Mutation effects predicted from sequence co-variation

Nature Biotechnology

Volumn 35, Issue 2, 2017, Pages 128-135

  Hopf, Thomas A ^a,b   Ingraham, John B ^a   Poelwijk, Frank J ^c   Schärfe, Charlotta P I ^a,d   Springer, Michael ^a   Sander, Chris ^a,c   Marks, Debora S ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^c DANA FARBER CANCER INSTITUTE   (United States)

^d UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

GENES; THROUGHPUT;

FUNCTIONAL ASSAYS; GENETIC VARIATION; MUTAGENESIS EXPERIMENT; MUTATION EFFECTS; PREDICTION METHODS; QUANTITATIVE EFFECTS; SEQUENCE CONSERVATION; SYSTEMATIC TESTING;

FORECASTING;

ARTICLE; EPISTASIS; GENE FREQUENCY; GENE MUTATION; GENE SEQUENCE; GENETIC CODE; GENETIC VARIATION; HUMAN; MUTAGENESIS; PREDICTION; PRIORITY JOURNAL; PROTEIN INTERACTION; QUANTITATIVE ANALYSIS; RNA RECOGNITION MOTIF; RNA STRUCTURE; SEQUENCE ALIGNMENT; STATISTICAL MODEL; AMINO ACID SEQUENCE; CHEMISTRY; CONSERVED SEQUENCE; DNA MUTATIONAL ANALYSIS; GENETICS; HIGH THROUGHPUT SEQUENCING; MOLECULAR EVOLUTION; MOLECULAR GENETICS; MUTATION; PROCEDURES;

PROTEOME;

AMINO ACID SEQUENCE; CONSERVED SEQUENCE; DNA MUTATIONAL ANALYSIS; EPISTASIS, GENETIC; EVOLUTION, MOLECULAR; GENETIC VARIATION; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MOLECULAR SEQUENCE DATA; MUTATION; PROTEOME;

EID: 85011967183     PISSN: 10870156     EISSN: 15461696     Source Type: Journal    
DOI: 10.1038/nbt.3769     Document Type: Article

References (80)

1. Miersch, S. &Sidhu, S.S. Intracellular targeting with engineered proteins. F1000Res. 5 http://dx.doi.org/10.12688/f1000research.8915.1 (2016).
2. Boeke, J.D., et al. GENOME ENGINEERING. The Genome Project-Write. Science 353, 126-127 (2016).
3. Ostrov, N. et al. Design, synthesis, and testing toward a 57-codon genome. Science 353, 819-822 (2016).
4. Romero, P.A., Tran, T.M. &Abate, A.R. Dissecting enzyme function with microfluidic-based deep mutational scanning. Proc. Natl. Acad. Sci. USA 112, 7159-7164 (2015).
5. Currin, A., Swainston, N., Day, P.J. &Kell, D.B. Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently. Chem. Soc. Rev. 44, 1172-1239 (2015).
6. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285-291 (2016).
7. Roscoe, B.P. &Bolon, D.N. Systematic exploration of ubiquitin sequence, E1 activation efficiency, and experimental fitness in yeast. J. Mol. Biol. 426, 2854-2870 (2014).
8. Roscoe, B.P., Thayer, K.M., Zeldovich, K.B., Fushman, D. &Bolon, D.N. Analyses of the effects of all ubiquitin point mutants on yeast growth rate. J. Mol. Biol. 425, 1363-1377 (2013).
9. Melamed, D., Young, D.L., Gamble, C.E., Miller, C.R. &Fields, S. Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein. RNA 19, 1537-1551 (2013).
10. Stiffler, M.A., Hekstra, D.R. &Ranganathan, R. Evolvability as a function of purifying selection in TEM-1 β-lactamase. Cell 160, 882-892 (2015).
11. McLaughlin, R.N. Jr., Poelwijk, F.J., Raman, A., Gosal, W.S. &Ranganathan, R. The spatial architecture of protein function and adaptation. Nature 491, 138-142 (2012).
12. Kitzman, J.O., Starita, L.M., Lo, R.S., Fields, S. &Shendure, J. Massively parallel single-amino-acid mutagenesis. Nat. Methods 12, 203-206, 4, 206 (2015).
13. Melnikov, A., Rogov, P., Wang, L., Gnirke, A. &Mikkelsen, T.S. Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes. Nucleic Acids Res. 42, e112 (2014).
14. Araya, C.L. et al. A fundamental protein property, thermodynamic stability, revealed solely from large-scale measurements of protein function. Proc. Natl. Acad. Sci. USA 109, 16858-16863 (2012).
15. Firnberg, E., Labonte, J.W., Gray, J.J. &Ostermeier, M. A comprehensive, high-resolution map of a gene's fitness landscape. Mol. Biol. Evol. 31, 1581-1592 (2014).
16. Starita, L.M. et al. Massively parallel functional analysis of BRCA1 RING domain variants. Genetics 200, 413-422 (2015).
17. Rockah-Shmuel, L., Tóth-Petróczy, Á. &Tawfik, D.S. Systematic mapping of protein mutational space by prolonged drift reveals the deleterious effects of seemingly neutral mutations. PLoS Comput. Biol. 11, e1004421 (2015).
18. Jacquier, H. et al. Capturing the mutational landscape of the beta-lactamase TEM-1. Proc. Natl. Acad. Sci. USA 110, 13067-13072 (2013).
19. Qi, H. et al. A quantitative high-resolution genetic profile rapidly identifies sequence determinants of hepatitis C viral fitness and drug sensitivity. PLoS Pathog. 10, e1004064 (2014).
20. Wu, N.C. et al. Functional constraint profiling of a viral protein reveals discordance of evolutionary conservation and functionality. PLoS Genet. 11, e1005310 (2015).
21. Mishra, P., Flynn, J.M., Starr, T.N. &Bolon, D.N. Systematic mutant analyses elucidate general and client-specific aspects of Hsp90 function. Cell Rep. 15, 588-598 (2016).
22. Doud, M.B. &Bloom, J.D. Accurate measurement of the effects of all amino-acid mutations to influenza hemagglutinin. bioRxiv 8, E155 (2016).
23. Deng, Z. et al. Deep sequencing of systematic combinatorial libraries reveals β-lactamase sequence constraints at high resolution. J. Mol. Biol. 424, 150-167 (2012).
24. Starita, L.M. et al. Activity-enhancing mutations in an E3 ubiquitin ligase identified by high-throughput mutagenesis. Proc. Natl. Acad. Sci. USA 110, E1263-E1272 (2013).
25. Aakre, C.D. et al. Evolving new protein-protein interaction specificity through promiscuous intermediates. Cell 163, 594-606 (2015).
26. Julien, P., Miñana, B., Baeza-Centurion, P., Valcárcel, J. &Lehner, B. The complete local genotype-phenotype landscape for the alternative splicing of a human exon. Nat. Commun. 7, 11558 (2016).
27. Li, C., Qian, W., Maclean, C.J. &Zhang, J. The fitness landscape of a tRNA gene. Science 352, 837-840 (2016).
28. Fowler, D.M. &Fields, S. Deep mutational scanning: a new style of protein science. Nat. Methods 11, 801-807 (2014).
29. Gasperini, M., Starita, L. &Shendure, J. The power of multiplexed functional analysis of genetic variants. Nat. Protoc. 11, 1782-1787 (2016).
30. Sarkisyan, K.S. et al. Local fitness landscape of the green fluorescent protein. Nature 533, 397-401 (2016).
31. Boucher, J.I., Bolon, D.N. &Tawfik, D.S. Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature. Protein Sci. 25, 1219-1226 (2016).
32. Gong, L.I., Suchard, M.A. &Bloom, J.D. Stability-mediated epistasis constrains the evolution of an influenza protein. eLife 2, e00631 (2013).
33. Kachroo, A.H. et al. Evolution. Systematic humanization of yeast genes reveals conserved functions and genetic modularity. Science 348, 921-925 (2015).
34. Sim, N.L. et al. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 40, W452 (2012).
35. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310-315 (2014).
36. Adzhubei, I., Jordan, D.M. &Sunyaev, S.R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 76, 7.20 (2013).
37. Breen, M.S., Kemena, C., Vlasov, P.K., Notredame, C. &Kondrashov, F.A. Epistasis as the primary factor in molecular evolution. Nature 490, 535-538 (2012).
38. McCandlish, D.M., Shah, P. &Plotkin, J.B. Epistasis and the dynamics of reversion in molecular evolution. Genetics 203, 1335-1351 (2016).
39. Ovchinnikov, S., Kamisetty, H. &Baker, D. Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information. eLife 3, e02030 (2014).
40. Hopf, T.A. et al. Sequence co-evolution gives 3D contacts and structures of protein complexes. eLife 3 http://dx.doi.org/10.7554/eLife.03430 (2014).
41. Hopf, T.A., et al. Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149, 1607-1621 (2012).
42. Marks, D.S., Hopf, T.A. &Sander, C. Protein structure prediction from sequence variation. Nat. Biotechnol. 30, 1072-1080 (2012).
43. Marks, D.S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS One 6, e28766 (2011).
44. Morcos, F. et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc. Natl. Acad. Sci. USA 108, E1293-E1301 (2011).
45. Jones, D.T., Buchan, D.W., Cozzetto, D. &Pontil, M. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics 28, 184-190 (2012).
46. Mann, J.K. et al. The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing. PLoS Comput. Biol. 10, e1003776 (2014).
47. Lapedes, A., Giraud, B. &Jarzynski, C. Using sequence alignments to predict protein structure and stability with high accuracy. Preprint at https://arxiv.org/pdf/1207.2484v1.pdf (2012).
48. Figliuzzi, M., Jacquier, H., Schug, A., Tenaillon, O. &Weigt, M. Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1. Mol. Biol. Evol. 33, 268-280 (2016).
49. Sella, G. &Hirsh, A.E. The application of statistical physics to evolutionary biology. Proc. Natl. Acad. Sci. USA 102, 9541-9546 (2005).
50. Giraud, B.G., Heumann, J.M. &Lapedes, A.S. Superadditive correlation. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Topics 59, 4983-4991 (1999).
51. Ovchinnikov, S. et al. Large-scale determination of previously unsolved protein structures using evolutionary information. eLife 4, e09248 (2015).
52. Kosciolek, T. &Jones, D.T. De novo structure prediction of globular proteins aided by sequence variation-derived contacts. PLoS One 9, e92197 (2014).
53. Besag, J. Statistical analysis of non-lattice data. Statistician 24, 179-195 (1975).
54. Balakrishnan, S., Kamisetty, H., Carbonell, J.G., Lee, S.I. &Langmead, C.J. Learning generative models for protein fold families. Proteins 79, 1061-1078 (2011).
55. Kamisetty, H., Ovchinnikov, S. &Baker, D. Assessing the utility of coevolution-based residue-residue contact predictions in a sequence-and structure-rich era. Proc. Natl. Acad. Sci. USA 110, 15674-15679 (2013).
56. Ekeberg, M., Lövkvist, C., Lan, Y., Weigt, M. &Aurell, E. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. Phys. Rev. E 87, 012707 (2013).
57. Di Nardo, A.A., Larson, S.M. &Davidson, A.R. The relationship between conservation, thermodynamic stability, and function in the SH3 domain hydrophobic core. J. Mol. Biol. 333, 641-655 (2003).
58. Halabi, N., Rivoire, O., Leibler, S. &Ranganathan, R. Protein sectors: evolutionary units of three-dimensional structure. Cell 138, 774-786 (2009).
59. Philip, A.F., Kumauchi, M. &Hoff, W.D. Robustness and evolvability in the functional anatomy of a PER-ARNT-SIM (PAS) domain. Proc. Natl. Acad. Sci. USA 107, 17986-17991 (2010).
60. Bershtein, S., Mu, W. &Shakhnovich, E.I. Soluble oligomerization provides a beneficial fitness effect on destabilizing mutations. Proc. Natl. Acad. Sci. USA 109, 4857-4862 (2012).
61. Landrum, M.J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D1, D862-D868 (2016).
62. Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248-249 (2010).
63. Capriotti, E., Calabrese, R. &Casadio, R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 22, 2729-2734 (2006).
64. Grimm, D.G. et al. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Hum. Mutat. 36, 513-523 (2015).
65. Bromberg, Y., Yachdav, G. &Rost, B. SNAP predicts effect of mutations on protein function. Bioinformatics 24, 2397-2398 (2008).
66. van Nimwegen, E. Inferring contacting residues within and between proteins: what do the probabilities mean? PLoS Comput. Biol. 12, e1004726 (2016).
67. Eddy, S.R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).
68. Suzek, B.E., Wang, Y., Huang, H., McGarvey, P.B. &Wu, C.H. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926-932 (2015).
69. Nawrocki, E.P. et al. Rfam 12.0: updates to the RNA families database. Nucleic Acids Res. 43, D130-D137 (2015).
70. Jaynes, E.T. Information theory and statistical mechanics. Phys. Rev. 106, 620 (1957).
71. Dunn, S.D., Wahl, L.M. &Gloor, G.B. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics 24, 333-340 (2008).
72. Toth-Petroczy, A. et al. Structured states of disordered proteins from genomic sequences. Cell 167, 158-170.e12 (2016).
73. Reva, B., Antipin, Y. &Sander, C. Determinants of protein function revealed by combinatorial entropy optimization. Genome Biol. 8, R232 (2007).
74. Kosorok, M.R. Brownian distance covariance and high dimensional data. Ann. Appl. Stat. 3, 1266-1269 (2009).
75. Matthews, B.W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405, 442-451 (1975).
76. Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).
77. Berman, H.M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235-242 (2000).
78. Pérez, F. &Granger, B.E. IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9, 21-29 (2007).
79. Van der Walt, S., Colbert, S.C. &Varoquaux, G. The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13, 22-30 (2011).
80. Hunter, J.D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90-95 (2007).