[en] We extended the use of Peplook, an in silico procedure for the prediction of three-dimensional (3D) models of linear peptides to the prediction of 3D models of cyclic peptides and thanks to the ab initio calculation procedure, to the calculation of peptides with non-proteinogenic amino acids. Indeed, such peptides cannot be predicted by homology or threading. We compare the calculated models with NMR and X-ray models and for the cyclic peptides, with models predicted by other in silico procedures (Pep-Fold and I-Tasser). For cyclic peptides, on a set of 38 peptides, average root mean square deviation of backbone atoms (BB-RMSD) was 3.8 and 4.1 A for Peplook and Pep-Fold, respectively. The best results are obtained with I-Tasser (2.5 A) although evaluations were biased by the fact that the resolved Protein Data Bank models could be used as template by the server. Peplook and Pep-Fold give similar results, better for short (up to 20 residues) than for longer peptides. For peptides with non-proteinogenic residues, performances of Peplook are sound with an average BB-RMSD of 3.6 A for 'non-natural peptides' and 3.4 A for peptides combining non-proteinogenic residues and cyclic structure. These results open interesting possibilities for the design of peptidic drugs. Copyright (c) 2011 European Peptide Society and John Wiley & Sons, Ltd.
Disciplines :
Biochemistry, biophysics & molecular biology
Author, co-author :
Beaufays, Jérôme ✱; Université de Liège - ULiège > Chimie et bio-industries > Centre de Bio. Fond. - Section de Biophysique moléc. numér.
Lins, Laurence ✱; Université de Liège - ULiège > Chimie et bio-industries > Centre de Bio. Fond. - Section de Biophysique moléc. numér.
Thomas, Annick ; Université de Liège - ULiège > Chimie et bio-industries > Centre de Bio. Fond. - Section de Biophysique moléc. numér.
Brasseur, Robert ; Université de Liège - ULiège > Chimie et bio-industries > Centre de Bio. Fond. - Section de Biophysique moléc. numér.
✱ These authors have contributed equally to this work.
Language :
English
Title :
In silico predictions of 3D structures of linear and cyclic peptides with natural and non-proteinogenic residues.
Publication date :
2012
Journal title :
Journal of Peptide Science
ISSN :
1075-2617
eISSN :
1099-1387
Publisher :
John Wiley & Sons, Inc, Chichester, United Kingdom
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Sali A, Blundell TL. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 1993; 234: 779-815.
Sanchez R, Sali A. Advances in comparative protein-structure modelling. Curr. Opin. Struct. Biol. 1997; 7: 206-214.
Marti-Renom MA, Stuart AC, Fiser A, Sanchez R, Melo F, Sali A. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct. 2000; 29: 291-325.
Bowie JU, Luthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science 1991; 253: 164-170.
Jones DT, Taylor WR, Thornton JM. A new approach to protein fold recognition. Nature 1992; 358: 86-89.
Luthy R, McLachlan AD, Eisenberg D. Secondary structure-based profiles: use of structure-conserving scoring tables in searching protein sequence databases for structural similarities. Proteins 1991; 10: 229-239.
Bradley P, Misura KM, Baker D. Toward high-resolution de novo structure prediction for small proteins. Science 2005; 309: 1868-1871.
Monge A, Friesner RA, Honig B. An algorithm to generate low-resolution protein tertiary structures from knowledge of secondary structure. Proc. Natl Acad. Sci. U S A 1994; 91: 5027-5029.
Eyrich VA, Standley DM, Felts AK, Friesner RA. Protein tertiary structure prediction using a branch and bound algorithm. Proteins 1999; 35: 41-57.
Maupetit J, Derreumaux P, Tuffery P. PEP-FOLD: an online resource for de novo peptide structure prediction. Nucleic Acids Res. 2009; 37: W498-503.
Maupetit J, Derreumaux P, Tuffery P. A fast method for large-scale de novo peptide and miniprotein structure prediction. J. Comput. Chem. 2009; 31: 726-738.
Kaur H, Garg A, Raghava GP. PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides. Protein Pept. Lett. 2007; 14: 626-631.
Hung LH, Samudrala R. PROTINFO: secondary and tertiary protein structure prediction. Nucleic Acids Res. 2003; 31: 3296-3299.
Hung LH, Ngan SC, Liu T, Samudrala R. PROTINFO: new algorithms for enhanced protein structure predictions. Nucleic Acids Res. 2005; 33: W77-80.
Bystroff C, Thorsson V, Baker D. HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J. Mol. Biol. 2000; 301: 173-190.
Bystroff C, Shao Y. Fully automated ab initio protein structure prediction using I-SITES, HMMSTR and ROSETTA. Bioinformatics 2002; 18 Suppl. 1: S54-61.
Zhang Y, Skolnick J. Automated structure prediction of weakly homologous proteins on a genomic scale. Proc. Natl Acad. Sci. U S A 2004; 101: 7594-7599.
Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 2008; 9: 40.
Thomas A, Deshayes S, Decaffmeyer M, Van Eyck MH, Charloteaux B, Brasseur R. Prediction of peptide structure: how far are we? Proteins 2006; 65: 889-897.
Etchebest C, Benros C, Hazout S, de Brevern AG. A structural alphabet for local protein structures: improved prediction methods. Proteins 2005; 59: 810-827.
Lins L, Charloteaux B, Heinen C, Thomas A, Brasseur R. "De novo" design of peptides with specific lipid-binding properties. Biophys. J. 2006; 90: 470-479.
Wu S, Zhang Y. LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res. 2007; 35: 3375-3382.
Zhang Y, Skolnick J. SPICKER: a clustering approach to identify near-native protein folds. J. Comput. Chem. 2004; 25: 865-871.
Li Y, Zhang Y. REMO: a new protocol to refine full atomic protein models from C-alpha traces by optimizing hydrogen-bonding networks. Proteins 2009; 76: 665-676.
Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJ. GROMACS: fast, flexible, and free. J. Comput. Chem. 2005; 26: 1701-1718.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.