Tools for predicting metal binding sites in protein: A Review

Current Bioinformatics

Volumn 6, Issue 4, 2011, Pages 444-449

  Mallick, Medhavi ^a   Vidyarthi, Ambarish Sharan ^a   Shankaracharya, ^a  

^a BIRLA INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Computational method; Metal binding site; Metalloprotein; Motif; Pdb; Stand alone tools

Indexed keywords

BINDING SITES; COMPUTATIONAL METHODS; FORECASTING; METAL IONS;

BINDING-SITES; FUNCTIONAL FEATURES; METAL-BINDING SITES; METALLO-PROTEINS; METALS IONS; MOTIF; PDB; PROTEIN A; RAPID GROWTH; STAND ALONE TOOLS;

PROTEINS;

CADMIUM; CALCIUM; COPPER; HEAVY METAL; IRON; MAGNESIUM; MANGANESE; MERCURY; METAL ION; NICKEL; POTASSIUM; PROTEIN; SODIUM; ZINC;

ALGORITHM; AMINO ACID COMPOSITION; ARTICLE; BINDING SITE; COMPUTER MODEL; METAL BINDING; METAL BINDING SITE; PRIORITY JOURNAL; PROTEIN DATA BANK;

EID: 81155151078     PISSN: 15748936     EISSN: None     Source Type: Journal    
DOI: 10.2174/157489311798072990     Document Type: Article

References (59)

1. Banci L, Bertini I, Calderone V, et al. M. S. A prokaryotic superoxide dismutase paralog lacking two Cu ligands: from largely unstructured in solution to ordered in the crystal. Proc Natl Acad Sci USA 2005; 102(21): 7541-7546.
2. (2+)- loaded porcine calbindin D9k determined by nuclear magnetic resonance spectroscopy. Biochemistry 1992; 31(4): 1011-1020.
3. Greenblatt HM, Feinberg H, Tucker PA, et al. Carboxypeptidase A: native, zincremoved and mercury-replaced forms. Acta Crystallogr D Biol Crystallogr 1998; 54 (3): 289-305.
4. Sun H, Li H, Sadler PJ. Transferrin as a metal ion mediator. Chem Rev 1999; 99(9): 2817-2842.
5. Passerini A, Punta M, Ceroni A, et al. Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks. Proteins 2006; 65: 305-316.
6. Eapen S, D'Souza SF. Prospects of genetic engineering of plants for phytoremediation of toxic metals. Biotechnology Advances 2005; 23: 97.
7. Sharma A, Roy S, Tripathi KP, et al. Predicted metal binding sites for hytoremediation. Bioinformatics 2009; 4(2): 66-70.
8. Babor M, Gerzon S, Rahev B, et al. Prediction of transition metal- binding sites from apo protein structures. Proteins 2008; 70: 208-217.
9. Lippi M, Passerini A, Punta M. Metal Detector: a web server for predicting metal-binding sites and disulfide bridges in proteins from sequence. Bioinformatics 2008; 24: 2094-5.
10. Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucl Acids Res 2000; 28, 235-240
11. Karlin S, Zhu ZY, Karlin KD. The extended environment of mononuclear metal centers in protein structures. Proc. Natl Acad. Sci 1997; 94: 14225-14230.
12. Gregory DS, Martin CR, Cheetham JC, et al. The prediction and characterization of metal binding sites in proteins. Protein Eng 1993; 6: 29-35.
13. Yamashita MM, Wesson L, Eisenman G, et al. Where metal ions bind in proteins. Biophysics 1990; 87: 5648-5652.
14. Kristensen DM, Ward RM, Lisewski AM, et al. Prediction of enzyme function based on 3D templates of evolutionarily important amino acides. BMC Bioinformatics 2008; 9: N17.
15. Rees DC, Lewis M, Lipscomb WN. Refined crystal structure of carboxypeptidase A at 1.54A resolution. J Mol Biol 1983; 168: 367-387.
16. Osterberg R. Metal Ions in Biological Systems. Dekker, New York 1974; 3: 45-88.
17. Sigrist CJA, Cerutti L, Hulo N, et al. PROSITE: a documented database using patterns and profiles as motif descriptors. Brief Bioinform 2002; 3: 265-274.
18. Castagnetto JM, Hennessy SW, Roberts VA, et al. MDB: the metalloprotein database and browser at the scripps research institute. Nucleic Acids Res 2002; 30: 379-382.
19. Molecular databases. http://www.personal.reading.ac.uk/~sas97sca/Mol%20Databases.htm (Accessed on January 15, 2011)
20. Golovin A, Dimitropoulos D, Oldfield T, et al. MSDsite: a database search and retrieval system for the analysis and viewing of bound ligands and active sites. Proteins 2005; 58: 190-199.
21. Andreini C, Bertini I, Rosato A. A hint to search for metalloproteins in gene banks. Bioinformatics 2004; 20: 1373-1380.
22. Lin CT, Lin KL, Yang CH, et al. Protein metal binding residue prediction based on neural networks. Int J Neural Syst 2005; 15: 71-84.
23. Expasy Proteomic server. http://www.expasy.ch/ (Accessed on January 15, 2011).
24. Microbial Genomics at the U.S. Department of Energy. http://microbialgenomics.energy.gov/databases.shtml (Accessed on January 22, 2011).
25. National center for Biotechnology Information. http://www.ncbi.nlm.nih.gov/ (Accessed on January 22, 2011).
26. Pfam 24.0. http://pfam.sanger.ac.uk/ (Accessed on January 22, 2011).
27. Prodoric Database. http://prodoric.tu-bs.de/ (Accessed on January 22, 2011).
28. Research collaborative for structural biology, Protein data bank. http: //www.rcsb.org/pdb/home/home.do (Accessed on January 22, 2011).
29. Sanger. http://www.sanger.ac.uk/ (Accessed on January 22, 2011).
30. Superfamily 1.75. HMM library and genome assignment server. http: //supfam.cs.bris.ac.uk/SUPERFAMILY/ (Accessed on January 22, 2011).
31. Kuntal BK, Aparoy P, Reddanna P. Development of tools and database for analysis of metal binding site in protein. Protein Pept Lett 2010; 17(6): 765-73.
32. Kensuke Nakamura, Aki Hirai, Md. Altaf-Ul-Amin, Hiroki Takahashi. MetalMine: a database of functional metal-binding sites in proteins. Plant Biotechnology 2009; 26: 517-521.
33. Babor M, Greenblatt HM, Edelman M, Sobolev V. Flexibility of metal binding sites in proteins on a database scale. Proteins 2005; 59: 221-230.
34. Chapelle O, Haffner P, Vapnik VN. Support vector machines for histogram-based image classification. IEEE Trans Neural Network 1999; 10: 1055-1064.
35. Niv M, Weinstein H. A flexible docking procedure for the exploration of peptide binding selectivity to known structures and homology models of PDZ domains. J Am Chem Soc 2005; 127: 14072-14079.
36. Korkin D, Davis FP, Alber F, et al. Structural modeling of protein interactions by analogy: application to PSD-95. PLoS Comput Biol 2006; 2: 1365-1376.
37. Betel D, Breitkreuz KE, Isserlin R et al. Structure-template prediction of novel protein interactions from sequence information. PLoS Comput Biol 2007; 3: 1783-1789.
38. Humphris EL, Kortemme T. Design of multi-specificity in protein interfaces. PLoS Comput Biol 2007; 3: 1591-1604.
39. Magesh S, Suzuki T, Miyagi T, et al. Homology modeling of human sialidase enzymes NEU1. NEU3 and NEU4 based on the crystal structure of NEU2: hints for the design of selective NEU3 inhibitors. J Mol Graph Model 2006; 25: 196-207.
40. Oshiro C, Bradley EK, Eksterowicz J, et al. Performance of 3Ddatebase molecular docking studies into homology models. J Med Chem 2004; 47: 764-767.
41. Park H, Hwang KY, Oh KH, et al. Discovery of novel a-glucosidase inhibitors based on the virtual screening with the homology- modeled protein structure. Bioorg Med Chem 2008; 16: 284-292.
42. Brylinski M, Skolnick J. A threading-based method (FINDSITE) for ligand-binding site prediction and function annotation. Proc Natl Acad Sci USA 2008; 105: 129-134.
43. Nicola G, Smith CA, Abagyan R. New method for the assessment of all drug-like pockets across a structural genome. J Comput Biol 2008; 15: 231-240.
44. Levy R, Edelman M, Sobolev V. Prediction of 3D metal binding sites from translated gene sequences based on remote-homology templates. Proteins 2008; 76, 365-374.
45. Sodhi JS, Bryson K, McGuffin LJ, Ward JJ, Wernisch L, Jones DT. Predicting metal-binding site residues in low-resolution structural models. J Mol Biol 2004;342: 307-320.
46. Taskar B, Chatalbashev V, Koller D, et al. Learning structured prediction models: a large margin approach. In Proceedings of ICML'05, 2005; 896-903.
47. Ceroni A, Passerini A, Vullo A, et al. Disulfind: a disulfide bonding state and cysteine connectivity prediction server. Nucleic Acids Res 2006; 34: 177-181.
48. Goyal K, Mohanty D, Mande SC. PAR-3D: a server to predict protein active site residues. Nucleic Acids Res 2007; 503-505.
49. Sodhi JS, Bryson K, McGuffin LJ, et al. Predicting metal-binding site residues in low-resolution structural models. J Mol Biol 2004; 342: 307-320.
50. Jones DT. GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol 1999; 287(4): 797-815.
51. Orengo CA, Michie AD, Jones S, et al. CATH: a hierarchic classification of protein domain structure. Structure 1997; 5,1093-1108.
52. Murzin AG, Brenner SE, Hubbard T, et al. SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 1995; 247: 536-540.
53. Holm L, Sander C. Mapping the protein universe. Science 1996; 273: 595-602.
54. Humphrey W, Dalke A, Schulten K. VMD - visual molecular dynamic. J Mol Graph 1996; 14: 33-38.
55. Thore S, Mauxion F, Seraphin B, et al. X-ray structure and activity of the yeast Pop2 protein: a nuclease subunit of the mRNA deadenylase complex. EMBO 2003; 12: 1150-1155.
56. Shu N, Zhou T, Hovmöller S. Prediction of zinc-binding sites in proteins from sequence. Bioinformatics 2008; 24: 775-82.
57. Goodford PJ. A computational procedure for determining energetically favorable binding sites on biologically important macromolecules. J Med Chem 1985; 28: 849-857.
58. Clarke ND, Yuan SM. Metal search: a computer program that helps design tetrahedral metal-binding sites. Proteins 1995, 23: 256-263.
59. Liang MP, Banatao DR, Klein TE, et al. WebFEATURE: An interactive web tool for identifying and visualizing functional sites on macromolecular structures. Nucleic Acids Res 2003, 31: 3324-3327.