Skip to main content
SpringerLink
Log in

Function Prediction Using Patches, Pockets and Other Surface Properties

  • Chapter
  • First Online:
From Protein Structure to Function with Bioinformatics

  • 3095 Accesses

Abstract

With few exceptions protein functions depend sensitively upon their interactions with other biomolecules. Thus, the surface of a protein is of particular interest for function annotation: definition of the protein surface in experimental or modelled protein structure enables the application of a wide range of structural bioinformatic tools for function prediction. The development of such tools has been significantly accelerated in recent years as a response to the flux of information from Structural Genomics programs which, at least in part, have deliberately targeted mysterious protein families of unknown function about which conventional homology-based protein function annotation can say little or nothing (Bateman et al. in Acta Crystallographica Section F: Structural Biology and Crystallization Communications 66:1148–1152, 2010). As this chapter will illustrate, the underlying principles behind the resulting toolset vary impressively but, ultimately, most are based upon discovering putative interaction sites through detecting ways in which they differ somehow from protein surface in general. These differences may be physicochemical, electrostatic or steric in nature, or be of evolutionary origin. Predictions can be strengthened by observing concordant results from orthogonal methods. Indeed, many programs now improve performance by combining multiple factors in their calculations. Some methods find functional sites in general, others provide direct evidence supporting specific biochemical functions. This chapter will not attempt a comprehensive historical overview of the area, rather aiming to guide the user to the current state of the art while acknowledging key methodology papers. Methods that are readily available will be favoured, particularly those implemented at servers and those for which plug-ins for popular molecular visualisation tools exist.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Adamian L, Naveed H, Liang J (2011) Lipid-binding surfaces of membrane proteins: evidence from evolutionary and structural analysis. Biochim Biophys Acta 1808(4):1092–1102

    Article  CAS  PubMed  Google Scholar 

  • Anand P, Chandra N (2014) Characterizing the pocketome of Mycobacterium tuberculosis and application in rationalizing polypharmacological target selection. Sci Rep 4:6356

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Arkin MR, Wells JA (2004) Small-molecule inhibitors of protein-protein interactions: progressing towards the dream. Nat Rev Drug Discov 3(4):301–317

    Article  CAS  PubMed  Google Scholar 

  • Ashkenazy H, Erez E, Martz E et al (2010) ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res 38(Web Server issue):W529–W533

    Google Scholar 

  • Ashkenazy H, Abadi S, Martz E et al (2016) ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res 44(W1):W344–W350

    Article  PubMed  PubMed Central  Google Scholar 

  • Bakan A, Nevins N, Lakdawala AS et al (2012) Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules. J Chem Theory Comput 8(7):2435–2447

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Baker NA, Sept D, Joseph S et al (2001) Electrostatics of nanosystems: application to microtubules and the ribosome. Proc Natl Acad Sci U S A 98(18):10037–10041

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bartlett GJ, Porter CT, Borkakoti N et al (2002) Analysis of catalytic residues in enzyme active sites. J Mol Biol 324(1):105–121

    Article  CAS  PubMed  Google Scholar 

  • Bateman A, Coggill P, Finn RD (2010) DUFs: families in search of function. Acta Crystallogr, Sect F: Struct Biol Cryst Commun 66(Pt 10):1148–1152

    Article  CAS  Google Scholar 

  • Beadle BM, Shoichet BK (2002) Structural bases of stability-function tradeoffs in enzymes. J Mol Biol 321(2):285–296

    Article  CAS  PubMed  Google Scholar 

  • Ben-Shimon A, Eisenstein M (2005) Looking at enzymes from the inside out: the proximity of catalytic residues to the molecular centroid can be used for detection of active sites and enzyme-ligand interfaces. J Mol Biol 351(2):309–326

    Article  CAS  PubMed  Google Scholar 

  • Berka K, Hanak O, Sehnal D et al (2012) MOLEonline 2.0: interactive web-based analysis of biomacromolecular channels. Nucleic Acids Res 40(Web Server issue):W222-W227

    Google Scholar 

  • Bianchi V, Mangone I, Ferre F et al (2013) webPDBinder: a server for the identification of ligand binding sites on protein structures. Nucleic Acids Res 41(Web Server issue):W308-W313

    Google Scholar 

  • Bogan AA, Thorn KS (1998) Anatomy of hot spots in protein interfaces. J Mol Biol 280(1):1–9

    Article  CAS  PubMed  Google Scholar 

  • Brady GP Jr, Stouten PF (2000) Fast prediction and visualization of protein binding pockets with PASS. J Comput Aided Mol Des 14(4):383–401

    Article  CAS  PubMed  Google Scholar 

  • Brylinski M (2014) eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models. PLoS Comput Biol 10(9):e1003829

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Brylinski M, Skolnick J (2009) FINDSITE: a threading-based approach to ligand homology modeling. PLoS Comput Biol 5(6):e1000405

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Burgoyne NJ, Jackson RM (2009) Predicting protein function from surface properties. In: Rigden DJ (ed) From protein structure to function with bioinformatics, 1st edn. Springer, Berlin, pp 167–186

    Google Scholar 

  • Capra JA, Laskowski RA, Thornton JM et al (2009) Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure. PLoS Comput Biol 5(12):e1000585

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Chagoyen M, Garcia-Martin JA, Pazos F (2016) Practical analysis of specificity-determining residues in protein families. Brief Bioinform 17(2):255–261

    Article  PubMed  Google Scholar 

  • Chartier M, Najmanovich R (2015) Detection of binding site molecular interaction field similarities. J Chem Inf Model 55(8):1600–1615

    Article  CAS  PubMed  Google Scholar 

  • Chartier M, Adriansen E, Najmanovich R (2016) IsoMIF finder: online detection of binding site molecular interaction field similarities. Bioinformatics 32(4):621–623

    Article  CAS  PubMed  Google Scholar 

  • Chen BY (2014) VASP-E: specificity annotation with a volumetric analysis of electrostatic isopotentials. PLoS Comput Biol 10(8):e1003792

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Chen CT, Peng HP, Jian JW et al (2012a) Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces. PLoS ONE 7(6):e37706

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen YC, Wright JD, Lim C (2012b) DR_bind: a web server for predicting DNA-binding residues from the protein structure based on electrostatics, evolution and geometry. Nucleic Acids Res 40(Web Server issue):W249–W256

    Google Scholar 

  • Chien YT, Huang SW (2012) Accurate prediction of protein catalytic residues by side chain orientation and residue contact density. PLoS ONE 7(10):e47951

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chovancova E, Pavelka A, Benes P et al (2012) CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures. PLoS Comput Biol 8(10):e1002708

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Connolly ML (1983) Analytical molecular surface calculation. J Appl Cryst 16:548–558

    Article  CAS  Google Scholar 

  • De Smet F, Christopoulos A, Carmeliet P (2014) Allosteric targeting of receptor tyrosine kinases. Nat Biotechnol 32(11):1113–1120

    Article  PubMed  CAS  Google Scholar 

  • de Vries SJ, Bonvin AM (2011) CPORT: a consensus interface predictor and its performance in prediction-driven docking with HADDOCK. PLoS ONE 6(3):e17695

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • del Sol A, Fujihashi H, Amoros D et al (2006) Residue centrality, functionally important residues, and active site shape: analysis of enzyme and non-enzyme families. Protein Sci 15(9):2120–2128

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Ding XM, Pan XY, Xu C et al (2010) Computational prediction of DNA-protein interactions: a review. Curr Comput Aided Drug Des 6(3):197–206

    Article  CAS  PubMed  Google Scholar 

  • Dundas J, Ouyang Z, Tseng J et al (2006) CASTp: computed atlas of surface topography of proteins with structural and topographical mapping of functionally annotated residues. Nucleic Acids Res 34(Web Server issue):W116–W118

    Google Scholar 

  • Elcock AH (2001) Prediction of functionally important residues based solely on the computed energetics of protein structure. J Mol Biol 312(4):885–896

    Article  CAS  PubMed  Google Scholar 

  • Esmaielbeiki R, Krawczyk K, Knapp B et al (2016) Progress and challenges in predicting protein interfaces. Brief Bioinform 17(1):117–131

    Article  PubMed  Google Scholar 

  • Feher M, Schmidt JM (2003) Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry. J Chem Inf Comput Sci 43(1):218–227

    Article  CAS  PubMed  Google Scholar 

  • Fersht A (1985) Enzyme structure and mechanism. Freeman, New York

    Google Scholar 

  • Friesner RA, Banks JL, Murphy RB et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749

    Article  CAS  PubMed  Google Scholar 

  • Gabdoulline RR, Stein M, Wade RC (2007) qPIPSA: relating enzymatic kinetic parameters and interaction fields. BMC Bioinformatics 8:373

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gao M, Skolnick J (2009) From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions. PLoS Comput Biol 5(3):e1000341

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Gao M, Skolnick J (2013) A comprehensive survey of small-molecule binding pockets in proteins. PLoS Comput Biol 9(10):e1003302

    Article  PubMed  PubMed Central  Google Scholar 

  • Ghersi D, Sanchez R (2009) EasyMIFS and SiteHound: a toolkit for the identification of ligand-binding sites in protein structures. Bioinformatics 25(23):3185–3186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Goldenberg O, Erez E, Nimrod G et al (2009) The ConSurf-DB: pre-calculated evolutionary conservation profiles of protein structures. Nucleic Acids Res 37(Database issue):D323–D327

    Google Scholar 

  • Han L, Zhang YJ, Song J et al (2012) Identification of catalytic residues using a novel feature that integrates the microenvironment and geometrical location properties of residues. PLoS ONE 7(7):e41370

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Haupt VJ, Daminelli S, Schroeder M (2013) Drug promiscuity in PDB: protein binding site similarity is key. PLoS ONE 8(6):e65894

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hendlich M, Rippmann F, Barnickel G (1997) LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins. J Mol Graph Model 15(6):359–363, 389

    Google Scholar 

  • Heo L, Shin WH, Lee MS et al (2014) GalaxySite: ligand-binding-site prediction by using molecular docking. Nucleic Acids Res 42(Web Server issue):W210–W214

    Google Scholar 

  • Hermann JC, Ghanem E, Li Y et al (2006) Predicting substrates by docking high-energy intermediates to enzyme structures. J Am Chem Soc 128(49):15882–15891

    Article  CAS  PubMed  Google Scholar 

  • Hermann JC, Marti-Arbona R, Fedorov AA et al (2007) Structure-based activity prediction for an enzyme of unknown function. Nature 448(7155):775–779

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Huang YF, Golding GB (2015) FuncPatch: a web server for the fast Bayesian inference of conserved functional patches in protein 3D structures. Bioinformatics 31:523–531

    Article  PubMed  CAS  Google Scholar 

  • Jalencas X, Mestres J (2013) Identification of similar binding sites to detect distant polypharmacology. Mol Inform 32:976–990

    Article  CAS  PubMed  Google Scholar 

  • Jambon M, Imberty A, Deleage G et al (2003) A new bioinformatic approach to detect common 3D sites in protein structures. Proteins 52(2):137–145

    Article  CAS  PubMed  Google Scholar 

  • Jones S, Thornton JM (1997) Analysis of protein-protein interaction sites using surface patches. J Mol Biol 272(1):121–132

    Article  CAS  PubMed  Google Scholar 

  • Jones S, Shanahan HP, Berman HM et al (2003) Using electrostatic potentials to predict DNA-binding sites on DNA-binding proteins. Nucleic Acids Res 31(24):7189–7198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jordan RA, El-Manzalawy Y, Dobbs D et al (2012) Predicting protein-protein interface residues using local surface structural similarity. BMC Bioinformatics 13:41-2105-13-41

    Google Scholar 

  • Kahraman A, Morris RJ, Laskowski RA et al (2010) On the diversity of physicochemical environments experienced by identical ligands in binding pockets of unrelated proteins. Proteins 78(5):1120–1136

    Article  CAS  PubMed  Google Scholar 

  • Kalinina OV, Gelfand MS, Russell RB (2009) Combining specificity determining and conserved residues improves functional site prediction. BMC Bioinformatics 10:174-2105-10-174

    Google Scholar 

  • Kanehisa M (2002) The KEGG database. Novartis Found Symp 247:91–101; discussion 101–103, 119–128, 244–252

    Google Scholar 

  • Kawabata T (2010) Detection of multiscale pockets on protein surfaces using mathematical morphology. Proteins 78(5):1195–1211

    Article  CAS  PubMed  Google Scholar 

  • Kawabata T, Go N (2007) Detection of pockets on protein surfaces using small and large probe spheres to find putative ligand binding sites. Proteins 68(2):516–529

    Article  CAS  PubMed  Google Scholar 

  • Kim OT, Yura K, Go N (2006) Amino acid residue doublet propensity in the protein-RNA interface and its application to RNA interface prediction. Nucleic Acids Res 34(22):6450–6460

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim J-, Cho Y, Lee M et al (2015) BetaCavityWeb: a webserver for molecular voids and tunnels. Nucleic Acids Res (in press)

    Google Scholar 

  • Kinnings SL, Xie L, Fung KH et al (2010) The Mycobacterium tuberculosis drugome and its polypharmacological implications. PLoS Comput Biol 6(11):e1000976

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kinoshita K, Nakamura H (2004) eF-site and PDBjViewer: database and viewer for protein functional sites. Bioinformatics 20(8):1329–1330

    Article  CAS  PubMed  Google Scholar 

  • Kinoshita K, Sadanami K, Kidera A et al (1999) Structural motif of phosphate-binding site common to various protein superfamilies: all-against-all structural comparison of protein-mononucleotide complexes. Protein Eng 12(1):11–14

    Article  CAS  PubMed  Google Scholar 

  • Konc J, Janezic D (2010) ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment. Bioinformatics 26(9):1160–1168

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Konc J, Janezic D (2014) ProBiS-ligands: a web server for prediction of ligands by examination of protein binding sites. Nucleic Acids Res 42(Web Server issue):W215–W220

    Google Scholar 

  • Kozlikova B, Sebestova E, Sustr V et al (2014) CAVER analyst 1.0: graphic tool for interactive visualization and analysis of tunnels and channels in protein structures. Bioinformatics 30(18):2684–2685

    Article  CAS  PubMed  Google Scholar 

  • Kulharia M, Bridgett SJ, Goody RS et al (2009) InCa-SiteFinder: a method for structure-based prediction of inositol and carbohydrate binding sites on proteins. J Mol Graph Model 28(3):297–303

    Article  CAS  PubMed  Google Scholar 

  • Lang PT, Brozell SR, Mukherjee S et al (2009) DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15(6):1219–1230

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Laskowski RA (1995) SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 13(5):323–330, 307–308

    Google Scholar 

  • Laskowski RA, Luscombe NM, Swindells MB et al (1996) Protein clefts in molecular recognition and function. Protein Sci 5(12):2438–2452

    CAS  PubMed  PubMed Central  Google Scholar 

  • Laurie AT, Jackson RM (2005) Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites. Bioinformatics 21(9):1908–1916

    Article  CAS  PubMed  Google Scholar 

  • Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:168-2105-10-168

    Google Scholar 

  • Lee B, Richards FM (1971) The interpretation of protein structures: estimation of static accessibility. J Mol Biol 55(3):379–400

    Article  CAS  PubMed  Google Scholar 

  • Lee TW, Yang AS, Brittain T et al (2015) An analysis approach to identify specific functional sites in orthologous proteins using sequence and structural information: application to neuroserpin reveals regions that differentially regulate inhibitory activity. Proteins 83(1):135–152

    Article  CAS  PubMed  Google Scholar 

  • Levitt DG, Banaszak LJ (1992) POCKET: a computer graphics method for identifying and displaying protein cavities and their surrounding amino acids. J Mol Graph 10(4):229–234

    Article  CAS  PubMed  Google Scholar 

  • Li H, Kasam V, Tautermann CS et al (2014a) Computational method to identify druggable binding sites that target protein-protein interactions. J Chem Inf Model 54(5):1391–1400

    Article  CAS  PubMed  Google Scholar 

  • Li S, Yamashita K, Amada KM et al (2014b) Quantifying sequence and structural features of protein-RNA interactions. Nucleic Acids Res 42(15):10086–10098

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Liang J, Edelsbrunner H, Fu P et al (1998) Analytical shape computation of macromolecules: II. Inaccessible cavities in proteins. Proteins 33(1):18–29

    Article  CAS  PubMed  Google Scholar 

  • Lichtarge O, Bourne HR, Cohen FE (1996) An evolutionary trace method defines binding surfaces common to protein families. J Mol Biol 257(2):342–358

    Article  CAS  PubMed  Google Scholar 

  • Lijnzaad P, Berendsen HJ, Argos P (1996) A method for detecting hydrophobic patches on protein surfaces. Proteins 26(2):192–203

    Article  CAS  PubMed  Google Scholar 

  • London N, Movshovitz-Attias D, Schueler-Furman O (2010) The structural basis of peptide-protein binding strategies. Structure 18(2):188–199

    Article  CAS  PubMed  Google Scholar 

  • Lu CH, Yu CS, Chien YT et al (2014) EXIA2: web server of accurate and rapid protein catalytic residue prediction. Biomed Res Int 2014:807839

    PubMed  PubMed Central  Google Scholar 

  • Lukk T, Sakai A, Kalyanaraman C et al (2012) Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily. Proc Natl Acad Sci U S A 109(11):4122–4127

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ma X, Qi Y, Lai L (2014) Allosteric sites can be identified based on the residue-residue interaction energy difference. Proteins

    Google Scholar 

  • Mahalingam R, Peng HP, Yang AS (2014a) Prediction of fatty acid-binding residues on protein surfaces with three-dimensional probability distributions of interacting atoms. Biophys Chem 192:10–19

    Article  CAS  PubMed  Google Scholar 

  • Mahalingam R, Peng HP, Yang AS (2014b) Prediction of FMN-binding residues with three-dimensional probability distributions of interacting atoms on protein surfaces. J Theor Biol 343:154–161

    Article  CAS  PubMed  Google Scholar 

  • Malik A, Ahmad S (2007) Sequence and structural features of carbohydrate binding in proteins and assessment of predictability using a neural network. BMC Struct Biol 7:1

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Mehio W, Kemp GJ, Taylor P et al (2010) Identification of protein binding surfaces using surface triplet propensities. Bioinformatics 26(20):2549–2555

    Article  CAS  PubMed  Google Scholar 

  • Miao Z, Westhof E (2015) Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score. Nucleic Acids Res 43(11):5340–5351

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Morgan DH, Kristensen DM, Mittelman D et al (2006) ET viewer: an application for predicting and visualizing functional sites in protein structures. Bioinformatics 22(16):2049–2050

    Article  CAS  PubMed  Google Scholar 

  • Nei M, Gojobori T (1986) Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Mol Biol Evol 3(5):418–426

    CAS  PubMed  Google Scholar 

  • Nemoto W, Saito A, Oikawa H (2013) Recent advances in functional region prediction by using structural and evolutionary information—remaining problems and future extensions. Comput Struct Biotechnol J 8:e201308007

    Article  PubMed  PubMed Central  Google Scholar 

  • Ngan CH, Bohnuud T, Mottarella SE et al (2012) FTMAP: extended protein mapping with user-selected probe molecules. Nucleic Acids Res 40(Web Server issue):W271–W275

    Google Scholar 

  • Nimrod G, Szilagyi A, Leslie C et al (2009) Identification of DNA-binding proteins using structural, electrostatic and evolutionary features. J Mol Biol 387(4):1040–1053

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Novac N (2013) Challenges and opportunities of drug repositioning. Trends Pharmacol Sci 34(5):267–272

    Article  CAS  PubMed  Google Scholar 

  • Ohlendorf DH, Matthew JB (1985) Electrostatics and flexibility in protein-DNA interactions. Adv Biophys 20:137–151

    Article  CAS  PubMed  Google Scholar 

  • Oliveira SH, Ferraz FA, Honorato RV et al (2014) KVFinder: steered identification of protein cavities as a PyMOL plugin. BMC Bioinformatics 15:197-2105-15-197

    Google Scholar 

  • Ondrechen MJ, Clifton JG, Ringe D (2001) THEMATICS: a simple computational predictor of enzyme function from structure. Proc Natl Acad Sci U S A 98(22):12473–12478

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pandya MJ, Sessions RB, Williams PB et al (2000) Structural characterization of a methionine-rich, emulsifying protein from sunflower seed. Proteins 38(3):341–349

    Article  CAS  PubMed  Google Scholar 

  • Paz I, Kligun E, Bengad B et al (2016) BindUP: a web server for non-homology-based prediction of DNA and RNA binding proteins. Nucleic Acids Res 44(W1):W568–W574

    Article  PubMed  PubMed Central  Google Scholar 

  • Pellegrini-Calace M, Maiwald T, Thornton JM (2009) PoreWalker: a novel tool for the identification and characterization of channels in transmembrane proteins from their three-dimensional structure. PLoS Comput Biol 5(7):e1000440

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera–a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612

    Article  CAS  PubMed  Google Scholar 

  • Pettit FK, Bare E, Tsai A et al (2007) HotPatch: a statistical approach to finding biologically relevant features on protein surfaces. J Mol Biol 369(3):863–879

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pravda L, Berka K, Svobodova Va Ekova R et al (2014) Anatomy of enzyme channels. BMC Bioinformatics 15(1):379

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Puton T, Kozlowski L, Tuszynska I et al (2012) Computational methods for prediction of protein-RNA interactions. J Struct Biol 179(3):261–268

    Article  CAS  PubMed  Google Scholar 

  • Qin S, Zhou HX (2007) meta-PPISP: a meta web server for protein-protein interaction site prediction. Bioinformatics 23(24):3386–3387

    Article  CAS  PubMed  Google Scholar 

  • Ren J, Xie L, Li WW et al (2010) SMAP-WS: a parallel web service for structural proteome-wide ligand-binding site comparison. Nucleic Acids Res 38(Web Server issue):W441–W444

    Google Scholar 

  • Richter S, Wenzel A, Stein M et al (2008) webPIPSA: a web server for the comparison of protein interaction properties. Nucleic Acids Res 36(Web Server issue):W276–W280

    Google Scholar 

  • Rocchia W, Sridharan S, Nicholls A et al (2002) Rapid grid-based construction of the molecular surface and the use of induced surface charge to calculate reaction field energies: applications to the molecular systems and geometric objects. J Comput Chem 23(1):128–137

    Article  CAS  PubMed  Google Scholar 

  • Roy A, Yang J, Zhang Y (2012) COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res 40(Web Server issue):W471–W477

    Google Scholar 

  • Sacquin-Mora S, Laforet E, Lavery R (2007) Locating the active sites of enzymes using mechanical properties. Proteins 67(2):350–359

    Article  CAS  PubMed  Google Scholar 

  • Sael L, Kihara D (2012) Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison. Proteins 80(4):1177–1195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sammond DW, Yarbrough JM, Mansfield E et al (2014) Predicting enzyme adsorption to lignin films by calculating enzyme surface hydrophobicity. J Biol Chem 289(30):20960–20969

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sankararaman S, Sjolander K (2008) INTREPID–INformation-theoretic TREe traversal for protein functional site IDentification. Bioinformatics 24(21):2445–2452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sankararaman S, Kolaczkowski B, Sjolander K (2009) INTREPID: a web server for prediction of functionally important residues by evolutionary analysis. Nucleic Acids Res 37(Web Server issue):W390–W395

    Google Scholar 

  • Sankararaman S, Sha F, Kirsch JF et al (2010) Active site prediction using evolutionary and structural information. Bioinformatics 26(5):617–624

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Schmidtke P, Barril X (2010) Understanding and predicting druggability. A high-throughput method for detection of drug binding sites. J Med Chem 53(15):5858–5867

    Article  CAS  PubMed  Google Scholar 

  • Schmidtke P, Le Guilloux V, Maupetit J et al (2010) Fpocket: online tools for protein ensemble pocket detection and tracking. Nucleic Acids Res 38(Web Server issue):W582–W589

    Google Scholar 

  • Schneider S, Zacharias M (2012) Combining geometric pocket detection and desolvation properties to detect putative ligand binding sites on proteins. J Struct Biol 180(3):546–550

    Article  CAS  PubMed  Google Scholar 

  • Segura J, Jones PF, Fernandez-Fuentes N (2011) Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams. BMC Bioinformatics 12:352-2105-12-352

    Google Scholar 

  • Sehnal D, Svobodova Varekova R, Berka K et al (2013) MOLE 2.0: advanced approach for analysis of biomacromolecular channels. J Cheminform 5(1):39-2946-5-39

    Google Scholar 

  • Shazman S, Celniker G, Haber O et al (2007) Patch Finder Plus (PFplus): a web server for extracting and displaying positive electrostatic patches on protein surfaces. Nucleic Acids Res 35(Web Server issue):W526–W530

    Google Scholar 

  • Siragusa L, Cross S, Baroni M et al (2015) BioGPS: navigating biological space to predict polypharmacology, off-targeting, and selectivity. Proteins

    Google Scholar 

  • Skolnick J, Zhou H, Gao M (2013) Are predicted protein structures of any value for binding site prediction and virtual ligand screening? Curr Opin Struct Biol 23(2):191–197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Somarowthu S, Yang H, Hildebrand DG et al (2011) High-performance prediction of functional residues in proteins with machine learning and computed input features. Biopolymers 95(6):390–400

    Article  CAS  PubMed  Google Scholar 

  • Sun M, Wang X, Zou C et al (2016) Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors. BMC Bioinformatics 17(1):231-016-1110-x

    Google Scholar 

  • Suzuki Y (2004) Three-dimensional window analysis for detecting positive selection at structural regions of proteins. Mol Biol Evol 21(12):2352–2359

    Article  CAS  PubMed  Google Scholar 

  • Tan KP, Nguyen TB, Patel S et al (2013) Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins. Nucleic Acids Res 41(Web Server issue):W314–W321

    Google Scholar 

  • Taroni C, Jones S, Thornton JM (2000) Analysis and prediction of carbohydrate binding sites. Protein Eng 13(2):89–98

    Article  CAS  PubMed  Google Scholar 

  • Tian B, Wallrapp F, Kalyanaraman C et al (2013) Predicting enzyme-substrate specificity with QM/MM methods: a case study of the stereospecificity of (D)-glucarate dehydratase. Biochemistry 52(33):5511–5513

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tian BX, Wallrapp FH, Holiday GL et al (2014) Predicting the functions and specificity of triterpenoid synthases: a mechanism-based multi-intermediate docking approach. PLoS Comput Biol 10(10):e1003874

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Tjong H, Zhou HX (2007) DISPLAR: an accurate method for predicting DNA-binding sites on protein surfaces. Nucleic Acids Res 35(5):1465–1477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tsai KC, Jian JW, Yang EW et al (2012) Prediction of carbohydrate binding sites on protein surfaces with 3-dimensional probability density distributions of interacting atoms. PLoS ONE 7(7):e40846

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Volkamer A, Kuhn D, Grombacher T et al (2012) Combining global and local measures for structure-based druggability predictions. J Chem Inf Model 52(2):360–372

    Article  CAS  PubMed  Google Scholar 

  • Vulpetti A, Kalliokoski T, Milletti F (2012) Chemogenomics in drug discovery: computational methods based on the comparison of binding sites. Future Med Chem 4(15):1971–1979

    Article  CAS  PubMed  Google Scholar 

  • Walsh I, Minervini G, Corazza A et al (2012) Bluues server: electrostatic properties of wild-type and mutated protein structures. Bioinformatics 28(16):2189–2190

    Article  CAS  PubMed  Google Scholar 

  • Ward RM, Venner E, Daines B et al (2009) Evolutionary trace annotation server: automated enzyme function prediction in protein structures using 3D templates. Bioinformatics 25(11):1426–1427

    Article  PubMed  CAS  Google Scholar 

  • Warwicker J (1986) Continuum dielectric modelling of the protein-solvent system, and calculation of the long-range electrostatic field of the enzyme phosphoglycerate mutase. J Theor Biol 121(2):199–210

    Article  CAS  PubMed  Google Scholar 

  • Wilkins A, Erdin S, Lua R et al (2012) Evolutionary trace for prediction and redesign of protein functional sites. Methods Mol Biol 819:29–42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xie ZR, Hwang MJ (2012) Ligand-binding site prediction using ligand-interacting and binding site-enriched protein triangles. Bioinformatics 28(12):1579–1585

    Article  CAS  PubMed  Google Scholar 

  • Xie L, Xie L, Bourne PE (2009) A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics 25(12):i305–i312

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xie ZR, Liu CK, Hsiao FC et al (2013) LISE: a server using ligand-interacting and site-enriched protein triangles for prediction of ligand-binding sites. Nucleic Acids Res 41(Web Server issue):W292–W296

    Google Scholar 

  • Yaffe E, Fishelovitch D, Wolfson HJ et al (2008) MolAxis: a server for identification of channels in macromolecules. Nucleic Acids Res 36(Web Server issue):W210–W215

    Google Scholar 

  • Yang J, Roy A, Zhang Y (2013) Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics 29(20):2588–2595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yuan Z, Zhao J, Wang ZX (2003) Flexibility analysis of enzyme active sites by crystallographic temperature factors. Protein Eng 16(2):109–114

    Article  CAS  PubMed  Google Scholar 

  • Zhang Z, Tang Y-, Sheng Z- et al (2009) An overview of the de novo prediction of enzyme catalytic residues. Curr Bioinformatics 4:197–206

    Article  CAS  Google Scholar 

  • Zhang Z, Li Y, Lin B et al (2011) Identification of cavities on protein surface using multiple computational approaches for drug binding site prediction. Bioinformatics 27(15):2083–2088

    Article  CAS  PubMed  Google Scholar 

  • Zhao H, Yang Y, Zhou Y (2013) Prediction of RNA binding proteins comes of age from low resolution to high resolution. Mol BioSyst 9(10):2417–2425

    Article  CAS  PubMed  Google Scholar 

  • Zhou HX, Shan Y (2001) Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins 44(3):336–343

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Integrative Biology, University of Liverpool, Crown St., Liverpool, L69 7ZB, UK

    Daniel J. Rigden

Authors
  1. Daniel J. Rigden
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel J. Rigden .

Editor information

Editors and Affiliations

  1. Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom

    Daniel J. Rigden

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Rigden, D.J. (2017). Function Prediction Using Patches, Pockets and Other Surface Properties. In: J. Rigden, D. (eds) From Protein Structure to Function with Bioinformatics. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1069-3_10

Download citation

Publish with us

Policies and ethics

Search

Navigation