Computational peptidology: A new and promising approach to therapeutic peptide design

Current Medicinal Chemistry

Volumn 20, Issue 15, 2013, Pages 1985-1996

  Zhou, P ^a   Wang, C ^a   Ren, Y ^b   Yang, C ^a   Tian, F ^c  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b CHONGQING UNIVERSITY OF EDUCATION   (China)

^c SOUTHWEST JIAOTONG UNIVERSITY   (China)

Author keywords

Computational peptidology; Peptide therapeutics; Peptidic drug; Protein protein interaction

Indexed keywords

LIGAND; MEMBRANE PROTEIN; PEPTIDE;

ALGORITHM; ARTICLE; BIOINFORMATICS; COMPLEX FORMATION; ENTROPY; MOLECULAR DYNAMICS; PROTEIN BINDING; PROTEIN MOTIF; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; SIMULATION;

ANIMALS; COMPUTATIONAL BIOLOGY; DRUG DESIGN; HUMANS; MOLECULAR DOCKING SIMULATION; PEPTIDES; PROTEIN INTERACTION MAPS; PROTEINS;

EID: 84877997444     PISSN: 09298673     EISSN: 1875533X     Source Type: Journal    
DOI: 10.2174/0929867311320150005     Document Type: Article

References (113)

1. Hopkins, A.L.; Groom, C.R. The druggable genome. Nat. Rev., 2002, 1, 727-730. (Pubitemid 37361545)
2. Lipinski, C.; Lombardo, F.; Dominy, B.; Feeney, P. Experimental and computational approaches to estimate solubility and permeabil-ity in drug discovery and development settings. Adv. Drug Deliv. Rev., 1997, 23, 3-25. (Pubitemid 27046991)
3. Owens, J. Determining druggability. Nat. Rev. Drug Discov., 2007, 6, 187-187.
4. Hambly, K.; Danzer, J.; Muskal, S.; Debe, D.A. Interrogating the druggable genome with structural informatics. Mol. Divers., 2006, 10, 273-281. (Pubitemid 44546682)
5. Wanner. J.; Fry, D.C.; Peng, Z.; Roberts, J. Druggability assessment of protein-protein interfaces. Future Med. Chem., 2011, 3, 2021-2038.
6. Wells, J.; McClendon, C. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature, 2007, 450, 1001-1009. (Pubitemid 350273630)
7. Archakov, A.I.; Govorun, V.M.; Dubano,v A.V.; Ivanov, Y.D.; Veselovsky, A.V.; Lewi, P.; Janssen, P. Protein-protein interactions as a target for drugs in proteomics. Proteomics, 2003, 3, 380-391. (Pubitemid 36428291)
8. Fletcher, S.; Hamilton, A.D. Targeting protein-protein interactions by rational design: Mimicry of protein surfaces. J. R. Soc. Interface, 2006, 3, 215-233.
9. González-Ruiz, D.; Gohlke, H. Targeting protein-protein interactions with small molecules: Challenges and perspectives for computational binding epitope detection and ligand finding. Curr. Med. Chem., 2006, 13, 2607-2625. (Pubitemid 44364140)
10. Vanhee, P.; van der Sloot, A.M.; Verschueren, E.; Serrano, L.; Rousseau, F.; Schymkowitz, J. Computational design of peptide ligands. Trends Biotech., 2011, 29, 231-239.
11. Floris, M.; Moro, S. Mimicking peptides ⋯ in silico. Mol. Inf., 2012, 31, 12-20.
12. Rubinstein, M., Niv, M.Y. Peptidic modulators of protein-protein interactions: Progress and challenges in computational design. Biopolymers, 2009, 91, 505-513.
13. Koehler, A.N. A complex task? Direct modulation of transcription factors with small molecules. Curr. Opin. Chem. Biol., 2010, 14, 331-340.
14. Seo, P.J.; Hong, S.Y.; Kim, S.G.; Park, C.M. Competitive inhibition of transcription factors by small interfering peptides. Trends Plant Sci., 2011, 16, 541-549.
15. Aloy, P.; Russell, R.B. Ten thousand interactions for the molecular biologist. Nat. Biotech., 2004, 22, 1317-1321. (Pubitemid 39336787)
16. Petsalaki, E.; Russell, R.B. Peptide-mediated interactions in biological systems: New discoveries and applications. Curr. Opin. Biotech., 2008, 19, 344-350.
17. Kliger, Y. Computational approaches to therapeutic peptide discovery. Biopolymers (Pept. Sci.) 2010, 94, 701-710.
18. Agdeppa, E.D. Rational design for peptide drugs. J. Nucl. Med., 2004, 47, 22N-24N.
19. Bradbury, J. Rational design of peptide drugs: Avoiding aggregation. Drug Discov. Today, 2005, 10, 1208-1209. (Pubitemid 41411426)
20. Lucchese, G.; Stufano, A.; Trost, B.; Kusalik, A.; Kanduc, D. Peptidology: Short amino acid modules in cell biology and immunology. Amino Acids, 2007, 33, 703-707. (Pubitemid 350165425)
21. Lonsdale, R.; Ranaghan, K.E.; Mulholland, A.J. Computational enzymology. Chem. Commun., 2010, 46, 2354-2372.
22. Ren, Y.; Tian, F.; Zhou, P. Computational peptidology. Progress in Chemistry, 2012, 24, 1674-1682.
23. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28, 235-242. (Pubitemid 30047768)
24. Stein, A.; Panjkovich, A.; Aloy, P. 3did update: Domain-domain and peptide-mediated interactions of known 3D structure. Nucleic Acids Res., 2009, 37, D300-D304.
25. Vanhee, P.; Reumers, J.; Stricher, F.; Baeten, L.; Serrano, L.; Schymkowitz, J.; Rousseau, F. PepX: A structural database of nonredundant protein-peptide complexes. Nucleic Acids Res., 2010, 38, D545-D551.
26. Neduva, V.; Russell, R.B. Peptides mediating interaction networks: New leads at last. Curr. Opin. Biotech., 2006, 17, 465-471. (Pubitemid 44442924)
27. Li, S.S.C. Specificity and versatility of SH3 and other prolinerecognition domains: Structural basis and implications for cellular signal transduction. Biochem. J., 2005, 390, 641-653.
28. Lee, H.J.; Zheng, J.J. PDZ domains and their binding partners: Structure, specificity, and modification. Cell Commun. Signal., 2010, 8: 8.
29. Sattler, M.; Liang, H.; Nettesheim, D.; Meadows, R.P.; Harlan, J.E.; Eberstadt, M.; Yoon, H.S.; Shuker, S.B.; Chang, B.S.; Minn, A.J.; Thompson, C.B.; Fesik, S.W. Structure of Bcl-XL-Bak peptide complex: Recognition between regulators of apoptosis. Science, 1997, 275, 983-986 (Pubitemid 27087715)
30. Vanhee, P.; Stricher, F.; Baeten, L.; Verschueren, E.; Lenaerts, T.; Serrano, L.; Rousseau, F.; Schymkowitz, J. Protein-peptide interactions adopt the same structural motifs as monomeric protein folds. Structure, 2009, 17, 1128-1136.
31. London, N.; Movshovitz-Attias, D.; Schueler-Furman, O. The structural basis of peptide-protein binding strategies. Structure, 2010, 18, 188-199.
32. Stein, A.; Aloy, P. Novel peptide-mediated interactions derived from high-resolution 3-dimensional structures. PLoS Comput. Biol., 2010, 6, e1000789.
33. Hetényi, C.; van der Spoel, D. Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Sci., 2002, 11, 1729-1737. (Pubitemid 34663553)
34. Yagi, Y.; Terada, K.; Noma, T.; Ikebukuro, K.; Sode, K. In silico panning for a non-competitive peptide inhibitor. BMC Bioinformatics, 2007, 8, 11.
35. Bursulaya, B.D.; Totrov, M.; Abagyan, R.; Brooks, C.L. Comparative study of several algorithms for flexible ligand docking. J. Comput. Aided Mol. Des., 2003, 17, 755-763. (Pubitemid 38418287)
36. Kellenberger, E.; Rodrigo, J.; Muller, P.; Rognan, D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins, 2004, 57, 225-242. (Pubitemid 39223729)
37. Gulukota, K.; Vajda, S.; Delisi, C. Peptide docking using dynamic programming. J. Comp. Chem., 1996, 17, 418-428. (Pubitemid 126553237)
38. Desmet, J.; Wilson, I.A.; Joniau, M.; de Maeyer, M.; Lasters, I. Computation of the binding of fully flexible peptides to proteins with flexible side chains. FASEB J., 1997, 11, 164-172. (Pubitemid 27097590)
39. Hou, T.; Wang, J.; Chen, L.; Xu, X. Automated docking of peptides and proteins by using a genetic algorithm combined with a tabu search. Protein Eng., 1999, 12, 639-648.
40. Niv, M.Y.; 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. (Pubitemid 41437055)
41. Antes, I. DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility. Proteins, 2010, 78, 1084-1104.
42. King, C.A.; Bradley, P. Structure-based prediction of proteinpeptide specificity in Rosetta. Proteins, 2010, 78, 3437-3449.
43. Donsky, E.; Wolfson, H.J. PepCrawler: A fast RRT-based algorithm for high-resolution refinement and binding-affinity estimation of peptide inhibitors. Bioinformatics, 2011, 27, 2836-2842.
44. London, N.; Raveh, B.; Cohen, E.; Fathi, G.; Schueler-Furman, O. Rosetta FlexPepDock web server -high resolution modeling of peptide-protein interactions. Nucleic Acids Res., 2011, 39, W249-W253.
45. Raveh, B.; London, N.; Schueler-Furman, O. Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins, 2010, 78, 2029-2040.
46. Liu, Z.; Dominy, B.N.; Shakhnovich, E.I. Structural mining: Selfconsistent design on flexible protein-peptide docking and transferable binding affinity potential. J. Am. Chem. Soc., 2004, 126, 8515-8528. (Pubitemid 38917975)
47. Prasad P.A.; Gautham, N. A new peptide docking strategy using a mean field technique with mutually orthogonal Latin square sampling. J. Comput. Aided Mol. Des., 2008, 22, 815-829.
48. Khan, J.M.; Ranganathan, S. pDOCK: A new technique for rapid and accurate docking of peptide ligands to major histocompatibility complexes. Immun. Res., 2010, 6, S2
49. Aita, T.; Nishigaki, K.; Husimi, Y. Toward the fast blind docking of a peptide to a target protein by using a four-body statistical pseudo-potential. Comput. Biol. Chem., 2010, 34, 53-62.
50. Huang, Z.; Wong, C.F. Docking flexible peptide to flexible protein by molecular dynamics using two implicit-solvent models: An evaluation in protein kinase and phosphatase systems. J. Phys. Chem. B, 2009, 113, 14343-14354.
51. Petsalaki, E., Stark, A., García-Urdiales, E.; Russell, R.B. Accurate prediction of peptide binding sites on protein surfaces. PLoS Comput. Biol., 2009, 5, e1000335.
52. Voelz. V.A.; Shell, M.S.; Dill, K.A. Predicting peptide structures in native proteins from physical simulations of fragments. PLoS Comput. Biol., 2009, 5, e1000281.
53. Ahmad, M.; Gu, W.; Helms, V. Mechanism of fast peptide recognition by SH3 domains. Angew. Chem. Int. Ed., 2008, 47, 7626-7630.
54. Staneva, I.; Wallin, S. Binding free energy landscape of domainpeptide interactions. PLoS Comput. Biol., 2011, 7, e1002131.
55. Lama, D.; Sankararamakrishnan, R. Molecular dynamics simulations of pro-apoptotic BH3 peptide helices in aqueous medium: Relationship between helix stability and their binding affinities to the anti-apoptotic protein Bcl-XL. J. Comput. Aided Mol. Des., 2011, 25, 413-426.
56. Dagliyan, O.; Proctor, E.A.; D'Auria, K.M.; Ding, F.; Dokholyan, N.V. Structural and dynamic determinants of protein-peptide recognition. Structure, 2011, 19, 1837-1845.
57. Zhou, P.; Tian, F.; Wu, Y.; Li, Z.; Shang, Z. Quantitative sequenceactivity model (QSAM): Applying QSAR strategy to model and predict bioactivity and function of peptides, proteins and nucleic acids. Curr. Comput. Aided Drug Des., 2008, 4, 311-321.
58. Du, Q.S.; Huang, R.B.; Chou, K.C. Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design. Curr. Protein Pept. Sci., 2008, 9, 248-260.
59. Hellberg, S.; Sjöström, M.; Skagerberg, B.; Wold, S. Peptide quantitative structure-activity relationships, a multivariate approach. J. Med. Chem., 1987, 30, 1126-1135. (Pubitemid 17117970)
60. Collantes, E.R. ; Dunn, W.J. Amino acid side chain descriptors for quantitative structure-activity relationship studies of peptide analogues. J. Med. Chem., 1995, 38, 2705-2713.
61. Tian, F.; Yang, L.; Lv, F.; Yang, Q.; Zhou, P. In silico quantitative prediction of peptides binding affinity to human MHC molecule: An intuitive quantitative structure-activity relationship approach. Amino Acids, 2009, 36, 535-554.
62. Wold, S.; Jonsson, J.; Sjörström, M.; Sandberg, M.; Rännar, S. DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Anal. Chim. Acta, 1993, 277, 239-253. (Pubitemid 23158002)
63. Zhou, P.; Tian, F.; Lv, F.; Shang, Z. Comprehensive comparison of eight statistical modelling methods used in quantitative structureretention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome. J. Chromatogr. A, 2009, 1216, 3107-3116.
64. Lafuente, E.M.; Reche, P.A. Prediction of MHC-peptide binding: A systematic and comprehensive overview. Curr. Pharm. Des., 2009, 15, 3209-3220.
65. Madden, D.R. The three-dimensional structure of peptide-MHC complexes. Annu. Rev. Immunol., 1995, 13, 587-622.
66. Reimand, J.; Hui, S.; Jain, S.; Law, B.; Bader, G.D. Domainmediated protein interaction prediction: From genome to network. FEBS Lett., 2012, 586, 2751-2763.
67. Woo, H.J.; Roux, B. Calculation of absolute protein-ligand binding free energy from computer simulations. Proc. Natl. Acad. Sci. USA, 2005, 102, 6825-6830. (Pubitemid 40675400)
68. Hou, T.; McLaughlin, W.; Lu, B.; Chen, K.; Wang, W. Prediction of binding affinities between the human amphiphysin-1 SH3 domain and its peptide ligands using homology modeling, molecular dynamics and molecular field analysis. J. Proteome Res., 2006, 5, 32-43. (Pubitemid 43100795)
69. Hou, T.; Zhang, W.; Case, D.A.; Wang, W. Characterization of domain-peptide interaction interface: A case study on the amphiphysin-1 SH3 domain. J. Mol. Biol., 2008, 376, 1201-1214.
70. Hou, T.; Xu, Z.; Zhang, W.; McLaughlin, W.A.; Case, D.A.; Xu, Y.; Wang, W. Characterization of domain-peptide interaction interface: A generic structure-based model to decipher the binding specificity of SH3 domains. Mol. Cell. Proteomics, 2009, 8, 639-649.
71. Tian, F.; Yang, L.; Lv, F.; Luo, X.; Pan, Y. Why OppA protein can bind sequence-independent peptides? A combination of QM/MM, PB/SA, and structure-based QSAR analyses. Amino Acids, 2011, 40, 493-503.
72. Tian, F.; Lv, Y.; Zhou, P.; Yang, L. Characterization of PDZ domain-peptide interactions using an integrated protocol of QM/MM, PB/SA, and CFEA analyses. J. Comput. Aided Mol. Des., 2011, 25, 947-958.
73. Bogan, A.A.; Thorn, K.S. Anatomy of hot spots in protein interfaces. J. Mol. Biol., 1998, 280, 1-9. (Pubitemid 28312802)
74. Hershberger, S.J.; Lee, S.G.; Chmielewski, J. Scaffolds for blocking protein-protein interactions. Curr. Top. Med. Chem., 2007, 7, 928-942. (Pubitemid 47044745)
75. Eichler, J. Peptides as protein binding site mimetics. Curr. Opin. Chem. Biol., 2008, 12, 707-713.
76. Hwang, H.; Pierce, B.; Mintseris, J.; Janin, J.; Weng, Z. Proteinprotein docking benchmark version 3.0. Proteins, 2008, 73, 705-709. (Pubitemid 352788651)
77. London, N.; Raveh, B.; Movshovitz-Attias, D.; Schueler-Furman, O. Can self-inhibitory peptides be derived from the interfaces of globular protein-protein interactions? Proteins, 2010, 78, 3140-3149.
78. Jochim, A.L.; Arora, P.S. Assessment of helical interfaces in protein-protein interactions. Mol. BioSyst., 2009, 5, 924-926.
79. Jochim, A.L.; Arora, P.S. Systematic analysis of helical protein interfaces reveals targets for synthetic inhibitors. ACS Chem. Biol., 2010, 5, 919-923.
80. Stein, A.; Aloy, P. Contextual specificity in peptide-mediated protein interactions. PLoS ONE, 2008, 3, e2524.
81. Redell, M.S.; Tweardy, D.J. Targeting transcription factors in cancer: Challenges and evolving strategies. Drug Discov. Today, 2006, 3, 261-267. (Pubitemid 44739754)
82. Moellering, R.E.; Cornejo, M.; Davis, T.N.; del Bianco, C.; Aster, J.C.; Blacklow, S.C.; Kung, A.L.; Gilliland, D.G.; Verdine, G.L.; Bradner, J.E. Direct inhibition of the NOTCH transcription factor complex. Nature, 2009, 462, 182-188.
83. Eaton, B.E.; Gold, L.; Zichi, D.A. Let's get specific: The relationship between specificity and affinity. Chem. Biol., 1995, 2, 633-638.
84. Yan, Z.; Wang, J. Specificity quantification of biomolecular recognition and its implication for drug discovery. Sci. Rep., 2012, 2, 309.
85. Grigoryan, G.; Reinke, A.W.; Keating, A.E. Design of proteininteraction specificity gives selective bZIP-binding peptides. Nature, 2009, 458, 859-864.
86. Overington, J.P.; Al-Lazikani, B.; Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov., 2006, 5, 993-996. (Pubitemid 44835126)
87. White, S.H. The progress of membrane protein structure determination. Protein Sci., 2004, 13, 1948-1949. (Pubitemid 38822136)
88. Edwards, R.J.; Moran, N.; Devocelle, M.; Kiernan, A.; Meade, G.; Signac, W.; Foy, M.; Park, S.D.; Dunne, E.; Kenny, D.; Shields, D.C. Bioinformatic discovery of novel bioactive peptides. Nat. Chem. Biol., 2007, 3, 108-112. (Pubitemid 46134997)
89. Shemesh, R.; Toporik, A.; Levine, Z.; Hecht, I.; Rotman, G.; Wool, A.; Dahary, D.; Gofer, E.; Kliger, Y.; Soffer, M.A.; Rosenberg, A.; Eshel, D.; Cohen, Y. Discovery and validation of novel peptide agonists for G-protein-coupled receptors. J. Biol. Chem., 2008, 283, 34643-34649.
90. Yin, H.; Slusky, J.S.; Berger, B.W.; Walters, R.S.; Vilaire, G.; Litvinov, R.I.; Lear, J.D.; Caputo, G.A.; Bennett, J.S.; DeGrado, W.F. Computational design of peptides that target transmembrane helices. Science, 2007, 315, 1817-1822. (Pubitemid 46580135)
91. Tompa, P. Intrinsically unstructured proteins. Trends Biochem. Sci., 2002, 27, 527-533. (Pubitemid 35279598)
92. Dyson, H.J.; Wright, P.E. Intrinsically unstructured proteins and their functions. Nat. Rev. Mol. Cell Biol., 2005, 6, 197-208. (Pubitemid 40314921)
93. Neduva, V.; Russell, R.B Linear motifs: Evolutionary interaction switches. FEBS Lett., 2005, 579, 3342-3345. (Pubitemid 40804684)
94. Ye, Y.B.; Lin, J.Y.; Chen, Q.; Liu, F.; Chen, H.J.; Li, J.Y.; Liu, W.Q.; Garbay, C.; Vidal, M. The cytotoxicity of a Grb2-SH3 inhibitor in Bcr-Abl positive K562 cells. Biochem. Pharmacol., 2008, 75, 2080-2091.
95. Roberts, K.E.; Cushing, P.R.; Boisguerin, P.; Madden, D.R.; Donald, B.R. Computational design of a PDZ domain peptide inhibitor that rescues CFTR activity. PLoS Comput. Biol., 2012, 8, e1002477.
96. Cushing, P.R.; Vouilleme, L.; Pellegrini, M.; Boisguerin, P.; Madden, D.R. A Stabilizing influence: CAL PDZ inhibition extends the half-life of ?F508-CFTR. Angew Chem. Int. Ed. Engl., 2010, 49, 9907-9911.
97. Aasland, R.; Abrams, C.; Ampe, C.; Ball, L.J.; Bedford, M.T.; Cesareni, G.; Gimona, M.; Hurley, J.H.; Jarchau, T.; Lehto, V.P.; Lemmon, M.A.; Linding, R.; Mayer, B.J.; Nagai, M.; Sudol. M.; Walter, U.; Winder, S.J. rmalization of nomenclature for peptide motifs as ligands of modular protein domains. FEBS Lett., 2002, 513, 141-144. (Pubitemid 34242992)
98. Vagner, J.; Qu, H.; Hruby, V.J. Peptidomimetics, a synthetic tool of drug discovery. Curr. Opin. Chem. Biol., 2008, 12, 292-296.
99. Beeley, N.R.A. Can peptides be mimicked? Drug Discov. Today, 2000, 5, 354-363. (Pubitemid 30453866)
100. Kiran, M.D.; Adikesavan, N.V.; Cirioni, O.; Giacometti, A.; Silvestri, C.; Scalise, G.; Ghiselli, R.; Saba, V.; Orlando, F.; Shoham, M.; Balaban, N. Discovery of a quorum-sensing inhibitor of drugresistant staphylococcal infections by structure-based virtual screening. Mol. Pharmacol., 2008, 73, 1578-1586. (Pubitemid 351574847)
101. Parthasarathi, L.; Casey, F.; Stein, A.; Aloy, P.; Shields, D.C. Approved drug mimics of short peptide ligands from protein interaction motifs. J. Chem. Inf. Model., 2008, 48, 1943-1948.
102. Killian, B.J.; Kravitz, J.Y.; Somani, S.; Dasgupta, P.; Pang, Y.P.; Gilson, M.K. Configurational entropy in protein-peptide binding: Computational study of Tsg101 ubiquitin E2 variant domain with an HIV-derived PTAP nonapeptide. J. Mol. Biol., 2009 389, 315-335.
103. Ahmad, S.; Kono, H.; Araúzo-Bravo, M.J.; Sarai, A. ReadOut: Structure-based calculation of direct and indirect readout energies and specificities for protein-DNA recognition. Nucleic Acids Res., 2006, 34, W124-W127. (Pubitemid 44529748)
104. Doytchinova, I.A.; Flower, D.R. Toward the quantitative prediction of T-cell epitopes: CoMFA and CoMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201. J. Med. Chem., 2001, 44, 3572-3581. (Pubitemid 33026664)
105. Tian, F.; Zhou, P.; Lv, F.; Song, R.; Li, Z. Three-dimensional holograph vector of atomic interaction field (3D-HoVAIF): A novel rotation-translation invariant 3D structure descriptor and its applications to peptides. J. Pept. Sci., 2007, 13, 549-566. (Pubitemid 47322355)
106. Fung, H.K.; Welsh, W.J.; Floudas, C.A. Computational de novo peptide and protein design: Rigid templates versus flexible templates. Ind. Eng. Chem. Res., 2008, 47, 993-1001. (Pubitemid 351345883)
107. Meirovitch, H.; Meirovitch, E.; Lee, J. New theoretical methodology for elucidating the solution structure of peptides from NMR data. 1. The relative contribution of low-energy microstates to the partition function. J. Phys. Chem., 1995, 99, 4847-4854.
108. Spadaccini, R.; Temussi, P.A. Natural peptide analgesics: The role of solution conformation. Cell. Mol. Life Sci., 2001, 58, 1572-1582. (Pubitemid 32996094)
109. Kahvejian, A.; Quackenbush, J.; Thompson, J.F. What would you do if you could sequence everything? Nat. Biotech., 2008, 26, 1125-1133.
110. Jorgensen, R.A. We're all computational biologists now⋯next stop, the global brain? Front. Genet., 2011, 2, 68.
111. Sato, A.K.; Viswanathan, M.; Kent, R.B.; Wood, C.R. Therapeutic peptides: Technological advances driving peptides into development. Curr. Opin. Biotech., 2006, 17, 638-642. (Pubitemid 44791908)
112. Vlieghe, P.; Lisowski, V.; Martinez, J.; Khrestchatisky, M. Synthetic therapeutic peptides: Science and market. Drug Discov. Today, 2010, 15, 40-56.
113. Mason, J.M. Design and development of peptides and peptide mimetics as antagonists for therapeutic intervention. Future Med. Chem., 2010, 2, 1813-1822.