jones, M.B. Swindells, and J.M. Thornton. CATH -a hierarchic classification of protein domain structures. Structure, 5(8): 1093–1108, 1997.
  161. M. Hendlich, F. Rippmann, and G. Barnickel. LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. Journal of Molecular Graphics and Modelling, 15(6):359–363, 1997.
  162. Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. Drug Discov Today, 15(15-16):656–67, 2010.


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