Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution



Directed evolution (DE) creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding, and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of the art of molecular modeling, discussing their strengths, limitations, and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques and a synergic effort between simulations and experimental validation.


Sequence Space Protein Stability Direct Evolution Reorganization Energy Free Energy Difference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Schmid A, Dordick JS, Hauer B, Kiener A, Wubbolts M, Witholt B (2001) Industrial biocatalysis today and tomorrow. Nature 409(6817):258–268PubMedCrossRefGoogle Scholar
  2. 2.
    Patel RN (2008) Synthesis of chiral pharmaceutical intermediates by biocatalysis. Coord Chem Rev 252(5–7):659–701CrossRefGoogle Scholar
  3. 3.
    Sukumaran J, Hanefeld U (2005) Enantioselective C-C bond synthesis catalysed by enzymes. Chem Soc Rev 34(6):530–542PubMedCrossRefGoogle Scholar
  4. 4.
    Koenig SH, Brown RD (1972) H(2)CO(3) as substrate for carbonic anhydrase in the dehydration of HCO(3)(−). Proc Natl Acad Sci U S A 69(9):2422–2425PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Bar-Even A, Noor E, Savir Y, Liebermeister W, Davidi D, Tawfik DS, Milo R (2011) The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50(21):4402–4410PubMedCrossRefGoogle Scholar
  6. 6.
    Milo R, Last RL (2012) Achieving diversity in the face of constraints: lessons from metabolism. Science 336(6089):1663–1667PubMedCrossRefGoogle Scholar
  7. 7.
    Currin A, Swainston N, Day PJ, Kell DB (2015) Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently. Chem Soc Rev 44(5):1172–1239PubMedCrossRefGoogle Scholar
  8. 8.
    Gutte B, Däumigen M, Wittschieber E (1979) Design, synthesis and characterisation of a 34-residue polypeptide that interacts with nucleic acids. Nature 281(5733):650–655PubMedCrossRefGoogle Scholar
  9. 9.
    Russell AJ, Fersht AR (1987) Rational modification of enzyme catalysis by engineering surface charge. Nature 328(6130):496–500PubMedCrossRefGoogle Scholar
  10. 10.
    Hellinga HW, Caradonna JP, Richards FM (1991) Construction of new ligand binding sites in proteins of known structure: II. Grafting of a buried transition metal binding site into Escherichia coli thioredoxin. J Mol Biol 222(3):787–803PubMedCrossRefGoogle Scholar
  11. 11.
    Jemli S, Ayadi-Zouari D, Hlima HB, Bejar S (2016) Biocatalysts: application and engineering for industrial purposes. Crit Rev Biotechnol 36(2):246–258PubMedCrossRefGoogle Scholar
  12. 12.
    Schueler-Furman O, Wang C, Bradley P, Misura K, Baker D (2005) Progress in modeling of protein structures and interactions. Science 310(5748):638–642PubMedCrossRefGoogle Scholar
  13. 13.
    Steiner K, Schwab H (2012) Recent advances in rational approaches for enzyme engineering. Comput Struct Biotechnol J 2:e201209010PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Richardson JS, Richardson DC (1989) The de novo design of protein structures. Trends Biochem Sci 14(7):304–309PubMedCrossRefGoogle Scholar
  15. 15.
    Ponder JW, Richards FM (1987) Tertiary templates for proteins: use of packing criteria in the enumeration of allowed sequences for different structural classes. J Mol Biol 193(4):775–791PubMedCrossRefGoogle Scholar
  16. 16.
    Bolon DN, Marcus JS, Ross SA, Mayo SL (2003) Prudent modeling of core polar residues in computational protein design. J Mol Biol 329(3):611–622PubMedCrossRefGoogle Scholar
  17. 17.
    Dahiyat BI, Mayo SL (1997) Probing the role of packing specificity in protein design. Proc Natl Acad Sci U S A 94(19):10172–10177PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Desjarlais JR, Handel TM (1999) Side-chain and backbone flexibility in protein core design1. J Mol Biol 290(1):305–318PubMedCrossRefGoogle Scholar
  19. 19.
    Scrutton NS, Berry A, Perham RN (1990) Redesign of the coenzyme specificity of a dehydrogenase by protein engineering. Nature 343(6253):38–43PubMedCrossRefGoogle Scholar
  20. 20.
    Carter P, Nilsson B, Burnier JP, Burdick D, Wells JA (1989) Engineering subtilisin BPN’ for site-specific proteolysis. Proteins Struct Funct Bioinforma 6(3):240–248CrossRefGoogle Scholar
  21. 21.
    Wells JA, Powers DB, Bott RR, Graycar TP, Estell DA (1987) Designing substrate specificity by protein engineering of electrostatic interactions. Proc Natl Acad Sci U S A 84(5):1219–1223PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Cedrone F, Ménez A, Quéméneur E (2000) Tailoring new enzyme functions by rational redesign. Curr Opin Struct Biol 10(4):405–410PubMedCrossRefGoogle Scholar
  23. 23.
    Looger LL, Dwyer MA, Smith JJ, Hellinga HW (2003) Computational design of receptor and sensor proteins with novel functions. Nature 423(6936):185–190PubMedCrossRefGoogle Scholar
  24. 24.
    Craik C, Largman C, Fletcher T, Roczniak S, Barr P, Fletterick R, Rutter W (1985) Redesigning trypsin: alteration of substrate specificity. Science 228(4697):291–297PubMedCrossRefGoogle Scholar
  25. 25.
    Bastianelli G, Bouillon A, Nguyen C, Crublet E, Pêtres S, Gorgette O, Le-Nguyen D, Barale J-C, Nilges M (2011) Computational reverse-engineering of a spider-venom derived peptide active against Plasmodium falciparum SUB1. PLoS ONE 6(7):e21812Google Scholar
  26. 26.
    Oelschlaeger P, Mayo SL (2005) Hydroxyl groups in the ββ sandwich of metallo-β-lactamases favor enzyme activity: a computational protein design study. J Mol Biol 350(3):395–401PubMedCrossRefGoogle Scholar
  27. 27.
    Guerois R, Nielsen JE, Serrano L (2002) Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J Mol Biol 320(2):369–387PubMedCrossRefGoogle Scholar
  28. 28.
    Yu H, Huang H (2014) Engineering proteins for thermostability through rigidifying flexible sites. Biotechnol Adv 32(2):308–315PubMedCrossRefGoogle Scholar
  29. 29.
    Kuhlman B, Baker D (2004) Exploring folding free energy landscapes using computational protein design. Curr Opin Struct Biol 14(1):89–95PubMedCrossRefGoogle Scholar
  30. 30.
    Kortemme T, Baker D (2004) Computational design of protein–protein interactions. Curr Opin Chem Biol 8(1):91–97PubMedCrossRefGoogle Scholar
  31. 31.
    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):371–379PubMedCrossRefGoogle Scholar
  32. 32.
    Reina J, Lacroix E, Hobson SD, Fernandez-Ballester G, Rybin V, Schwab MS, Serrano L, Gonzalez C (2002) Computer-aided design of a PDZ domain to recognize new target sequences. Nat Struct Mol Biol 9(8):621–627Google Scholar
  33. 33.
    Shifman JM, Mayo SL (2002) Modulating calmodulin binding specificity through computational protein design. J Mol Biol 323(3):417–423PubMedCrossRefGoogle Scholar
  34. 34.
    Lippow SM, Tidor B (2007) Progress in computational protein design. Curr Opin Biotechnol 18(4):305–311PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Ashworth J, Havranek JJ, Duarte CM, Sussman D, Monnat RJ, Stoddard BL, Baker D (2006) Computational redesign of endonuclease DNA binding and cleavage specificity. Nature 441(7093):656–659PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Chevalier BS, Kortemme T, Chadsey MS, Baker D, Monnat RJ Jr, Stoddard BL (2002) Design, activity, and structure of a highly specific artificial endonuclease. Mol Cell 10(4):895–905PubMedCrossRefGoogle Scholar
  37. 37.
    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):1346–1347PubMedCrossRefGoogle Scholar
  38. 38.
    Yang W, Wilkins AL, Ye Y, Z-r L, S-y L, Urbauer JL, Hellinga HW, Kearney A, van der Merwe PA, Yang JJ (2005) Design of a calcium-binding protein with desired structure in a cell adhesion molecule. J Am Chem Soc 127(7):2085–2093PubMedCrossRefGoogle Scholar
  39. 39.
    Palmer AE, Giacomello M, Kortemme T, Hires SA, Lev-Ram V, Baker D, Tsien RY (2006) Ca2+ indicators based on computationally redesigned calmodulin-peptide pairs. Chem Biol 13(5):521–530PubMedCrossRefGoogle Scholar
  40. 40.
    Lassila JK, Keeffe JR, Oelschlaeger P, Mayo SL (2005) Computationally designed variants of Escherichia coli chorismate mutase show altered catalytic activity. Protein Eng Des Sel 18(4):161–163PubMedCrossRefGoogle Scholar
  41. 41.
    Bornscheuer UT, Pohl M (2001) Improved biocatalysts by directed evolution and rational protein design. Curr Opin Chem Biol 5(2):137–143PubMedCrossRefGoogle Scholar
  42. 42.
    Faiella M, Andreozzi C, de Rosales RTM, Pavone V, Maglio O, Nastri F, DeGrado WF, Lombardi A (2009) An artificial di-iron oxo-protein with phenol oxidase activity. Nat Chem Biol 5(12):882–884PubMedCrossRefGoogle Scholar
  43. 43.
    Tinberg CE, Khare SD, Dou J, Doyle L, Nelson JW, Schena A, Jankowski W, Kalodimos CG, Johnsson K, Stoddard BL, Baker D (2013) Computational design of ligand binding proteins with high affinity and selectivity. Nature 501(7466):212–216PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Kaplan J, DeGrado WF (2004) De novo design of catalytic proteins. Proc Natl Acad Sci U S A 101(32):11566–11570PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Dahiyat BI, Mayo SL (1997) De novo protein design: fully automated sequence selection. Science 278(5335):82–87PubMedCrossRefGoogle Scholar
  46. 46.
    Jiang L, Althoff EA, Clemente FR, Doyle L, Röthlisberger D, Zanghellini A, Gallaher JL, Betker JL, Tanaka F, Barbas CF, Hilvert D, Houk KN, Stoddard BL, Baker D (2008) De novo computational design of retro-aldol enzymes. Science (New York, NY) 319(5868):1387–1391CrossRefGoogle Scholar
  47. 47.
    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):190–195PubMedCrossRefGoogle Scholar
  48. 48.
    Moroz YS, Dunston TT, Makhlynets OV, Moroz OV, Wu Y, Yoon JH, Olsen AB, McLaughlin JM, Mack KL, Gosavi PM, van Nuland NAJ, Korendovych IV (2015) New tricks for old proteins: single mutations in a nonenzymatic protein give rise to various enzymatic activities. J Am Chem Soc 137(47):14905–14911PubMedCrossRefGoogle Scholar
  49. 49.
    Zanghellini A (2014) De novo computational enzyme design. Curr Opin Biotechnol 29:132–138PubMedCrossRefGoogle Scholar
  50. 50.
    Petrounia IP, Arnold FH (2000) Designed evolution of enzymatic properties. Curr Opin Biotechnol 11(4):325–330PubMedCrossRefGoogle Scholar
  51. 51.
    Arnold FH (2001) Combinatorial and computational challenges for biocatalyst design. Nature 409(6817):253–257PubMedCrossRefGoogle Scholar
  52. 52.
    Minshull J, Willem Stemmer PC (1999) Protein evolution by molecular breeding. Curr Opin Chem Biol 3(3):284–290PubMedCrossRefGoogle Scholar
  53. 53.
    Packer MS, Liu DR (2015) Methods for the directed evolution of proteins. Nat Rev Genet 16(7):379–394PubMedCrossRefGoogle Scholar
  54. 54.
    Jaeger K-E, Eggert T (2004) Enantioselective biocatalysis optimized by directed evolution. Curr Opin Biotechnol 15(4):305–313PubMedCrossRefGoogle Scholar
  55. 55.
    Jestin J-L, Kaminski PA (2004) Directed enzyme evolution and selections for catalysis based on product formation. J Biotechnol 113(103):85PubMedCrossRefGoogle Scholar
  56. 56.
    Tao H, Cornish VW (2002) Milestones in directed enzyme evolution. Curr Opin Chem Biol 6(6):858–864PubMedCrossRefGoogle Scholar
  57. 57.
    Williams GJ, Nelson AS, Berry A (2004) Directed evolution of enzymes for biocatalysis and the life sciences. Cell Mol Life Sci CMLS 61(24):3034–3046PubMedCrossRefGoogle Scholar
  58. 58.
    Dalby PA (2003) Optimising enzyme function by directed evolution. Curr Opin Struct Biol 13(4):500–505PubMedCrossRefGoogle Scholar
  59. 59.
    Bershtein S, Tawfik DS (2008) Advances in laboratory evolution of enzymes. Curr Opin Chem Biol 12(2):151–158PubMedCrossRefGoogle Scholar
  60. 60.
    Park S, Morley KL, Horsman GP, Holmquist M, Hult K, Kazlauskas RJ (2005) Focusing mutations into the P. fluorescens esterase binding site increases enantioselectivity more effectively than distant mutations. Chem Biol 12 (1):45–54Google Scholar
  61. 61.
    Strausberg SL, Ruan B, Fisher KE, Alexander PA, Bryan PN (2005) Directed coevolution of stability and catalytic activity in calcium-free subtilisin. Biochemistry 44(9):3272–3279PubMedCrossRefGoogle Scholar
  62. 62.
    Chockalingam K, Chen Z, Katzenellenbogen JA, Zhao H (2005) Directed evolution of specific receptor–ligand pairs for use in the creation of gene switches. Proc Natl Acad Sci U S A 102(16):5691–5696PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    Chica RA, Doucet N, Pelletier JN (2005) Semi-rational approaches to engineering enzyme activity: combining the benefits of directed evolution and rational design. Curr Opin Biotechnol 16(4):378–384PubMedCrossRefGoogle Scholar
  64. 64.
    Hill CM, Li W-S, Thoden JB, Holden HM, Raushel FM (2003) Enhanced degradation of chemical warfare agents through molecular engineering of the phosphotriesterase active site. J Am Chem Soc 125(30):8990–8991PubMedCrossRefGoogle Scholar
  65. 65.
    Reetz MT, Carballeira JD (2007) Iterative saturation mutagenesis (ISM) for rapid directed evolution of functional enzymes. Nat Protocol 2(4):891–903CrossRefGoogle Scholar
  66. 66.
    Lutz S, Patrick WM (2004) Novel methods for directed evolution of enzymes: quality, not quantity. Curr Opin Biotechnol 15(4):291–297PubMedCrossRefGoogle Scholar
  67. 67.
    Lutz S (2010) Beyond directed evolution–semi-rational protein engineering and design. Curr Opin Biotechnol 21(6):734–743PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Lippow SM, Moon TS, Basu S, Yoon S-H, Li X, Chapman BA, Robison K, Lipovšek D, Prather KLJ (2010) Engineering enzyme specificity using computational design of a defined-sequence library. Chem Biol 17(12):1306–1315PubMedCrossRefGoogle Scholar
  69. 69.
    Sebestova E, Bendl J, Brezovsky J, Damborský J (2014) Computational tools for designing smart libraries. Methods Mol Biol 1179:291–314PubMedCrossRefGoogle Scholar
  70. 70.
    Voigt CA, Mayo SL, Arnold FH, Wang Z-G (2001) Computationally focusing the directed evolution of proteins. J Cell Biochem 84(S37):58–63CrossRefGoogle Scholar
  71. 71.
    Zaugg J, Gumulya Y, Gillam EM, Boden M (2014) Computational tools for directed evolution: a comparison of prospective and retrospective strategies. Methods Mol Biol 1179:315–333PubMedCrossRefGoogle Scholar
  72. 72.
    Damborsky J, Brezovsky J (2009) Computational tools for designing and engineering biocatalysts. Curr Opin Chem Biol 13(1):26–34PubMedCrossRefGoogle Scholar
  73. 73.
    Pei J (2008) Multiple protein sequence alignment. Curr Opin Struct Biol 18(3):382–386PubMedCrossRefGoogle Scholar
  74. 74.
    Pavelka A, Chovancova E, Damborsky J (2009) HotSpot Wizard: a web server for identification of hot spots in protein engineering. Nucleic Acids Res 37(suppl 2):W376–W383PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Kuipers RK, Joosten H-J, van Berkel WJH, Leferink NGH, Rooijen E, Ittmann E, van Zimmeren F, Jochens H, Bornscheuer U, Vriend G, Martins dos Santos VAP, Schaap PJ (2010) 3DM: systematic analysis of heterogeneous superfamily data to discover protein functionalities. Proteins Struct Funct Bioinforma 78(9):2101–2113Google Scholar
  76. 76.
    Jochens H, Bornscheuer UT (2010) Natural diversity to guide focused directed evolution. ChemBioChem 11(13):1861–1866PubMedCrossRefGoogle Scholar
  77. 77.
    Goldsmith M, Tawfik DS (2012) Directed enzyme evolution: beyond the low-hanging fruit. Curr Opin Struct Biol 22(4):406–412PubMedCrossRefGoogle Scholar
  78. 78.
    Barak Y, Nov Y, Ackerley DF, Matin A (2007) Enzyme improvement in the absence of structural knowledge: a novel statistical approach. ISME J 2(2):171–179PubMedCrossRefGoogle Scholar
  79. 79.
    Rosenberg M, Goldblum A (2006) Computational protein design: a novel path to future protein drugs. Curr Pharm Des 12(31):3973–3997PubMedCrossRefGoogle Scholar
  80. 80.
    Poole AM, Ranganathan R (2006) Knowledge-based potentials in protein design. Curr Opin Struct Biol 16(4):508–513PubMedCrossRefGoogle Scholar
  81. 81.
    Koder RL, Dutton PL (2006) Intelligent design: the de novo engineering of proteins with specified functions. Dalton Trans 25:3045–3051CrossRefGoogle Scholar
  82. 82.
    Butterfoss GL, Kuhlman B (2006) Computer-based design of novel protein structures. Annu Rev Biophys Biomol Struct 35(1):49–65PubMedCrossRefGoogle Scholar
  83. 83.
    Ambroggio XI, Kuhlman B (2006) Design of protein conformational switches. Curr Opin Struct Biol 16(4):525–530PubMedCrossRefGoogle Scholar
  84. 84.
    Vizcarra CL, Mayo SL (2005) Electrostatics in computational protein design. Curr Opin Chem Biol 9(6):622–626PubMedCrossRefGoogle Scholar
  85. 85.
    Morin A, Meiler J, Mizoue LS (2011) Computational design of protein-ligand interfaces: potential in therapeutic development. Trends Biotechnol 29(4):159–166PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Malisi C, Schumann M, Toussaint NC, Kageyama J, Kohlbacher O, Höcker B (2012) Binding pocket optimization by computational protein design. PLoS ONE 7(12):e52505PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Saven JG (2011) Computational protein design: engineering molecular diversity, nonnatural enzymes, nonbiological cofactor complexes, and membrane proteins. Curr Opin Chem Biol 15(3):452–457PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Ollikainen N, Smith CA, Fraser JS, Kortemme T (2013) Methods in enzymology: “Flexible backbone sampling methods to model and design protein alternative conformations”. Methods Enzymol 523:61–85PubMedPubMedCentralCrossRefGoogle Scholar
  89. 89.
    Park S, Yang X, Saven JG (2004) Advances in computational protein design. Curr Opin Struct Biol 14(4):487–494PubMedCrossRefGoogle Scholar
  90. 90.
    Samish I, MacDermaid CM, Perez-Aguilar JM, Saven JG (2011) Theoretical and computational protein design. Annu Rev Phys Chem 62(1):129–149PubMedCrossRefGoogle Scholar
  91. 91.
    Smith RD, Damm-Ganamet KL, Dunbar JB, Ahmed A, Chinnaswamy K, Delproposto JE, Kubish GM, Tinberg CE, Khare SD, Dou J, Doyle L, Stuckey JA, Baker D, Carlson HA (2015) CSAR benchmark exercise 2013: evaluation of results from a combined computational protein design, docking, and scoring/ranking challenge. J Chem Inf Model ASAP 56:1022CrossRefGoogle Scholar
  92. 92.
    Wijma HJ, Janssen DB (2013) Computational design gains momentum in enzyme catalysis engineering. FEBS J 280(13):2948–2960PubMedCrossRefGoogle Scholar
  93. 93.
    Boas FE, Harbury PB (2007) Potential energy functions for protein design. Curr Opin Struct Biol 17(2):199–204PubMedCrossRefGoogle Scholar
  94. 94.
    Boas FE, Harbury PB (2008) Design of protein-ligand binding based on the molecular-mechanics energy model. J Mol Biol 380(2):415–424PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Sirin S, Pearlman DA, Sherman W (2014) Physics-based enzyme design: predicting binding affinity and catalytic activity. Proteins Struct Funct Bioinforma 82(12):3397–3409CrossRefGoogle Scholar
  96. 96.
    Wickstrom L, Gallicchio E, Levy RM (2012) The linear interaction energy method for the prediction of protein stability changes upon mutation. Proteins 80(1):111–125PubMedCrossRefGoogle Scholar
  97. 97.
    Mendes J, Guerois R, Serrano L (2002) Energy estimation in protein design. Curr Opin Struct Biol 12(4):441–446PubMedCrossRefGoogle Scholar
  98. 98.
    Schneider M, Fu X, Keating AE (2009) X-ray vs. NMR structures as templates for computational protein design. Proteins 77(1):97–110PubMedPubMedCentralCrossRefGoogle Scholar
  99. 99.
    Adamczyk AJ, Cao J, Kamerlin SCL, Warshel A (2011) Catalysis by dihydrofolate reductase and other enzymes arises from electrostatic preorganization, not conformational motions. Proc Natl Acad Sci U S A 108(34):14115–14120PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Gagné D, French Rachel L, Narayanan C, Simonović M, Agarwal Pratul K, Doucet N (2015) Perturbation of the conformational dynamics of an active-site loop alters enzyme activity. Structure 23(12):2256–2266PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Bhabha G, Lee J, Ekiert DC, Gam J, Wilson IA, Dyson HJ, Benkovic SJ, Wright PE (2011) A dynamic knockout reveals that conformational fluctuations influence the chemical step of enzyme catalysis. Science 332(6026):234–238PubMedPubMedCentralCrossRefGoogle Scholar
  102. 102.
    Allen BD, Nisthal A, Mayo SL (2010) Experimental library screening demonstrates the successful application of computational protein design to large structural ensembles. Proc Natl Acad Sci 107(46):19838–19843PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Fu X, Apgar JR, Keating AE (2007) Modeling backbone flexibility to achieve sequence diversity: the design of novel α-helical ligands for Bcl-xL. J Mol Biol 371(4):1099–1117PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Smith CA, Kortemme T (2008) Backrub-like backbone simulation recapitulates natural protein conformational variability and improves mutant side-chain prediction. J Mol Biol 380(4):742–756PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Lassila JK (2010) Conformational diversity and computational enzyme design. Curr Opin Chem Biol 14(5):676–682PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Mandell DJ, Kortemme T (2009) Backbone flexibility in computational protein design. Curr Opin Biotechnol 20(4):420–428PubMedCrossRefGoogle Scholar
  107. 107.
    Marrink SJ, Risselada HJ, Yefimov S, Tieleman DP, De Vries AH (2007) The MARTINI force field: coarse grained model for biomolecular simulations. J Phys Chem B 111(27):7812–7824PubMedCrossRefGoogle Scholar
  108. 108.
    Bowen JP, Allinger NL (2007) Molecular mechanics: the art and science of parameterization. Rev Comput Chem 2:81–97CrossRefGoogle Scholar
  109. 109.
    Doruker P, Atilgan AR, Bahar I (2000) Dynamics of proteins predicted by molecular dynamics simulations and analytical approaches: application to α-amylase inhibitor. Proteins Struct Funct Bioinforma 40(3):512–524CrossRefGoogle Scholar
  110. 110.
    Berendsen H (1988) Dynamic simulation as an essential tool in molecular modeling. J Comput Aided Mol Des 2(3):217–221PubMedCrossRefGoogle Scholar
  111. 111.
    Grossman J, Towles B, Greskamp B, Shaw DE (2015) Filtering, reductions and synchronization in the anton 2 network. In: Parallel and Distributed Processing Symposium (IPDPS), 2015 IEEE International. IEEE, pp 860–870Google Scholar
  112. 112.
    Rathore N, de Pablo JJ (2002) Monte Carlo simulation of proteins through a random walk in energy space. J Chem Phys 116(16):7225–7230CrossRefGoogle Scholar
  113. 113.
    Borrelli KW, Vitalis A, Alcantara R, Guallar V (2005) PELE: protein energy landscape exploration. A novel Monte Carlo based technique. J Chem Theory Comput 1(6):1304–1311PubMedCrossRefGoogle Scholar
  114. 114.
    Cabeza de Vaca I, Lucas MF, Guallar V (2015) New Monte Carlo based technique to study DNA–ligand interactions. J Chem Theory Comput 11(12):5598–5605PubMedCrossRefGoogle Scholar
  115. 115.
    Borrelli KW, Cossins B, Guallar V (2010) Exploring hierarchical refinement techniques for induced fit docking with protein and ligand flexibility. J Comput Chem 31(6):1224–1235PubMedGoogle Scholar
  116. 116.
    Halperin I, Ma B, Wolfson H, Nussinov R (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins Struct Funct Bioinforma 47(4):409–443CrossRefGoogle Scholar
  117. 117.
    Gao J, Truhlar DG (2002) Quantum mechanical methods for enzyme kinetics. Annu Rev Phys Chem 53(1):467–505PubMedCrossRefGoogle Scholar
  118. 118.
    Korkegian A (2005) Computational thermostabilization of an enzyme. Science 308(5723):857–860PubMedPubMedCentralCrossRefGoogle Scholar
  119. 119.
    Kellogg EH, Leaver-Fay A, Baker D (2011) Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins 79(3):830–838PubMedCrossRefGoogle Scholar
  120. 120.
    Dunbrack RL Jr (2002) Rotamer libraries in the 21st century. Curr Opin Struct Biol 12(4):431–440PubMedCrossRefGoogle Scholar
  121. 121.
    Kuhlman B, Baker D (2000) Native protein sequences are close to optimal for their structures. Proc Natl Acad Sci 97(19):10383–10388PubMedPubMedCentralCrossRefGoogle Scholar
  122. 122.
    Potapov V, Cohen M, Schreiber G (2009) Assessing computational methods for predicting protein stability upon mutation: good on average but not in the details. Protein Eng Des Sel 22(9):553–560PubMedCrossRefGoogle Scholar
  123. 123.
    Estrada J, Echenique P, Sancho J (2015) Predicting stabilizing mutations in proteins using Poisson-Boltzmann based models: study of unfolded state ensemble models and development of a successful binary classifier based on residue interaction energies. Phys Chem Chem Phys 17(46):31044–31054PubMedCrossRefGoogle Scholar
  124. 124.
    Karplus M, Ichiye T, Pettitt BM (1987) Configurational entropy of native proteins. Biophys J 52(6):1083–1085PubMedPubMedCentralCrossRefGoogle Scholar
  125. 125.
    Chong S-H, Ham S (2015) Dissecting protein configurational entropy into conformational and vibrational contributions. J Phys Chem B 119(39):12623–12631PubMedCrossRefGoogle Scholar
  126. 126.
    Frappier V, Chartier M, Najmanovich RJ (2015) ENCoM server: exploring protein conformational space and the effect of mutations on protein function and stability. Nucleic Acids Res 43(W1):W395–W400PubMedPubMedCentralCrossRefGoogle Scholar
  127. 127.
    Frappier V, Najmanovich RJ (2014) A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations. PLoS Comput Biol 10(4):e1003569PubMedPubMedCentralCrossRefGoogle Scholar
  128. 128.
    Seeliger D, Daniel S, de Groot BL (2010) Protein thermostability calculations using alchemical free energy simulations. Biophys J 98(10):2309–2316PubMedPubMedCentralCrossRefGoogle Scholar
  129. 129.
    Huang X, Gao D, Zhan C-G (2011) Computational design of a thermostable mutant of cocaine esterase via molecular dynamics simulations. Org Biomol Chem 9(11):4138–4143PubMedPubMedCentralCrossRefGoogle Scholar
  130. 130.
    Joo JC, Pack SP, Kim YH, Yoo YJ (2011) Thermostabilization of Bacillus circulans xylanase: computational optimization of unstable residues based on thermal fluctuation analysis. J Biotechnol 151(1):56–65PubMedCrossRefGoogle Scholar
  131. 131.
    Lee C-W, Wang H-J, Hwang J-K, Tseng C-P (2014) Protein thermal stability enhancement by designing salt bridges: a combined computational and experimental study. PLoS ONE 9(11):e112751PubMedPubMedCentralCrossRefGoogle Scholar
  132. 132.
    Pikkemaat MG, Linssen ABM, Berendsen HJC, Janssen DB (2002) Molecular dynamics simulations as a tool for improving protein stability. Protein Eng 15(3):185–192PubMedCrossRefGoogle Scholar
  133. 133.
    Gribenko AV, Patel MM, Liu J, McCallum SA, Wang C, Makhatadze GI (2009) Rational stabilization of enzymes by computational redesign of surface charge-charge interactions. Proc Natl Acad Sci 106(8):2601–2606PubMedPubMedCentralCrossRefGoogle Scholar
  134. 134.
    Spector S, Wang M, Carp SA, Robblee J, Hendsch ZS, Fairman R, Tidor B, Raleigh DP (2000) Rational modification of protein stability by the mutation of charged surface residues. Biochemistry 39(5):872–879PubMedCrossRefGoogle Scholar
  135. 135.
    Schweiker KL, Arash Z-A, Davidson AR, Makhatadze GI (2007) Computational design of the Fyn SH3 domain with increased stability through optimization of surface charge-charge interactions. Protein Sci 16(12):2694–2702PubMedPubMedCentralCrossRefGoogle Scholar
  136. 136.
    Borgo B, Havranek JJ (2012) Automated selection of stabilizing mutations in designed and natural proteins. Proc Natl Acad Sci U S A 109(5):1494–1499PubMedPubMedCentralCrossRefGoogle Scholar
  137. 137.
    Hendsch ZS, Thorlakur J, Sauer RT, Bruce T (1996) Protein stabilization by removal of unsatisfied polar groups: computational approaches and experimental tests. Biochemistry 35(24):7621–7625PubMedCrossRefGoogle Scholar
  138. 138.
    Koudelakova T, Chaloupkova R, Brezovsky J, Prokop Z, Sebestova E, Hesseler M, Khabiri M, Plevaka M, Kulik D, Kuta Smatanova I, Rezacova P, Ettrich R, Bornscheuer UT, Damborsky J (2013) Engineering enzyme stability and resistance to an organic cosolvent by modification of residues in the access tunnel. Angew Chem Int Ed Engl 52(7):1959–1963PubMedCrossRefGoogle Scholar
  139. 139.
    Wijma HJ, Floor RJ, Jekel PA, Baker D, Marrink SJ, Janssen DB (2014) Computationally designed libraries for rapid enzyme stabilization. Protein Eng Des Sel 27(2):49–58PubMedPubMedCentralCrossRefGoogle Scholar
  140. 140.
    Wijma HJ, Floor RJ, Janssen DB (2013) Structure- and sequence-analysis inspired engineering of proteins for enhanced thermostability. Curr Opin Struct Biol 23(4):588–594PubMedCrossRefGoogle Scholar
  141. 141.
    Schreier B, Stumpp C, Wiesner S, Hocker B (2009) Computational design of ligand binding is not a solved problem. Proc Natl Acad Sci 106(44):18491–18496PubMedPubMedCentralCrossRefGoogle Scholar
  142. 142.
    Allison B, Combs S, DeLuca S, Lemmon G, Mizoue L, Meiler J (2014) Computational design of protein-small molecule interfaces. J Struct Biol 185(2):193–202PubMedCrossRefGoogle Scholar
  143. 143.
    Gainza P, Roberts KE, Georgiev I, Lilien RH, Keedy DA, Chen C-Y, Reza F, Anderson AC, Richardson DC, Richardson JS, Donald BR (2013) OSPREY: protein design with ensembles, flexibility, and provable algorithms. Methods Enzymol 523:87–107PubMedPubMedCentralCrossRefGoogle Scholar
  144. 144.
    Keedy DA, Georgiev I, Triplett EB, Donald BR, Richardson DC, Richardson JS (2012) The role of local backrub motions in evolved and designed mutations. PLoS Comput Biol 8(8):e1002629PubMedPubMedCentralCrossRefGoogle Scholar
  145. 145.
    Davis IW, Bryan Arendall W, Richardson DC, Richardson JS (2006) The backrub motion: how protein backbone shrugs when a sidechain dances. Structure 14(2):265–274PubMedCrossRefGoogle Scholar
  146. 146.
    Chen C-Y, Georgiev I, Anderson AC, Donald BR (2009) Computational structure-based redesign of enzyme activity. Proc Natl Acad Sci U S A 106(10):3764–3769PubMedPubMedCentralCrossRefGoogle Scholar
  147. 147.
    Frey KM, Georgiev I, Donald BR, Anderson AC (2010) Predicting resistance mutations using protein design algorithms. Proc Natl Acad Sci U S A 107(31):13707–13712PubMedPubMedCentralCrossRefGoogle Scholar
  148. 148.
    Zhou Y, Xu W, Donald BR, Zeng J (2014) An efficient parallel algorithm for accelerating computational protein design. Bioinformatics 30(12):i255–i263PubMedPubMedCentralCrossRefGoogle Scholar
  149. 149.
    Hallen MA, Keedy DA, Donald BR (2013) Dead-end elimination with perturbations (DEEPer): a provable protein design algorithm with continuous sidechain and backbone flexibility. Proteins 81(1):18–39PubMedCrossRefGoogle Scholar
  150. 150.
    Lilien RH, Stevens BW, Anderson AC, Donald BR (2005) A novel ensemble-based scoring and search algorithm for protein redesign and its application to modify the substrate specificity of the gramicidin synthetase a phenylalanine adenylation enzyme. J Comput Biol 12(6):740–761PubMedCrossRefGoogle Scholar
  151. 151.
    Leach AR (2001) Molecular modelling: principles and applications. Pearson Education, New YorkGoogle Scholar
  152. 152.
    Shields GC, Seybold PG (2013) Computational approaches for the prediction of pKa values. CRC Press, Boca RatonCrossRefGoogle Scholar
  153. 153.
    Pardo I, Santiago G, Gentili P, Lucas F, Monza E, Medrano F, Galli C, Martínez A, Guallar V, Camarero S (2016) Re-designing the substrate binding pocket of laccase for enhanced oxidation of sinapic acid. Catal Sci Technol ASAP 6:3900CrossRefGoogle Scholar
  154. 154.
    Young T, Abel R, Kim B, Berne BJ, Friesner RA (2007) Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding. Proc Natl Acad Sci 104(3):808–813PubMedPubMedCentralCrossRefGoogle Scholar
  155. 155.
    Kiss G, Çelebi-Ölçüm N, Moretti R, Baker D, Houk KN (2013) Computational enzyme design. Angew Chem Int Ed 52(22):5700–5725CrossRefGoogle Scholar
  156. 156.
    Doerr S, De Fabritiis G (2014) On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations. J Chem Theory Comput 10(5):2064–2069PubMedCrossRefGoogle Scholar
  157. 157.
    Wijma HJ, Floor RJ, Bjelic S, Marrink SJ, Baker D, Janssen DB (2015) Enantioselective enzymes by computational design and in silico screening. Angew Chem Int Ed Engl 54(12):3726–3730PubMedCrossRefGoogle Scholar
  158. 158.
    Jiménez-Osés G, Osuna S, Gao X, Sawaya MR, Gilson L, Collier SJ, Huisman GW, Yeates TO, Tang Y, Houk KN (2014) The role of distant mutations and allosteric regulation on LovD active site dynamics. Nat Chem Biol 10(6):431–436PubMedPubMedCentralCrossRefGoogle Scholar
  159. 159.
    Osuna S, Jiménez-Osés G, Noey EL, Houk KN (2015) Molecular dynamics explorations of active site structure in designed and evolved enzymes. Acc Chem Res 48(4):1080–1089PubMedCrossRefGoogle Scholar
  160. 160.
    DuBay KH, Bowman GR, Geissler PL (2015) Fluctuations within folded proteins: implications for thermodynamic and allosteric regulation. Acc Chem Res 48(4):1098–1105PubMedCrossRefGoogle Scholar
  161. 161.
    Sethi A, Eargle J, Black AA, Luthey-Schulten Z (2009) Dynamical networks in tRNA:protein complexes. Proc Natl Acad Sci U S A 106(16):6620–6625PubMedPubMedCentralCrossRefGoogle Scholar
  162. 162.
    Madadkar-Sobhani A, Guallar V (2013) PELE web server: atomistic study of biomolecular systems at your fingertips. Nucleic Acids Res 41(Web Server issue):W322–W328Google Scholar
  163. 163.
    Lucas MF, Guallar V (2012) An atomistic view on human hemoglobin carbon monoxide migration processes. Biophys J 102(4):887–896PubMedPubMedCentralCrossRefGoogle Scholar
  164. 164.
    Takahashi R, Gil VA, Guallar V (2014) Monte Carlo free ligand diffusion with Markov state model analysis and absolute binding free energy calculations. J Chem Theory Comput 10(1):282–288PubMedCrossRefGoogle Scholar
  165. 165.
    Hosseini A, Brouk M, Lucas MF, Glaser F, Fishman A, Guallar V (2015) Atomic picture of ligand migration in toluene 4-monooxygenase. J Phys Chem B 119(3):671–678PubMedCrossRefGoogle Scholar
  166. 166.
    Lüdemann SK, Lounnas V, Wade RC (2000) How do substrates enter and products exit the buried active site of cytochrome P450cam? 1. Random expulsion molecular dynamics investigation of ligand access channels and mechanisms. J Mol Biol 303(5):797–811PubMedCrossRefGoogle Scholar
  167. 167.
    Grubmüller H, Heymann B, Tavan P (1996) Ligand binding: molecular mechanics calculation of the streptavidin-biotin rupture force. Science 271(5251):997–999PubMedCrossRefGoogle Scholar
  168. 168.
    Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinforma 10(1):168CrossRefGoogle Scholar
  169. 169.
    Chovancova E, Eva C, Antonin P, Petr B, Ondrej S, Jan B, Barbora K, Artur G, Vilem S, Martin K, Petr M, Lada B, Jiri S, Jiri D (2012) CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures. PLoS Comput Biol 8(10):e1002708PubMedPubMedCentralCrossRefGoogle Scholar
  170. 170.
    Senn HM, Walter T (2009) QM/MM methods for biomolecular systems. Angew Chem Int Ed 48(7):1198–1229CrossRefGoogle Scholar
  171. 171.
    Chaskar P, Prasad C, Vincent Z, Röhrig UF (2014) Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function. J Chem Inf Model 54(11):3137–3152PubMedCrossRefGoogle Scholar
  172. 172.
    Cho AE, Victor G, Berne BJ, Richard F (2005) Importance of accurate charges in molecular docking: quantum mechanical/molecular mechanical (QM/MM) approach. J Comput Chem 26(9):915–931PubMedPubMedCentralCrossRefGoogle Scholar
  173. 173.
    Fedorov DG, Nagata T, Kitaura K (2012) Exploring chemistry with the fragment molecular orbital method. Phys Chem Chem Phys 14(21):7562–7577PubMedCrossRefGoogle Scholar
  174. 174.
    Jensen JH, Willemoës M, Winther JR, De Vico L (2014) In silico prediction of mutant HIV-1 proteases cleaving a target sequence. PLoS ONE 9(5):e95833PubMedPubMedCentralCrossRefGoogle Scholar
  175. 175.
    Grisewood MJ, Gifford NP, Pantazes RJ, Li Y, Cirino PC, Janik MJ, Maranas CD (2013) OptZyme: computational enzyme redesign using transition state analogues. PLoS ONE 8(10):e75358PubMedPubMedCentralCrossRefGoogle Scholar
  176. 176.
    Atkins PW (1998) Physical chemistry. W H Freeman & Company, New YorkGoogle Scholar
  177. 177.
    Khersonsky O, Rothlisberge D, Wollacott AM, Dym O, Baker D, Tawfik DS (2011) Optimization of the in silico designed Kemp eliminase KE70 by computational design and directed evolution. J Mol Biol 407(3):391–412PubMedPubMedCentralCrossRefGoogle Scholar
  178. 178.
    Genheden S, Samuel G, Ulf R (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10(5):449–461PubMedPubMedCentralCrossRefGoogle Scholar
  179. 179.
    van der Kamp MW, Mulholland AJ (2013) Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology. Biochemistry 52(16):2708–2728PubMedCrossRefGoogle Scholar
  180. 180.
    Zheng F, Fang Z, Wenchao Y, Mei-Chuan K, Junjun L, Hoon C, Daquan G, Min T, Hsin-Hsiung T, Woods JH, Chang-Guo Z (2008) Most efficient cocaine hydrolase designed by virtual screening of transition states. J Am Chem Soc 130(36):12148–12155PubMedPubMedCentralCrossRefGoogle Scholar
  181. 181.
    Kamerlin SCL, Arieh W (2011) The empirical valence bond model: theory and applications. Wiley Interdiscip Rev Comput Mol Sci 1(1):30–45CrossRefGoogle Scholar
  182. 182.
    Frushicheva MP, Cao J, Chu ZT, Warshel A (2010) Exploring challenges in rational enzyme design by simulating the catalysis in artificial kemp eliminase. Proc Natl Acad Sci U S A 107(39):16869–16874PubMedPubMedCentralCrossRefGoogle Scholar
  183. 183.
    Frushicheva MP, Cao J, Warshel A (2011) Challenges and advances in validating enzyme design proposals: the case of kemp eliminase catalysis. Biochemistry 50(18):3849–3858PubMedPubMedCentralCrossRefGoogle Scholar
  184. 184.
    Amrein BA, Ireneusz Szeler F, Purg M, Kulkarni Y, Kamerlin SCL (2017) CADEE: Computer-aided directed evolution of enzymes. IUCrJ 4:50–64.Google Scholar
  185. 185.
    Hediger MR, De Vico L, Svendsen A, Besenmatter W, Jensen JH (2012) A computational methodology to screen activities of enzyme variants. PLoS ONE 7(12):e49849PubMedPubMedCentralCrossRefGoogle Scholar
  186. 186.
    Hediger MR, Casper S, De Vico L, Jensen JH (2013) A computational method for the systematic screening of reaction barriers in enzymes: searching for Bacillus circulans xylanase mutants with greater activity towards a synthetic substrate. PeerJ 1:e111PubMedPubMedCentralCrossRefGoogle Scholar
  187. 187.
    Hediger MR, De Vico L, Rannes JB, Christian J, Werner B, Allan S, Jensen JH (2013) In silico screening of 393 mutants facilitates enzyme engineering of amidase activity in CalB. PeerJ 1:e145PubMedPubMedCentralCrossRefGoogle Scholar
  188. 188.
    Ito M, Mika I, Tore B (2014) Novel approach for identifying key residues in enzymatic reactions: proton abstraction in ketosteroid isomerase. J Phys Chem B 118(46):13050–13058PubMedCrossRefGoogle Scholar
  189. 189.
    Steinmann C, Fedorov DG, Jensen JH (2012) The effective fragment molecular orbital method for fragments connected by covalent bonds. PLoS ONE 7(7):e41117PubMedPubMedCentralCrossRefGoogle Scholar
  190. 190.
    Steinmann C, Casper S, Fedorov DG, Jensen JH (2013) Mapping enzymatic catalysis using the effective fragment molecular orbital method: towards all ab initio biochemistry. PLoS ONE 8(4):e60602PubMedPubMedCentralCrossRefGoogle Scholar
  191. 191.
    Marcus RA (1993) Electron transfer reactions in chemistry. Theory and experiment. Rev Mod Phys 65(3):599–610CrossRefGoogle Scholar
  192. 192.
    Blumberger J, Jochen B (2008) Free energies for biological electron transfer from QM/MM calculation: method, application and critical assessment. Phys Chem Chem Phys 10(37):5651PubMedCrossRefGoogle Scholar
  193. 193.
    Wallrapp FH, Voityuk AA, Guallar V (2013) In-silico assessment of protein-protein electron transfer. A case study: cytochrome c peroxidase–cytochrome c. PLoS Comput Biol 9(3):e1002990PubMedPubMedCentralCrossRefGoogle Scholar
  194. 194.
    Monza E, Lucas MF, Camarero S, Alejaldre LC, Martínez AT, Guallar V (2015) Insights into laccase engineering from molecular simulations: toward a binding-focused strategy. J Phys Chem Lett 6(8):1447–1453PubMedCrossRefGoogle Scholar
  195. 195.
    Gerard S, Felipe de S, Fátima Lucas M, Emanuele M, Sandra A, Ángel TM, Susana Camarero, VG (2016) Computer-aided laccase engineering: toward biological oxidation of arylamines. ACS Catalysis, 6:5415–5423Google Scholar
  196. 196.
    Acebes S, Fernandez-Fueyo E, Monza E, Lucas M, Almendral D, Ruiz-Dueñas FJ, Lund H, Martinez AT, Guallar V (2016) Rational enzyme engineering through biophysical and biochemical modeling. ACS Catal ACS Catalysis 6(3):1624–1629Google Scholar
  197. 197.
    Guallar V, Wallrapp F (2008) Mapping protein electron transfer pathways with QM/MM methods. J R Soc Interface 5(0):S233PubMedPubMedCentralCrossRefGoogle Scholar
  198. 198.
    Vidal-Limón A, Águila S, Ayala M, Batista CV, Vazquez-Duhalt R (2013) Peroxidase activity stabilization of cytochrome P450 BM3 by rational analysis of intramolecular electron transfer. J Inorg Biochem 122:18–26PubMedCrossRefGoogle Scholar
  199. 199.
    Fox RJ, Huisman GW (2008) Enzyme optimization: moving from blind evolution to statistical exploration of sequence–function space. Trends Biotechnol 26(3):132–138PubMedCrossRefGoogle Scholar
  200. 200.
    Feng X, Sanchis J, Reetz MT, Rabitz H (2012) Enhancing the efficiency of directed evolution in focused enzyme libraries by the adaptive substituent reordering algorithm. Chem Eur J 18(18):5646–5654PubMedCrossRefGoogle Scholar
  201. 201.
    Cui Q, Elstner M (2014) Density functional tight binding: values of semi-empirical methods in an ab initio era. Phys Chem Chem Phys 16(28):14368–14377PubMedPubMedCentralCrossRefGoogle Scholar
  202. 202.
    Christensen AS, Elstner M, Cui Q (2015) Improving intermolecular interactions in DFTB3 using extended polarization from chemical-potential equalization. J Chem Phys 143(8):084123PubMedPubMedCentralCrossRefGoogle Scholar
  203. 203.
    Yilmazer ND, Korth M (2015) Enhanced semiempirical QM methods for biomolecular interactions. Comput Struct Biotechnol J 13:169–175PubMedPubMedCentralCrossRefGoogle Scholar
  204. 204.
    Privett HK, Kiss G, Lee TM, Blomberg R, Chica RA, Thomas LM, Hilvert D, Houk KN, Mayo SL (2012) Iterative approach to computational enzyme design. Proc Natl Acad Sci 109(10):3790–3795PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Joint BSC-CRG-IRB Research Program in Computational BiologyBarcelona Supercomputing CenterBarcelonaSpain
  2. 2.Anaxomics BiotechBarcelonaSpain
  3. 3.ICREA, Passeig Lluís Companys 23BarcelonaSpain

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