Skip to main content

The Framework of Computational Protein Design

  • Protocol
  • First Online:
Computational Protein Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1529))

Abstract

Computational protein design (CPD) has established itself as a leading field in basic and applied science with a strong coupling between the two. Proteins are computationally designed from the level of amino acids to the level of a functional protein complex. Design targets range from increased thermo- (or other) stability to specific requested reactions such as protein–protein binding, enzymatic reactions, or nanotechnology applications. The design scheme may encompass small regions of the proteins or the entire protein. In either case, the design may aim at the side-chains or at the full backbone conformation. Herein, the main framework for the process is outlined highlighting key elements in the CPD iterative cycle. These include the very definition of CPD, the diverse goals of CPD, components of the CPD protocol, methods for searching sequence and structure space, scoring functions, and augmenting the CPD with other optimization tools. Taken together, this chapter aims to introduce the framework of CPD.

“Most people make the mistake of thinking design is what it looks like. People think it’s this veneer—that the designers are handed this box and told, ‘Make it look good!’ That’s not what we think design is. It’s not just what it looks like and feels like. Design is how it works.” Steve Jobs, Apple’s C.E.O in an interview to the New-York Times. Nov. 30th 2003, The Guts of a New Machine

http://www.nytimes.com/2003/11/30/magazine/the-guts-of-a-new-machine.html

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
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

  1. Samish I, MacDermaid CM, Perez-Aguilar JM, Saven JG (2011) Theoretical and computational protein design. Annu Rev Phys Chem 62:129–149. doi:10.1146/annurev-physchem-032210-103509

    Article  CAS  PubMed  Google Scholar 

  2. Pantazes RJ, Grisewood MJ, Maranas CD (2011) Recent advances in computational protein design. Curr Opin Struct Biol 21(4):467–472. doi:10.1016/j.sbi.2011.04.005

    Article  CAS  PubMed  Google Scholar 

  3. Saven JG (2011) Computational protein design: engineering molecular diversity, nonnatural enzymes, nonbiological cofactor complexes, and membrane proteins. Curr Opin Chem Biol 15(3):452–457. doi:10.1016/j.cbpa.2011.03.014

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Tian P (2010) Computational protein design, from single domain soluble proteins to membrane proteins. Chem Soc Rev 39(6):2071–2082. doi:10.1039/b810924a

    Article  CAS  PubMed  Google Scholar 

  5. Suarez M, Jaramillo A (2009) Challenges in the computational design of proteins. J R Soc Interface 6 Suppl 4:S477–S491. doi:10.1098/rsif.2008.0508.focus

  6. Lippow SM, Tidor B (2007) Progress in computational protein design. Curr Opin Biotechnol 18(4):305–311. doi:10.1016/j.copbio.2007.04.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Rosenberg M, Goldblum A (2006) Computational protein design: a novel path to future protein drugs. Curr Pharm Des 12(31):3973–3997

    Article  CAS  PubMed  Google Scholar 

  8. Butterfoss GL, Kuhlman B (2006) Computer-based design of novel protein structures. Annu Rev Biophys Biomol Struct 35:49–65. doi:10.1146/annurev.biophys.35.040405.102046

    Article  CAS  PubMed  Google Scholar 

  9. Johnson LB, Huber TR, Snow CD (2014) Methods for library-scale computational protein design. Methods Mol Biol 1216:129–159. doi:10.1007/978-1-4939-1486-9_7

    Article  CAS  PubMed  Google Scholar 

  10. Davey JA, Chica RA (2012) Multistate approaches in computational protein design. Protein Sci 21(9):1241–1252. doi:10.1002/pro.2128

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lassila JK (2010) Conformational diversity and computational enzyme design. Curr Opin Chem Biol 14(5):676–682. doi:10.1016/j.cbpa.2010.08.010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Mandell DJ, Kortemme T (2009) Backbone flexibility in computational protein design. Curr Opin Biotechnol 20(4):420–428. doi:10.1016/j.copbio.2009.07.006

    Article  CAS  PubMed  Google Scholar 

  13. Ollikainen N, Smith CA, Fraser JS, Kortemme T (2013) Flexible backbone sampling methods to model and design protein alternative conformations. Methods Enzymol 523:61–85. doi:10.1016/B978-0-12-394292-0.00004-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Vizcarra CL, Mayo SL (2005) Electrostatics in computational protein design. Curr Opin Chem Biol 9(6):622–626. doi:10.1016/j.cbpa.2005.10.014

    Article  CAS  PubMed  Google Scholar 

  15. Verschueren E, Vanhee P, van der Sloot AM, Serrano L, Rousseau F, Schymkowitz J (2011) Protein design with fragment databases. Curr Opin Struct Biol 21(4):452–459. doi:10.1016/j.sbi.2011.05.002

    Article  CAS  PubMed  Google Scholar 

  16. Saven JG (2001) Designing protein energy landscapes. Chem Rev 101(10):3113–3130

    Article  CAS  PubMed  Google Scholar 

  17. Kuhlman B, Baker D (2004) Exploring folding free energy landscapes using computational protein design. Curr Opin Struct Biol 14(1):89–95. doi:10.1016/j.sbi.2004.01.002

    Article  CAS  PubMed  Google Scholar 

  18. Hwang I, Park S (2008) Computational design of protein therapeutics. Drug Discov Today Technol 5(2-3):e43–e48. doi:10.1016/j.ddtec.2008.11.004

    Article  PubMed  Google Scholar 

  19. Feldmeier K, Hocker B (2013) Computational protein design of ligand binding and catalysis. Curr Opin Chem Biol 17(6):929–933

    Article  CAS  PubMed  Google Scholar 

  20. Wijma HJ, Janssen DB (2013) Computational design gains momentum in enzyme catalysis engineering. FEBS J 280(13):2948–2960. doi:10.1111/febs.12324

    Article  CAS  PubMed  Google Scholar 

  21. Khare SD, Fleishman SJ (2013) Emerging themes in the computational design of novel enzymes and protein-protein interfaces. FEBS Lett 587(8):1147–1154. doi:10.1016/j.febslet.2012.12.009

    Article  CAS  PubMed  Google Scholar 

  22. Nanda V, Koder RL (2010) Designing artificial enzymes by intuition and computation. Nat Chem 2(1):15–24. doi:10.1038/nchem.473

    Article  CAS  PubMed  Google Scholar 

  23. Havranek JJ (2010) Specificity in computational protein design. J Biol Chem 285(41):31095–31099. doi:10.1074/jbc.R110.157685

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sharabi O, Erijman A, Shifman JM (2013) Computational methods for controlling binding specificity. Methods Enzymol 523:41–59. doi:10.1016/B978-0-12-394292-0.00003-5

    Article  CAS  PubMed  Google Scholar 

  25. Senes A (2011) Computational design of membrane proteins. Curr Opin Struct Biol 21(4):460–466. doi:10.1016/j.sbi.2011.06.004

    Article  CAS  PubMed  Google Scholar 

  26. Perez-Aguilar JM, Saven JG (2012) Computational design of membrane proteins. Structure 20(1):5–14. doi:10.1016/j.str.2011.12.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Parmar AS, Pike D, Nanda V (2014) Computational design of metalloproteins. Methods Mol Biol 1216:233–249. doi:10.1007/978-1-4939-1486-9_12

    Article  CAS  PubMed  Google Scholar 

  28. Nanda V, Zahid S, Xu F, Levine D (2011) Computational design of intermolecular stability and specificity in protein self-assembly. Methods Enzymol 487:575–593. doi:10.1016/B978-0-12-381270-4.00020-2

    Article  CAS  PubMed  Google Scholar 

  29. Ambroggio XI, Kuhlman B (2006) Design of protein conformational switches. Curr Opin Struct Biol 16(4):525–530. doi:10.1016/j.sbi.2006.05.014

    Article  CAS  PubMed  Google Scholar 

  30. Kortemme T, Baker D (2004) Computational design of protein-protein interactions. Curr Opin Chem Biol 8(1):91–97. doi:10.1016/j.cbpa.2003.12.008

    Article  CAS  PubMed  Google Scholar 

  31. Joyce GF (2007) Forty years of in vitro evolution. Angew Chem Int Ed Engl 46(34):6420–6436. doi:10.1002/anie.200701369

    Article  CAS  PubMed  Google Scholar 

  32. Fleishman SJ, Whitehead TA, Ekiert DC, Dreyfus C, Corn JE, Strauch EM, Wilson IA, Baker D (2011) Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science 332(6031):816–821. doi:10.1126/science.1202617

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Shlyk-Kerner O, Samish I, Kaftan D, Holland N, Sai PS, Kless H, Scherz A (2006) Protein flexibility acclimatizes photosynthetic energy conversion to the ambient temperature. Nature 442(7104):827–830. doi:10.1038/nature04947

    Article  CAS  PubMed  Google Scholar 

  34. Lane MD, Seelig B (2014) Advances in the directed evolution of proteins. Curr Opin Chem Biol 22:129–136. doi:10.1016/j.cbpa.2014.09.013

    Article  CAS  PubMed  Google Scholar 

  35. Packer MS, Liu DR (2015) Methods for the directed evolution of proteins. Nat Rev Genet 16(7):379–394. doi:10.1038/nrg3927

    Article  CAS  PubMed  Google Scholar 

  36. Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A (2014) Critical assessment of methods of protein structure prediction (CASP)—round x. Proteins 82(Suppl 2):1–6. doi:10.1002/prot.24452

    Article  CAS  PubMed  Google Scholar 

  37. Yue K, Dill KA (1992) Inverse protein folding problem: designing polymer sequences. Proc Natl Acad Sci U S A 89(9):4163–4167

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Whitehead TA, Baker D, Fleishman SJ (2013) Computational design of novel protein binders and experimental affinity maturation. Methods Enzymol 523:1–19. doi:10.1016/B978-0-12-394292-0.00001-1

    Article  CAS  PubMed  Google Scholar 

  39. Rothlisberger D, Khersonsky O, Wollacott AM, Jiang L, DeChancie J, Betker J, Gallaher JL, Althoff EA, Zanghellini A, Dym O, Albeck S, Houk KN, Tawfik DS, Baker D (2008) Kemp elimination catalysts by computational enzyme design. Nature 453(7192):190–195. doi:10.1038/nature06879

    Article  PubMed  Google Scholar 

  40. Tanford C (1978) The hydrophobic effect and the organization of living matter. Science 200(4345):1012–1018

    Article  CAS  PubMed  Google Scholar 

  41. Bowie JU, Luthy R, Eisenberg D (1991) A method to identify protein sequences that fold into a known three-dimensional structure. Science 253(5016):164–170

    Article  CAS  PubMed  Google Scholar 

  42. Godzik A, Kolinski A, Skolnick J (1992) Topology fingerprint approach to the inverse protein folding problem. J Mol Biol 227(1):227–238

    Article  CAS  PubMed  Google Scholar 

  43. Carbonell P, Trosset JY (2015) Computational protein design methods for synthetic biology. Methods Mol Biol 1244:3–21. doi:10.1007/978-1-4939-1878-2_1

    Article  CAS  PubMed  Google Scholar 

  44. Richter F, Baker D (2013) Computational protein design for synthetic biology. In: Zhao H (ed) Synthetic biology tools and applications. Elsevier Inc., San Diego, CA

    Google Scholar 

  45. Quinn TP, Tweedy NB, Williams RW, Richardson JS, Richardson DC (1994) Betadoublet: de novo design, synthesis, and characterization of a beta-sandwich protein. Proc Natl Acad Sci U S A 91(19):8747–8751

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kuhlman B, Dantas G, Ireton GC, Varani G, Stoddard BL, Baker D (2003) Design of a novel globular protein fold with atomic-level accuracy. Science 302(5649):1364–1368. doi:10.1126/science.1089427

    Article  CAS  PubMed  Google Scholar 

  47. Kaplan J, DeGrado WF (2004) De novo design of catalytic proteins. Proc Natl Acad Sci U S A 101(32):11566–11570. doi:10.1073/pnas.0404387101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Joh NH, Wang T, Bhate MP, Acharya R, Wu Y, Grabe M, Hong M, Grigoryan G, DeGrado WF (2014) De novo design of a transmembrane Zn(2)(+)-transporting four-helix bundle. Science 346(6216):1520–1524. doi:10.1126/science.1261172

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Huang PS, Love JJ, Mayo SL (2007) A de novo designed protein protein interface. Protein Sci 16(12):2770–2774. doi:10.1110/ps.073125207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Desjarlais JR, Handel TM (1995) De novo design of the hydrophobic cores of proteins. Protein Sci 4(10):2006–2018. doi:10.1002/pro.5560041006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ventura S, Vega MC, Lacroix E, Angrand I, Spagnolo L, Serrano L (2002) Conformational strain in the hydrophobic core and its implications for protein folding and design. Nat Struct Biol 9(6):485–493. doi:10.1038/nsb799

    Article  CAS  PubMed  Google Scholar 

  52. Keating AE, Malashkevich VN, Tidor B, Kim PS (2001) Side-chain repacking calculations for predicting structures and stabilities of heterodimeric coiled coils. Proc Natl Acad Sci U S A 98(26):14825–14830. doi:10.1073/pnas.261563398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Slovic AM, Kono H, Lear JD, Saven JG, DeGrado WF (2004) Computational design of water-soluble analogues of the potassium channel KcsA. Proc Natl Acad Sci U S A 101(7):1828–1833. doi:10.1073/pnas.0306417101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Slovic AM, Summa CM, Lear JD, DeGrado WF (2003) Computational design of a water-soluble analog of phospholamban. Protein Sci 12(2):337–348. doi:10.1110/ps.0226603

    Article  PubMed  PubMed Central  Google Scholar 

  55. Voet AR, Noguchi H, Addy C, Simoncini D, Terada D, Unzai S, Park SY, Zhang KY, Tame JR (2014) Computational design of a self-assembling symmetrical beta-propeller protein. Proc Natl Acad Sci U S A 111(42):15102–15107. doi:10.1073/pnas.1412768111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Woolfson DN, Bartlett GJ, Bruning M, Thomson AR (2012) New currency for old rope: from coiled-coil assemblies to alpha-helical barrels. Curr Opin Struct Biol 22(4):432–441. doi:10.1016/j.sbi.2012.03.002

    Article  CAS  PubMed  Google Scholar 

  57. Lanci CJ, MacDermaid CM, Kang SG, Acharya R, North B, Yang X, Qiu XJ, DeGrado WF, Saven JG (2012) Computational design of a protein crystal. Proc Natl Acad Sci U S A 109(19):7304–7309. doi:10.1073/pnas.1112595109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Swift J, Wehbi WA, Kelly BD, Stowell XF, Saven JG, Dmochowski IJ (2006) Design of functional ferritin-like proteins with hydrophobic cavities. J Am Chem Soc 128(20):6611–6619. doi:10.1021/ja057069x

    Article  CAS  PubMed  Google Scholar 

  59. Summa CM, Rosenblatt MM, Hong JK, Lear JD, DeGrado WF (2002) Computational de novo design, and characterization of an A(2)B(2) diiron protein. J Mol Biol 321(5):923–938

    Article  CAS  PubMed  Google Scholar 

  60. Cochran FV, Wu SP, Wang W, Nanda V, Saven JG, Therien MJ, DeGrado WF (2005) Computational de novo design and characterization of a four-helix bundle protein that selectively binds a nonbiological cofactor. J Am Chem Soc 127(5):1346–1347. doi:10.1021/ja044129a

    Article  CAS  PubMed  Google Scholar 

  61. Shifman JM, Mayo SL (2003) Exploring the origins of binding specificity through the computational redesign of calmodulin. Proc Natl Acad Sci U S A 100(23):13274–13279. doi:10.1073/pnas.2234277100

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kortemme T, Joachimiak LA, Bullock AN, Schuler AD, Stoddard BL, Baker D (2004) Computational redesign of protein-protein interaction specificity. Nat Struct Mol Biol 11(4):371–379. doi:10.1038/nsmb749

    Article  CAS  PubMed  Google Scholar 

  63. Potapov V, Reichmann D, Abramovich R, Filchtinski D, Zohar N, Ben Halevy D, Edelman M, Sobolev V, Schreiber G (2008) Computational redesign of a protein-protein interface for high affinity and binding specificity using modular architecture and naturally occurring template fragments. J Mol Biol 384(1):109–119. doi:10.1016/j.jmb.2008.08.078

    Article  CAS  PubMed  Google Scholar 

  64. Lippow SM, Wittrup KD, Tidor B (2007) Computational design of antibody-affinity improvement beyond in vivo maturation. Nat Biotechnol 25(10):1171–1176. doi:10.1038/nbt1336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Dagliyan O, Shirvanyants D, Karginov AV, Ding F, Fee L, Chandrasekaran SN, Freisinger CM, Smolen GA, Huttenlocher A, Hahn KM, Dokholyan NV (2013) Rational design of a ligand-controlled protein conformational switch. Proc Natl Acad Sci U S A 110(17):6800–6804. doi:10.1073/pnas.1218319110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Korendovych IV, Kulp DW, Wu Y, Cheng H, Roder H, DeGrado WF (2011) Design of a switchable eliminase. Proc Natl Acad Sci U S A 108(17):6823–6827. doi:10.1073/pnas.1018191108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Yin H, Slusky JS, Berger BW, Walters RS, Vilaire G, Litvinov RI, Lear JD, Caputo GA, Bennett JS, DeGrado WF (2007) Computational design of peptides that target transmembrane helices. Science 315(5820):1817–1822. doi:10.1126/science.1136782

    Article  CAS  PubMed  Google Scholar 

  68. Samish I (2009) Search and sampling in structural bioinformatics. In: Bourne P, Gu J (eds) Structural bioinformatics. Wiley, New York, pp 207–236

    Google Scholar 

  69. Dunbrack RL Jr, Karplus M (1993) Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J Mol Biol 230(2):543–574. doi:10.1006/jmbi.1993.1170

    Article  CAS  PubMed  Google Scholar 

  70. Dunbrack RL Jr (2002) Rotamer libraries in the 21st century. Curr Opin Struct Biol 12(4):431–440

    Article  CAS  PubMed  Google Scholar 

  71. Shapovalov MV, Dunbrack RL Jr (2011) A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure 19(6):844–858. doi:10.1016/j.str.2011.03.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Subramaniam S, Senes A (2014) Backbone dependency further improves side chain prediction efficiency in the Energy-based Conformer Library (bEBL). Proteins 82(11):3177–3187. doi:10.1002/prot.24685

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Kuhlman B, Baker D (2000) Native protein sequences are close to optimal for their structures. Proc Natl Acad Sci U S A 97(19):10383–10388

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Grigoryan G, Degrado WF (2011) Probing designability via a generalized model of helical bundle geometry. J Mol Biol 405(4):1079–1100. doi:10.1016/j.jmb.2010.08.058

    Article  CAS  PubMed  Google Scholar 

  75. Schramm CA, Hannigan BT, Donald JE, Keasar C, Saven JG, Degrado WF, Samish I (2012) Knowledge-based potential for positioning membrane-associated structures and assessing residue-specific energetic contributions. Structure 20(5):924–935. doi:10.1016/j.str.2012.03.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Xu F, Zahid S, Silva T, Nanda V (2011) Computational design of a collagen A:B:C-type heterotrimer. J Am Chem Soc 133(39):15260–15263. doi:10.1021/ja205597g

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Shifman JM, Mayo SL (2002) Modulating calmodulin binding specificity through computational protein design. J Mol Biol 323(3):417–423

    Article  CAS  PubMed  Google Scholar 

  78. Havranek JJ, Harbury PB (2003) Automated design of specificity in molecular recognition. Nat Struct Biol 10(1):45–52. doi:10.1038/nsb877

    Article  CAS  PubMed  Google Scholar 

  79. Bolon DN, Grant RA, Baker TA, Sauer RT (2005) Specificity versus stability in computational protein design. Proc Natl Acad Sci U S A 102(36):12724–12729. doi:10.1073/pnas.0506124102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Grigoryan G, Reinke AW, Keating AE (2009) Design of protein-interaction specificity gives selective bZIP-binding peptides. Nature 458(7240):859–864. doi:10.1038/nature07885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Fry HC, Lehmann A, Saven JG, DeGrado WF, Therien MJ (2010) Computational design and elaboration of a de novo heterotetrameric alpha-helical protein that selectively binds an emissive abiological (porphinato)zinc chromophore. J Am Chem Soc 132(11):3997–4005. doi:10.1021/ja907407m

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Koga N, Tatsumi-Koga R, Liu G, Xiao R, Acton TB, Montelione GT, Baker D (2012) Principles for designing ideal protein structures. Nature 491(7423):222–227. doi:10.1038/nature11600

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Fry HC, Lehmann A, Sinks LE, Asselberghs I, Tronin A, Krishnan V, Blasie JK, Clays K, DeGrado WF, Saven JG, Therien MJ (2013) Computational de novo design and characterization of a protein that selectively binds a highly hyperpolarizable abiological chromophore. J Am Chem Soc 135(37):13914–13926. doi:10.1021/ja4067404

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Jamroz M, Kolinski A (2010) Modeling of loops in proteins: a multi-method approach. BMC Struct Biol 10:5. doi:10.1186/1472-6807-10-5

    Article  PubMed  PubMed Central  Google Scholar 

  85. Hildebrand PW, Goede A, Bauer RA, Gruening B, Ismer J, Michalsky E, Preissner R (2009) SuperLooper—a prediction server for the modeling of loops in globular and membrane proteins. Nucleic Acids Res 37:W571–W574. doi:10.1093/nar/gkp338

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Soto CS, Fasnacht M, Zhu J, Forrest L, Honig B (2008) Loop modeling: sampling, filtering, and scoring. Proteins 70(3):834–843. doi:10.1002/prot.21612

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ilan Samish .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media New York

About this protocol

Cite this protocol

Samish, I. (2017). The Framework of Computational Protein Design. In: Samish, I. (eds) Computational Protein Design. Methods in Molecular Biology, vol 1529. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6637-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6637-0_1

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6635-6

  • Online ISBN: 978-1-4939-6637-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics