Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins

Abstract

FTMap is a computational mapping server that identifies binding hot spots of macromolecules—i.e., regions of the surface with major contributions to the ligand-binding free energy. To use FTMap, users submit a protein, DNA or RNA structure in PDB (Protein Data Bank) format. FTMap samples billions of positions of small organic molecules used as probes, and it scores the probe poses using a detailed energy expression. Regions that bind clusters of multiple probe types identify the binding hot spots in good agreement with experimental data. FTMap serves as the basis for other servers, namely FTSite, which is used to predict ligand-binding sites, FTFlex, which is used to account for side chain flexibility, FTMap/param, used to parameterize additional probes and FTDyn, for mapping ensembles of protein structures. Applications include determining the druggability of proteins, identifying ligand moieties that are most important for binding, finding the most bound-like conformation in ensembles of unliganded protein structures and providing input for fragment-based drug design. FTMap is more accurate than classical mapping methods such as GRID and MCSS, and it is much faster than the more-recent approaches to protein mapping based on mixed molecular dynamics. By using 16 probe molecules, the FTMap server finds the hot spots of an average-size protein in <1 h. As FTFlex performs mapping for all low-energy conformers of side chains in the binding site, its completion time is proportionately longer.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Principles and tools of the FTMap algorithm.
Figure 2: Flowchart of the FTMap algorithm.
Figure 3: Flowchart of the FTFlex algorithm.
Figure 4: Screenshot of FTMap results for PDB ID 2ren (apo structure of renin).
Figure 5: Viewing FTMap results for PDB ID 2ren (apo structure of renin) using PyMOL.
Figure 6: Screen capture of FTSite results for human lymphocyte kinase (Lck, PDB ID 3lck).
Figure 7: Transition from stage 1 to stage 2 in FTFlex: selection of consensus clusters around which low-energy side chain conformers will be explored in repeated mapping calculations.
Figure 8: Screenshot of the PyMOL session from the application of FTMap/param to the unbound structure of thrombin (PDB ID 1hxf) with the user-defined probe molecule 1-(3-chlorophenyl)methanamine, a thrombin inhibitor.
Figure 9: Screenshots of the result page when applying FTDyn to map an ensemble of 24 structures of the MDM2 protein (PDB ID 1z1m), determined by NMR.
Figure 10: Mapping of the 24 MDM2 structures obtained by NMR using FTDyn.
Figure 11: Comparison of FTMap results with experimental data.
Figure 12: Consensus sites identified using FTMap for thrombin (PDB ID 1ths) are shown in line representation, overlapping with fragments and a high-affinity ligand shown as sticks.
Figure 13: Flexible mapping of the apo structure of CDK2 (PDB ID 1pw2) using FTFlex.

Accession codes

Accessions

Protein Data Bank

References

  1. 1

    DeLano, W.L., Ultsch, M.H., de Vos, A.M. & Wells, J.A. Convergent solutions to binding at a protein-protein interface. Science 287, 1279–1283 (2000).

    CAS  PubMed  Google Scholar 

  2. 2

    Thanos, C.D., DeLano, W.L. & Wells, J.A. Hot-spot mimicry of a cytokine receptor by a small molecule. Proc. Natl. Acad. Sci. USA 103, 15422–15427 (2006).

    CAS  PubMed  Google Scholar 

  3. 3

    DeLano, W.L. Unraveling hot spots in binding interfaces: progress and challenges. Curr. Opin. Struct. Biol. 12, 14–20 (2002).

    CAS  PubMed  Google Scholar 

  4. 4

    Clackson, T. & Wells, J.A. A hot spot of binding energy in a hormone-receptor interface. Science 267, 383–386 (1995).

    CAS  PubMed  Google Scholar 

  5. 5

    Keskin, O., Ma, B.Y. & Nussinov, R. Hot regions in protein-protein interactions: the organization and contribution of structurally conserved hot spot residues. J. Mol. Biol. 345, 1281–1294 (2005).

    CAS  PubMed  Google Scholar 

  6. 6

    Bogan, A.A. & Thorn, K.S. Anatomy of hot spots in protein interfaces. J. Mol. Biol. 280, 1–9 (1998).

    CAS  PubMed  Google Scholar 

  7. 7

    Kortemme, T. & Baker, D. A simple physical model for binding energy hot spots in protein-protein complexes. Proc. Natl. Acad. Sci. USA 99, 14116–14121 (2002).

    CAS  PubMed  Google Scholar 

  8. 8

    Hajduk, P.J., Huth, J.R. & Fesik, S.W. Druggability indices for protein targets derived from NMR-based screening data. J. Med. Chem. 48, 2518–2525 (2005).

    CAS  PubMed  Google Scholar 

  9. 9

    Vajda, S. & Guarnieri, F. Characterization of protein-ligand interaction sites using experimental and computational methods. Curr. Opin. Drug Discov. Devel. 9, 354–362 (2006).

    CAS  PubMed  Google Scholar 

  10. 10

    Seco, J., Luque, F.J. & Barril, X. Binding site detection and druggability index from first principles. J. Med. Chem. 52, 2363–2371 (2009).

    CAS  PubMed  Google Scholar 

  11. 11

    Mattos, C. & Ringe, D. Locating and characterizing binding sites on proteins. Nat. Biotechnol. 14, 595–599 (1996).

    CAS  PubMed  Google Scholar 

  12. 12

    Allen, K.N. et al. An experimental approach to mapping the binding surfaces of crystalline proteins. J. Phys. Chem. 100, 2605–2611 (1996).

    CAS  Google Scholar 

  13. 13

    Ciulli, A., Williams, G., Smith, A.G., Blundell, T.L. & Abell, C. Probing hot spots at protein-ligand binding sites: a fragment-based approach using biophysical methods. J. Med. Chem. 49, 4992–5000 (2006).

    CAS  PubMed  Google Scholar 

  14. 14

    Dennis, S., Kortvelyesi, T. & Vajda, S. Computational mapping identifies the binding sites of organic solvents on proteins. Proc. Natl. Acad. Sci. USA 99, 4290–4295 (2002).

    CAS  PubMed  Google Scholar 

  15. 15

    Silberstein, M. et al. Identification of substrate binding sites in enzymes by computational solvent mapping. J. Mol. Biol. 332, 1095–1113 (2003).

    CAS  PubMed  Google Scholar 

  16. 16

    Landon, M.R. et al. Detection of ligand binding hot spots on protein surfaces via fragment-based methods: application to DJ-1 and glucocerebrosidase. J. Comput. Aided Mol. Des. 23, 491–500 (2009).

    CAS  PubMed  Google Scholar 

  17. 17

    Kuttner, Y.Y. & Engel, S. Protein hot spots: the islands of stability. J. Mol. Biol. 415, 419–428 (2012).

    CAS  PubMed  Google Scholar 

  18. 18

    Landon, M.R., Lancia, D.R. Jr., Yu, J., Thiel, S.C. & Vajda, S. Identification of hot spots within druggable binding regions by computational solvent mapping of proteins. J. Med. Chem. 50, 1231–1240 (2007).

    CAS  PubMed  Google Scholar 

  19. 19

    Brenke, R. et al. Fragment-based identification of druggable ′hot spots′ of proteins using Fourier domain correlation techniques. Bioinformatics 25, 621–627 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Chuang, G.Y. et al. Binding hot spots and amantadine orientation in the influenza a virus M2 proton channel. Biophys. J. 97, 2846–2853 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Landon, M.R. et al. Novel druggable hot spots in avian influenza neuraminidase H5N1 revealed by computational solvent mapping of a reduced and representative receptor ensemble. Chem. Biol. Drug Des. 71, 106–116 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Ngan, C.H. et al. FTSite: high accuracy detection of ligand binding sites on unbound protein structures. Bioinformatics 28, 286–287 (2012).

    CAS  PubMed  Google Scholar 

  23. 23

    Villar, E.A. et al. How proteins bind macrocycles. Nat. Chem. Biol. 10, 723–731 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Zerbe, B.S., Hall, D.R., Vajda, S., Whitty, A. & Kozakov, D. Relationship between hot spot residues and ligand binding hot spots in protein-protein interfaces. J. Chem. Inf. Model. 52, 2236–2244 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Rees, D.C., Congreve, M., Murray, C.W. & Carr, R. Fragment-based lead discovery. Nat. Rev. Drug Discov. 3, 660–672 (2004).

    CAS  Google Scholar 

  26. 26

    Erlanson, D.A., McDowell, R.S. & O'Brien, T. Fragment-based drug discovery. J. Med. Chem. 47, 3463–3482 (2004).

    CAS  PubMed  Google Scholar 

  27. 27

    Hartshorn, M.J. et al. Fragment-based lead discovery using X-ray crystallography. J. Med. Chem. 48, 403–413 (2005).

    CAS  PubMed  Google Scholar 

  28. 28

    Kozakov, D. et al. Structural conservation of druggable hot spots in protein-protein interfaces. Proc. Natl. Acad. Sci. USA 108, 13528–13533 (2011).

    CAS  PubMed  Google Scholar 

  29. 29

    Hall, D.R., Ngan, C.H., Zerbe, B.S., Kozakov, D. & Vajda, S. Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery. J. Chem. Inf. Model. 52, 199–209 (2012).

    CAS  PubMed  Google Scholar 

  30. 30

    Grove, L.E., Hall, D.R., Beglov, D., Vajda, S. & Kozakov, D. FTFlex: accounting for binding site flexibility to improve fragment-based identification of druggable hot spots. Bioinformatics 29, 1218–1219 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Ngan, C.H. et al. FTMAP: extended protein mapping with user-selected probe molecules. Nucleic Acids Res. 40, W271–275 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Bohnuud, T., Kozakov, D. & Vajda, S. Evidence of conformational selection driving the formation of ligand binding sites in protein-protein interfaces. PLoS Comput. Biol. 10, e1003872 (2014).

    PubMed  PubMed Central  Google Scholar 

  33. 33

    Ivetac, A. & McCammon, J.A. Mapping the druggable allosteric space of G protein–coupled receptors: a fragment-based molecular dynamics approach. Chem. Biol. Drug Des. 76, 201–217 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Hall, D.H. et al. Robust identification of binding hot spots using continuum electrostatics: application to hen egg-white lysozyme. J. Am. Chem. Soc. 133, 20668–20671 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Hall, D.R., Kozakov, D. & Vajda, S. Analysis of protein binding sites by computational solvent mapping. Methods Mol. Biol. 819, 13–27 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Brooks, B.R. et al. Charmm - a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4, 187–217 (1983).

    CAS  Google Scholar 

  37. 37

    Schaefer, M. & Karplus, M. A comprehensive analytical treatment of continuum electrostatics. J. Phys. Chem. 100, 1578–1599 (1996).

    CAS  Google Scholar 

  38. 38

    Goodford, P.J. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J. Med. Chem. 28, 849–857 (1985).

    CAS  PubMed  Google Scholar 

  39. 39

    Miranker, A. & Karplus, M. Functionality maps of binding-sites - a multiple copy simultaneous search method. Proteins 11, 29–34 (1991).

    CAS  PubMed  Google Scholar 

  40. 40

    Mattos, C. et al. Multiple solvent crystal structures: probing binding sites, plasticity and hydration. J. Mol. Biol. 357, 1471–1482 (2006).

    CAS  PubMed  Google Scholar 

  41. 41

    Berman, H.M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Bohnuud, T. et al. Computational mapping reveals dramatic effect of Hoogsteen breathing on duplex DNA reactivity with formaldehyde. Nucleic Acids Res. 40, 7644–7652 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Chuang, G.Y., Kozakov, D., Brenke, R., Comeau, S.R. & Vajda, S. DARS (Decoys As the Reference State) potentials for protein-protein docking. Biophys. J. 95, 4217–4227 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Beglov, D. et al. Minimal ensembles of side chain conformers for modeling protein-protein interactions. Proteins 80, 591–601 (2012).

    CAS  PubMed  Google Scholar 

  45. 45

    O′Boyle, N.M., Vandermeersch, T., Flynn, C.J., Maguire, A.R. & Hutchison, G.R. Confab: systematic generation of diverse low-energy conformers. J. Cheminform. 3, 8 (2011).

    PubMed  PubMed Central  Google Scholar 

  46. 46

    Wang, J., Wang, W., Kollman, P.A. & Case, D.A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model. 25, 247–260 (2006).

    PubMed  Google Scholar 

  47. 47

    Wang, J., Wolf, R.M., Caldwell, J.W., Kollman, P.A. & Case, D.A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).

    CAS  PubMed  Google Scholar 

  48. 48

    Alexeev, Y., Mazanetz, M.P., Ichihara, O. & Fedorov, D.G. GAMESS as a free quantum-mechanical platform for drug research. Curr. Top. Med. Chem. 12, 2013–2033 (2012).

    CAS  PubMed  Google Scholar 

  49. 49

    Jakalian, A., Jack, D.B. & Bayly, C.I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J. Comput. Chem. 23, 1623–1641 (2002).

    CAS  PubMed  Google Scholar 

  50. 50

    Votapka, L. & Amaro, R.E. Multistructural hot spot characterization with FTProd. Bioinformatics 29, 393–394 (2013).

    CAS  PubMed  Google Scholar 

  51. 51

    Guerois, R., Nielsen, J.E. & Serrano, L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1,000 mutations. J. Mol. Biol. 320, 369–387 (2002).

    CAS  PubMed  Google Scholar 

  52. 52

    Zhu, X. & Mitchell, J.C. KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features. Proteins 79, 2671–2683 (2011).

    CAS  PubMed  Google Scholar 

  53. 53

    Tuncbag, N., Gursoy, A. & Keskin, O. Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy. Bioinformatics 25, 1513–1520 (2009).

    CAS  PubMed  Google Scholar 

  54. 54

    Deng, L. et al. PredHS: a web server for predicting protein-protein interaction hot spots by using structural neighborhood properties. Nucleic Acids Res. 42, W290–W295 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Laurie, A.T. & Jackson, R.M. Methods for the prediction of protein-ligand binding sites for structure-based drug design and virtual ligand screening. Curr. Protein Pept. Sci. 7, 395–406 (2006).

    CAS  PubMed  Google Scholar 

  56. 56

    Levitt, D.G. & Banaszak, L.J. Pocket: a computer-graphics method for identifying and displaying protein cavities and their surrounding amino acids. J. Mol. Graph. 10, 229–234 (1992).

    CAS  PubMed  Google Scholar 

  57. 57

    Huang, B. & Schroeder, M. LIGSITEcsc: predicting ligand binding sites using the Connolly surface and degree of conservation. BMC Struct. Biol. 6, 19 (2006).

    PubMed  PubMed Central  Google Scholar 

  58. 58

    Hendlich, M., Rippmann, F. & Barnickel, G. LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J. Mol. Graph. Model 15, 359–363, 389 (1997).

    CAS  PubMed  Google Scholar 

  59. 59

    Brady, G.P. Jr. & Stouten, P.F. Fast prediction and visualization of protein binding pockets with PASS. J. Comput. Aided Mol. Des. 14, 383–401 (2000).

    CAS  PubMed  Google Scholar 

  60. 60

    Binkowski, T.A., Naghibzadeh, S. & Liang, J. CASTp: Computed atlas of surface topography of proteins. Nucleic Acids Res. 31, 3352–3355 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Wass, M.N., Kelley, L.A. & Sternberg, M.J. 3DLigandSite: predicting ligand-binding sites using similar structures. Nucleic Acids Res. 38, W469–W473 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Brylinski, M. & Skolnick, J. A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation. Proc. Natl. Acad. Sci. USA 105, 129–134 (2008).

    CAS  PubMed  Google Scholar 

  63. 63

    Chou, K.C. & Cai, Y.D. A novel approach to predict active sites of enzyme molecules. Proteins 55, 77–82 (2004).

    CAS  PubMed  Google Scholar 

  64. 64

    Laurie, A.T.R. & Jackson, R.M. Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 21, 1908–1916 (2005).

    CAS  PubMed  Google Scholar 

  65. 65

    Halgren, T.A. Identifying and characterizing binding sites and assessing druggability. J. Chem. Inf. Model. 49, 377–389 (2009).

    CAS  PubMed  Google Scholar 

  66. 66

    Hernandez, M., Ghersi, D. & Sanchez, R. SITEHOUND-web: a server for ligand binding site identification in protein structures. Nucleic Acids Res. 37, W413–W416 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67

    English, A.C., Done, S.H., Caves, L.S., Groom, C.R. & Hubbard, R.E. Locating interaction sites on proteins: the crystal structure of thermolysin soaked in 2% to 100% isopropanol. Proteins 37, 628–640 (1999).

    CAS  PubMed  Google Scholar 

  68. 68

    English, A.C., Groom, C.R. & Hubbard, R.E. Experimental and computational mapping of the binding surface of a crystalline protein. Protein Eng. 14, 47–59 (2001).

    CAS  PubMed  Google Scholar 

  69. 69

    Haider, M.K., Bertrand, H.O. & Hubbard, R.E. Predicting fragment binding poses using a combined MCSS MM-GBSA approach. J. Chem. Inf. Model. 51, 1092–1105 (2011).

    CAS  PubMed  Google Scholar 

  70. 70

    Lexa, K.W. & Carlson, H.A. Improving protocols for protein mapping through proper comparison to crystallography data. J. Chem. Inf. Model. 53, 391–402 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Bakan, A., Nevins, N., Lakdawala, A.S. & Bahar, I. Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules. J. Chem. Theory Comput. 8, 2435–2447 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Raman, E.P., Yu, W., Lakkaraju, S.K. & MacKerell, A.D. Jr. Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach. J. Chem. Inf. Model. 53, 3384–3398 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Yu, W., Lakkaraju, S.K., Raman, E.P. & Mackerell, A.D. Jr. Site identification by ligand competitive saturation (SILCS) assisted pharmacophore modeling. J. Comput. Aided Mol. Des. 8, 491–507 (2014).

    Google Scholar 

  74. 74

    Sielecki, A.R. et al. Structure of recombinant human renin, a target for cardiovascular-active drugs, at 2.5 Å resolution. Science 243, 1346–1351 (1989).

    CAS  PubMed  Google Scholar 

  75. 75

    Rahuel, J. et al. Structure-based drug design: the discovery of novel nonpeptide orally active inhibitors of human renin. Chem. Biol. 7, 493–504 (2000).

    CAS  PubMed  Google Scholar 

  76. 76

    Rahuel, J., Priestle, J.P. & Grutter, M.G. The crystal structures of recombinant glycosylated human renin alone and in complex with a transition state analog inhibitor. J. Struct. Biol. 107, 227–236 (1991).

    CAS  PubMed  Google Scholar 

  77. 77

    Dechene, M., Wink, G., Smith, M., Swartz, P. & Mattos, C. Multiple solvent crystal structures of ribonuclease A: an assessment of the method. Proteins 76, 861–881 (2009).

    CAS  PubMed  Google Scholar 

  78. 78

    Villar, E.A. et al. How proteins bind macrocycles. Nat. Chem. Biol. 10, 723–731 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Qiu, X.Y., Yin, M.L., Padmanabhan, K.P., Krstenansky, J.L. & Tulinsky, A. Structures of thrombin complexes with a designed and a natural exosite peptide inhibitor. J. Biol. Chem. 268, 20318–20326 (1993).

    CAS  PubMed  Google Scholar 

  80. 80

    Howard, N. et al. Application of fragment screening and fragment linking to the discovery of novel thrombin inhibitors. J. Med. Chem. 49, 1346–1355 (2006).

    CAS  PubMed  Google Scholar 

  81. 81

    Howard, N. et al. Application of fragment screening and fragment linking to the discovery of novel thrombin inhibitors. J. Med. Chem. 49, 1346–1355 (2006).

    CAS  PubMed  Google Scholar 

  82. 82

    Yamaguchi, H. & Hendrickson, W.A. Structural basis for activation of human lymphocyte kinase Lck upon tyrosine phosphorylation. Nature 384, 484–489 (1996).

    CAS  Google Scholar 

  83. 83

    Zhu, X.T. et al. Structural analysis of the lymphocyte-specific kinase Lck in complex with non-selective and Src family selective kinase inhibitors. Structure 7, 651–661 (1999).

    CAS  PubMed  Google Scholar 

  84. 84

    Wu, S.Y. et al. Discovery of a novel family of CDK inhibitors with the program LIDAEUS: structural basis for ligand-induced disordering of the activation loop. Structure 11, 399–410 (2003).

    CAS  PubMed  Google Scholar 

  85. 85

    Bramson, H.N. et al. Oxindole-based inhibitors of cyclin-dependent kinase 2 (CDK2): design, synthesis, enzymatic activities, and X-ray crystallographic analysis. J. Med. Chem. 44, 4339–4358 (2001).

    CAS  PubMed  Google Scholar 

  86. 86

    Zhang, E. & Tulinsky, A. The molecular environment of the Na+ binding site of thrombin. Biophys. Chem. 63, 185–200 (1997).

    CAS  PubMed  Google Scholar 

  87. 87

    Howard, N. et al. Application of fragment screening and fragment linking to the discovery of novel thrombin inhibitors. J. Med. Chem. 49, 1346–1355 (2006).

    CAS  PubMed  Google Scholar 

  88. 88

    Uhrinova, S. et al. Structure of free MDM2 N-terminal domain reveals conformational adjustments that accompany p53-binding. J. Mol. Biol. 350, 587–598 (2005).

    CAS  PubMed  Google Scholar 

  89. 89

    Michelsen, K. et al. Ordering of the N-terminus of human MDM2 by small-molecule inhibitors. J. Am. Chem. Soc. 134, 17059–17067 (2012).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This investigation was supported by grants GM064700 from the National Institute of General Medical Sciences.

Author information

Affiliations

Authors

Contributions

D.K., D.R.H., T.B., B.X. and S.E.M. developed the servers; D.K., L.E.G., D.R.H., T.B., D.B., L.L. and B.X. performed experiments; L.E.G., D.R.H. and S.V. prepared the manuscript with editing by T.B., S.V. and L.E.G.

Corresponding authors

Correspondence to Dima Kozakov or Sandor Vajda.

Ethics declarations

Competing interests

D.R.H. is a full-time employee of Acpharis Inc., and the company licenses software related to protein mapping. D.K., D.B. and S.V. own stock in Acpharis. However, all programs and servers described here are free for research use.

Supplementary information

Supplementary Text and Figures

Supplementary Table 1 (PDF 84 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kozakov, D., Grove, L., Hall, D. et al. The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins. Nat Protoc 10, 733–755 (2015). https://doi.org/10.1038/nprot.2015.043

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing