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Machine-learning-guided directed evolution for protein engineering

Yang, Kevin K., Wu, Zachary, Arnold, Frances H.·2019·Nature Methods

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  1. Dou, J. et al. Sampling and energy evaluation challenges in ligand binding protein design. Protein Sci. 26, 2426–2437 (2017). · DOI
  2. Garcia-Borras, M., Houk, K. N. & Jiménez-Osés, G. Computational design of protein function. In Computational Tools for Chemical Biology (ed. Martín-Santamaría, S.) 87–107 (Royal Society of Chemistry, 2017). · DOI
  3. Mandecki, W. The game of chess and searches in protein sequence space. Trends Biotechnol. 16, 200–202 (1998). · DOI
  4. Pierce, N. A. & Winfree, E. Protein design is NP-hard. Protein Eng. 15, 779–782 (2002). · DOI
  5. Smith, J. M. Natural selection and the concept of a protein space. Nature 225, 563–564 (1970). · DOI
  6. Orr, H. A. The distribution of fitness effects among beneficial mutations in Fisher’s geometric model of adaptation. J. Theor. Biol. 238, 279–285 (2006). · DOI
  7. Khersonsky, O. & Tawfik, D. S. Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu. Rev. Biochem. 79, 471–505 (2010). · DOI · Paper page
  8. Romero, P. A. & Arnold, F. H. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 10, 866–876 (2009). · DOI
  9. Drummond, D. A., Silberg, J. J., Meyer, M. M., Wilke, C. O. & Arnold, F. H. On the conservative nature of intragenic recombination. Proc. Natl Acad. Sci. USA 102, 5380–5385 (2005). · DOI
  10. Fox, R. J. et al. Improving catalytic function by ProSAR-driven enzyme evolution. Nat. Biotechnol. 25, 338–344 (2007). · DOI
  11. Romero, P. A., Krause, A. & Arnold, F. H. Navigating the protein fitness landscape with Gaussian processes. Proc. Natl Acad. Sci. USA 110, E193–E201 (2013). This is the first study to combine SCHEMA recombination with the GP-UCB algorithm to optimize a protein property. · DOI
  12. Bedbrook, C. N., Yang, K. K., Rice, A. J., Gradinaru, V. & Arnold, F. H. Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization. PLoS Comput. Biol. 13, e1005786 (2017). · DOI
  13. Bedbrook, C. N., Yang, K. K., Robinson, J. E., Gradinaru, V. & Arnold, F. H. Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics. Preprint at https://www.biorxiv.org/content/10.1101/565606v1 (2019). This paper demonstrates the utility of machine learning for optimizing a property that would not be possible to engineer with directed evolution alone. · DOI
  14. Wu, Z., Kan, S. B. J., Lewis, R. D., Wittmann, B. J. & Arnold, F. H. Machine learning-assisted directed protein evolution with combinatorial libraries. Proc. Natl Acad. Sci. USA 116, 8852–8858 (2019). · DOI
  15. Murphy, K. Machine Learning, a Probabilistic Perspective (MIT Press, 2012). Murphy’s textbook provides a thorough introduction to modern machine learning.
  16. Liao, J. et al. Engineering proteinase K using machine learning and synthetic genes. BMC Biotechnol. 7, 16 (2007). · DOI
  17. Govindarajan, S. et al. Mapping of amino acid substitutions conferring herbicide resistance in wheat glutathione transferase. ACS Synth. Biol. 4, 221–227 (2015). · DOI
  18. Musdal, Y., Govindarajan, S. & Mannervik, B. Exploring sequence-function space of a poplar glutathione transferase using designed information-rich gene variants. Protein Eng. Des. Sel. 30, 543–549 (2017). · DOI
  19. Wolpert, D. H. The lack of a priori distinctions between learning algorithms. Neural Comput. 8, 1341–1390 (1996). · DOI
  20. Li, Y. et al. A diverse family of thermostable cytochrome P450s created by recombination of stabilizing fragments. Nat. Biotechnol. 25, 1051–1056 (2007). · DOI
  21. Friedman, J. H. Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002). · DOI · Paper page
  22. Tian, J., Wu, N., Chu, X. & Fan, Y. Predicting changes in protein thermostability brought about by single- or multi-site mutations. BMC Bioinforma. 11, 370 (2010). · DOI
  23. Li, Y. & Fang, J. PROTS-RF: a robust model for predicting mutation-induced protein stability changes. PLoS One 7, e47247 (2012). · DOI
  24. Jia, L., Yarlagadda, R. & Reed, C. C. Structure based thermostability prediction models for protein single point mutations with machine learning tools. PLoS One 10, e0138022 (2015). · DOI
  25. Nadaraya, E. On estimating regression. Theory Probab. Its Appl. 9, 141–142 (1964). · DOI · Paper page
  26. Leslie, C., Eskin, E. & Noble, W. S. The spectrum kernel: a string kernel for SVM protein classification. Pac. Symp. Biocomput. 2002, 564–575 (2002).
  27. Leslie, C. S., Eskin, E., Cohen, A., Weston, J. & Noble, W. S. Mismatch string kernels for discriminative protein classification. Bioinformatics 20, 467–476 (2004). · DOI
  28. Jokinen, E., Heinonen, M. & Lähdesmäki, H. mGPfusion: predicting protein stability changes with Gaussian process kernel learning and data fusion. Bioinformatics 34, i274–i283 (2018). · DOI
  29. Capriotti, E., Fariselli, P. & Casadio, R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 33, W306–W310 (2005). · DOI
  30. Capriotti, E., Fariselli, P., Calabrese, R. & Casadio, R. Predicting protein stability changes from sequences using support vector machines. Bioinformatics 21, ii54–ii58 (2005). · DOI
  31. Cheng, J., Randall, A. & Baldi, P. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 62, 1125–1132 (2006). · DOI
  32. Buske, F. A., Their, R., Gillam, E. M. & Bodén, M. In silico characterization of protein chimeras: relating sequence and function within the same fold. Proteins 77, 111–120 (2009). · DOI
  33. Liu, J. & Kang, X. Grading amino acid properties increased accuracies of single point mutation on protein stability prediction. BMC Bioinforma. 13, 44 (2012). · DOI
  34. Zaugg, J., Gumulya, Y., Malde, A. K. & Bodén, M. Learning epistatic interactions from sequence-activity data to predict enantioselectivity. J. Comput. Aided Mol. Des. 31, 1085–1096 (2017). · DOI
  35. Saladi, S. M., Javed, N., Müller, A. & Clemons, W. M. Jr. A statistical model for improved membrane protein expression using sequence-derived features. J. Biol. Chem. 293, 4913–4927 (2018). · DOI
  36. Wilson, A. G. & Nickisch, H. Kernel interpolation for scalable structured Gaussian processes (KISS-GP). In Proc. 32nd International Conference on Machine Learning (eds. Bach, F. & Blei, D.) 1775–1784 (JMLR, 2015).
  37. Wang, K. A. et al. Exact Gaussian processes on a million data points. Preprint at https://arxiv.org/abs/1903.08114 (2019). · arXiv
  38. Pires, D. E., Ascher, D. B. & Blundell, T. L. mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30, 335–342 (2014). · DOI · Paper page
  39. Mellor, J., Grigoras, I., Carbonell, P. & Faulon, J.-L. Semisupervised Gaussian process for automated enzyme search. ACS Synth. Biol. 5, 518–528 (2016). · DOI
  40. Saito, Y. et al. Machine-learning-guided mutagenesis for directed evolution of fluorescent proteins. ACS Synth. Biol. 7, 2014–2022 (2018). · DOI
  41. Zhang, S. et al. A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res. 44, e32 (2016). · DOI
  42. Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015). · DOI · Paper page
  43. Zeng, H., Edwards, M. D., Liu, G. & Gifford, D. K. Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics 32, i121–i127 (2016). · DOI
  44. Hu, J. & Liu, Z. DeepMHC: deep convolutional neural networks for high-performance peptide-MHC binding affinity prediction. Preprint at https://www.biorxiv.org/content/early/2017/12/24/239236 (2017). · DOI
  45. Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S. & De Fabritiis, G. DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics 33, 3036–3042 (2017). · DOI
  46. Gomes, J., Ramsundar, B., Feinberg, E. N. & Pande, V. S. Atomic convolutional networks for predicting protein-ligand binding affinity. Preprint at https://arxiv.org/abs/1703.10603 (2017). · arXiv
  47. Mazzaferro, C. Predicting protein binding affinity with word embeddings and recurrent neural networks. Preprint at https://www.biorxiv.org/content/early/2017/04/18/128223 (2017). · DOI
  48. Khurana, S. et al. DeepSol: a deep learning framework for sequence-based protein solubility prediction. Bioinformatics 34, 2605–2613 (2018). · DOI
  49. Dehouck, Y. et al. Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. Bioinformatics 25, 2537–2543 (2009). · DOI
  50. Giollo, M., Martin, A. J., Walsh, I., Ferrari, C. & Tosatto, S. C. NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation. BMC Genom. 15, S7 (2014). · DOI
  51. Almagro Armenteros, J. J., Sønderby, C. K., Sønderby, S. K., Nielsen, H. & Winther, O. DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics 33, 3387–3395 (2017). · DOI
  52. Sønderby, S. K. & Winther, O. Protein secondary structure prediction with long short term memory networks. Preprint at https://arxiv.org/abs/1412.7828 (2014). · arXiv
  53. Szalkai, B. & Grolmusz, V. Near perfect protein multi-label classification with deep neural networks. Methods 132, 50–56 (2018). · DOI
  54. Cao, R. et al. ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network. Molecules 22, 1732 (2017). · DOI
  55. Hopf, T. A. et al. Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149, 1607–1621 (2012). · DOI
  56. Domingos, P. A few useful things to know about machine learning. Commun. ACM 55, 78–87 (2012). · DOI · Paper page
  57. Bengio, Y., Courville, A. & Vincent, P. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013). · DOI · Paper page
  58. Kawashima, S. et al. AAindex: amino acid index database, progress report 2008. Nucleic Acids Res. 36, D202–D205 (2008). · DOI
  59. Ofer, D. & Linial, M. ProFET: feature engineering captures high-level protein functions. Bioinformatics 31, 3429–3436 (2015). · DOI
  60. Barley, M. H., Turner, N. J. & Goodacre, R. Improved descriptors for the quantitative structure–activity relationship modeling of peptides and proteins. J. Chem. Inf. Model. 58, 234–243 (2018). · DOI
  61. Qiu, J., Hue, M., Ben-Hur, A., Vert, J.-P. & Noble, W. S. A structural alignment kernel for protein structures. Bioinformatics 23, 1090–1098 (2007). · DOI
  62. Henikoff, S. & Henikoff, J. G. Amino acid substitution matrices from protein blocks. Proc. Natl Acad. Sci. USA 89, 10915–10919 (1992). · DOI · Paper page
  63. Asgari, E. & Mofrad, M. R. Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS One 10, e0141287 (2015). · DOI
  64. Ng, P. dna2vec: consistent vector representations of variable-length k-mers. Preprint at https://arxiv.org/abs/1701.06279 (2017). · arXiv
  65. Kimothi, D., Soni, A., Biyani, P. & Hogan, J. M. Distributed representations for biological sequence analysis. Preprint at https://arxiv.org/abs/1608.05949 (2016). · arXiv
  66. Yang, K. K., Wu, Z., Bedbrook, C. N. & Arnold, F. H. Learned protein embeddings for machine learning. Bioinformatics 34, 2642–2648 (2018). · DOI
  67. Schwartz, A. S. et al. Deep semantic protein representation for annotation, discovery, and engineering. Preprint at https://www.biorxiv.org/content/early/2018/07/10/365965 (2018). · DOI
  68. Yang, K. K., Chen, Y., Lee, A. & Yue, Y. Batched stochastic Bayesian optimization via combinatorial constraints design. Proc. Mach. Learn. Res. 89, 3410–3419 (2019).
  69. Srinivas, N., Krause, A., Kakade, S. M. & Seeger, M. Gaussian process optimization in the bandit setting: no regret and experimental design. In Proc. 27th International Conference on Machine Learning (eds. Fürnkranz, J. & Joachims, T.) 1015–1022 (Omnipress, 2010).
  70. Fox, R. et al. Optimizing the search algorithm for protein engineering by directed evolution. Protein Eng. 16, 589–597 (2003). This study is the first to use machine learning to guide directed evolution. · DOI
  71. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017). · DOI · Paper page
  72. Baker, D. An exciting but challenging road ahead for computational enzyme design. Protein Sci. 19, 1817–1819 (2010). · DOI
  73. Radford, A., Metz, L. & Chintala, S. Unsupervised representation learning with deep convolutional generative adversarial networks. Preprint at https://arxiv.org/abs/1511.06434 (2015). · arXiv
  74. Ha, D. & Eck, D. A neural representation of sketch drawings. Sixth International Conference on Learning Representations https://openreview.net/forum?id=Hy6GHpkCW (2018).
  75. Roberts, A., Engel, J., Raffel, C., Hawthorne, C. & Eck, D. A hierarchical latent vector model for learning long-term structure in music. Preprint at https://arxiv.org/abs/1803.05428 (2018). · arXiv
  76. Sinai, S., Kelsic, E., Church, G. M. & Nowak, M. A. Variational auto-encoding of protein sequences. Preprint at https://arxiv.org/abs/1712.03346 (2017). · arXiv
  77. Riesselman, A. J., Ingraham, J. B. & Marks, D. S. Deep generative models of genetic variation capture the effects of mutations. Nat. Methods 15, 816–822 (2018). This study predicts the effects of mutations without using any labeled data. · DOI · Paper page
  78. Costello, Z. & Garcia Martin, H. How to hallucinate functional proteins. Preprint at https://arxiv.org/abs/1903 (2019).
  79. Müller, A. T., Hiss, J. A. & Schneider, G. Recurrent neural network model for constructive peptide design. J. Chem. Inf. Model. 58, 472–479 (2018). · DOI · Paper page
  80. Gupta, A. & Zou, J. Feedback GAN (FBGAN) for DNA: a novel feedback-loop architecture for optimizing protein functions. Preprint at https://arxiv.org/abs/1804.01694 (2018). · arXiv
  81. Anand, N. & Huang, P. Generative modeling for protein structures. In Advances in Neural Information Processing Systems 31 (eds. Bengio, S. et al.) 7504–7515 (Curran Associates, 2018).
  82. Brookes, D. H. & Listgarten, J. Design by adaptive sampling. Preprint at https://arxiv.org/abs/1810.03714 (2018). · arXiv
  83. Brookes, D. H., Park, H. & Listgarten, J. Conditioning by adaptive sampling for robust design. Proc. Mach. Learn. Res. 97, 773–782 (2019).

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Authors
Yang, K. K., Wu, Z., & Arnold, F. H.
Venue
Nature Methods
Published
2019-07-15
Volume
16
Issue
8
Pages
687-694
ISSN
  • 1548-7091
  • 1548-7105
Cited by
1,130
References
16
Language
en

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