Navigating the protein fitness landscape with Gaussian processes

Proceedings of the National Academy of Sciences of the United States of America

Volumn 110, Issue 3, 2013, Pages

  Romero, Philip A ^a   Krause, Andreas ^b   Arnold, Frances H ^a  

^a California Institute of Technology   (United States)

^b ETH ZURICH   (Switzerland)

Author keywords

Active learning; Experimental design; Machine learning; Protein engineering; Recombination

Indexed keywords

PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; BAYES THEOREM; CATALYSIS; ENZYME ACTIVITY; KERNEL METHOD; LIGAND BINDING; PRIORITY JOURNAL; PROTEIN ENGINEERING; THERMOSTABILITY;

ALGORITHMS; BAYES THEOREM; CYTOCHROME P-450 ENZYME SYSTEM; DATABASES, PROTEIN; EVOLUTION, MOLECULAR; MODELS, MOLECULAR; NORMAL DISTRIBUTION; PROTEIN ENGINEERING; PROTEIN STABILITY; PROTEINS; RECOMBINANT FUSION PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 84872552315     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1215251110     Document Type: Article

References (44)

1. Romero PA, Arnold FH (2009) Exploring protein fitness landscapes by directed evolution. Nat Rev Mol Cell Biol 10(12):866-876.
2. Mandecki W (1998) The game of chess and searches in protein sequence space. Trends Biotechnol 16:200-202.
3. Pierce NA, Winfree E (2002) Protein design is NP-hard. Protein Eng 15(10):779-782. (Pubitemid 36043220)
4. Keefe AD, Szostak JW (2001) Functional proteins from a random-sequence library. Nature 410(6829):715-718. (Pubitemid 32290361)
5. Axe DD (2004) Estimating the prevalence of protein sequences adopting functional enzyme folds. J Mol Biol 341(5):1295-1315.
6. Taverna DM, Goldstein RA (2002) Why are proteins marginally stable? Proteins 46(1):105-109. (Pubitemid 34033580)
7. Orr HA (2006) The distribution of fitness effects among beneficial mutations in Fisher's geometric model of adaptation. J Theor Biol 238(2):279-285.
8. Dahiyat BI, Mayo SL (1997) De novo protein design: Fully automated sequence selection. Science 278(5335):82-87. (Pubitemid 27446279)
9. Jiang L, et al. (2008) De novo computational design of retro-aldol enzymes. Science 319(5868):1387-1391. (Pubitemid 351354873)
10. Baker D (2010) An exciting but challenging road ahead for computational enzyme design. Protein Sci 19(10):1817-1819.
11. Williams CKI, Rasmussen CE (1996) Advances in Neural Information Processing Systems, eds Touretzky DS, Mozer MC, Hasselmo ME (MIT Press, Cambridge, MA), pp 514-520.
12. Rasmussen CE, Williams C (2006) Gaussian Processes for Machine Learning (MIT Press, Cambridge, MA).
13. Stein ML (1999) Interpolation of Spatial Data: Some Theory for Kriging (Springer, New York), 1st Ed.
14. Williams CKI, Barber D (1997) Bayesian classification with Gaussian processes. IEEE Trans Pattern Anal Mach Intell 20:1342-1351. (Pubitemid 128741378)
15. Otey CR, et al. (2006) Structure-guided recombination creates an artificial family of cytochromes P450. PLoS Biol 4(5):e112.
16. Li Y, et al. (2007) A diverse family of thermostable cytochrome P450s created by recombination of stabilizing fragments. Nat Biotechnol 25(9):1051-1056. (Pubitemid 47517641)
17. Otey CR, et al. (2004) Functional evolution and structural conservation in chimeric cytochromes p450: Calibrating a structure-guided approach. Chem Biol 11(3):309-318. (Pubitemid 39403870)
18. Shewry MC, Wynn HP (1987) Maximum entropy sampling. J Appl Stat 14:165-170.
19. Guestrin C, Krause A, Singh AP (2005) Near-optimal sensor placements in Gaussian processes. Proceedings of the 22nd International Conference on Machine Learning, eds De Raedt L, Wrobel S (ACM, New York, NY), Vol 1, pp 265-272.
20. Krause A, Horvitz E, Kansal A, Zhao F (2008) Toward community sensing. Proceedings of the 7th International Conference on Information Processing in Sensor Networks (IEEE Computer Society, Washington, DC), pp 481-492.
21. Krause A, Guestrin C (2007) Near-optimal observation selection using submodular functions. Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI Press, Palo Alto, CA), Vol 22, pp 1650-1654.
22. Gavrilets S (1997) Evolution and speciation on holey adaptive landscapes. Trends Ecol Evol 12(8):307-312.
23. Brustad EM, et al. (2012) Structure-guided directed evolution of highly selective p450-based magnetic resonance imaging sensors for dopamine and serotonin. J Mol Biol 422(2):245-262.
24. Sutton RS, Barto AG (1998) Reinforcement Learning (MIT Press, Cambridge, MA).
25. Lizotte D, Wang T, Bowling M, Schuurmans D (2007) Automatic gait optimization with Gaussian process regression. Proceedings of the 20th International Joint Conference on Artificial Intelligence, ed Veloso MM (AAAI Press, Palo Alto, CA), pp 944-949.
26. Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455-492. (Pubitemid 128507405)
27. Frazier PI, Powell WB, Dayanik S (2008) A knowledge-gradient policy for sequential information collection. SIAM J Contr Optim 47:2410-2439.
28. Auer P (2002) Using confidence bounds for exploitation-exploration trade-offs. J Mach Learn Res 3:397-422.
29. Srinivas N, Krause A, Kakade SM, Seeger M (2010) Gaussian process optimization in the bandit setting: No regret and experimental design. Proceedings of the 27th International Conference on Machine learning, eds Furnkranz J, Joachims T (Omnipress, Madison, WI), pp 1015-1022.
30. Desautels T, Krause A, Burdick J (2012) Parallelizing exploration-exploitation tradeoffs with Gaussian process bandit optimization. Proceedings of the 29th International Conference on Machine Learning, eds Langford J, Pineau J (Omnipress Madison, WI).
31. Salazar O, Cirino PC, Arnold FH (2003) Thermostabilization of a cytochrome p450 peroxygenase. ChemBioChem 4(9):891-893. (Pubitemid 37185702)
32. Fox RJ, et al. (2007) Improving catalytic function by ProSAR-driven enzyme evolution. Nat Biotechnol 25(3):338-344. (Pubitemid 46398768)
33. Liao J, et al. (2007) Engineering proteinase K using machine learning and synthetic genes. BMC Biotechnol 7:16.
34. Zhang C, Liu S, Zhou H, Zhou Y (2004) An accurate, residue-level, pair potential of mean force for folding and binding based on the distance-scaled, ideal-gas reference state. Protein Sci 13(2):400-411. (Pubitemid 38124961)
35. Drummond DA, Silberg JJ, Meyer MM, Wilke CO, Arnold FH (2005) On the conservative nature of intragenic recombination. Proc Natl Acad Sci USA 102(15):5380-5385. (Pubitemid 40530266)
36. Landwehr M, Carbone M, Otey CR, Li Y, Arnold FH (2007) Diversification of catalytic function in a synthetic family of chimeric cytochrome p450s. Chem Biol 14(3):269-278. (Pubitemid 46428143)
37. Nemhauser GL, Wolsey LA, Fisher ML (1978) An analysis of approximations for maximizing submodular set functions. Math Prog 14:265-294.
38. Kelmans AK, Kimelfeld BN (1983) Multiplicative submodularity of a matrix's principal minor as a function of the set of its rows and some combinatorial applications. Discrete Math 44:113-116.
39. Minoux M (1978) Accelerated greedy algorithms for maximizing submodular set functions. Optim Tech 7:234-243.
40. Barnes HJ, Arlotto MP, Waterman MR (1991) Expression and enzymatic activity of recombinant cytochrome P450 17 alpha-hydroxylase in Escherichia coli. Proc Natl Acad Sci USA 88(13):5597-5601. (Pubitemid 21914792)
41. Otey CR, Joern JM (2003) High-throughput screen for aromatic hydroxylation. Methods Mol Biol 230:141-148.
42. Shapiro MG, et al. (2010) Directed evolution of a magnetic resonance imaging contrast agent for noninvasive imaging of dopamine. Nat Biotechnol 28(3):264-270.
43. Otey CR (2003) High-throughput carbon monoxide binding assay for cytochromes p450. Methods Mol Biol 230:137-139.
44. Peña D, Guttman I (1993) Comparing probabilistic methods for outlier detection in linear models. Biometrika 80:603-610.