ScaffoldSeq: Software for characterization of directed evolution populations

Proteins: Structure, Function and Bioinformatics

Volumn 84, Issue 7, 2016, Pages 869-874

  Woldring, Daniel R ^a   Holec, Patrick V ^a   Hackel, Benjamin J ^a  

^a University of Minnesota   (United States)

Author keywords

bioinformatics; epistasis; family clustering; high throughput; sequence analysis; software

Indexed keywords

FIBRONECTIN; PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ANTIBODY SCREENING; ARTICLE; DIRECTED EVOLUTION; DNA SEQUENCE; EPISTASIS; EVOLUTION; GENE INTERACTION; GENETIC CODE; GENETIC PARAMETERS; HIGH THROUGHPUT SEQUENCING; NEXT GENERATION SEQUENCING; PHYLOGENETIC TREE; POPULATION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN FAMILY; PROTEIN INTERACTION; SEQUENCE ALIGNMENT; SOFTWARE; ANIMAL; CHEMISTRY; CLUSTER ANALYSIS; DIRECTED MOLECULAR EVOLUTION; GENETICS; HUMAN; PROCEDURES;

ALGORITHMS; ANIMALS; CLUSTER ANALYSIS; DIRECTED MOLECULAR EVOLUTION; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, DNA; SOFTWARE;

EID: 84963537683     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.25040     Document Type: Article

References (47)

1. Zemlin M, Klinger M, Link J, Zemlin C, Bauer K, Engler JA, Schroeder Jr HW, Kirkham PM. Expressed murine and human CDR-H3 intervals of equal length exhibit distinct repertoires that differ in their amino acid composition and predicted range of structures. J Mol Biol 2003;334:733–749.
2. DeKosky BJ, Ippolito GC, Deschner RP, Lavinder JJ, Wine Y, Rawlings BM, Varadarajan N, Giesecke C, Dörner T, Andrews SF, Wilson PC, Hunicke-Smith SP, Willson CG, Ellington AD, Georgiou G. High-throughput sequencing of the paired human immunoglobulin heavy and light chain repertoire. Nat Biotechnol 2013;31:166–169.
3. Reddy ST, Ge X, Miklos AE, Hughes RA, Kang SH, Hoi KH, Chrysostomou C, Hunicke-Smith SP, Iverson BL, Tucker PW, Ellington AD, Georgiou G. Monoclonal antibodies isolated without screening by analyzing the variable-gene repertoire of plasma cells. Nat Biotechnol 2010;28:965–969.
4. Tse E, Lobato MN, Forster A, Tanaka T, Chung GTY, Rabbitts TH. Intracellular antibody capture technology: application to selection of intracellular antibodies recognising the BCR-ABL oncogenic proteina. J Mol Biol 2002;317:85–94.
5. Steipe B, Schiller B, Pluckthun A, Steinbacher S. Sequence statistics reliably predict stabilizing mutations in a protein domain. J Mol Biol 1994;240:188–192.
6. Woldring DR, Holec PV, Zhou H, Hackel BJ. High-Throughput ligand discovery reveals a sitewise gradient of diversity in broadly evolved hydrophilic fibronectin domains. PLoS One 2015;10:e0138956.
7. Fowler DM, Fields S. Deep mutational scanning: a new style of protein science. Nat Methods 2014;11:801–807.
8. Tripathi A, Varadarajan R. Residue specific contributions to stability and activity inferred from saturation mutagenesis and deep sequencing. Curr Opin Struct Biol 2014;24:63–71.
9. Kruziki MA, Bhatnagar S, Woldring DR, Duong VT, Hackel BJ. A 45-Amino-acid scaffold mined from the PDB for high-affinity ligand engineering. Chem Biol 2015;22:946–956.
10. Fowler DM, Araya CL, Gerard W, Fields S. Enrich: software for analysis of protein function by enrichment and depletion of variants. Bioinformatics 2011;27:3430–3431.
11. Kim T, Tyndel MS, Huang H, Sidhu SS, Bader GD, Gfeller D, Kim PM. MUSI: an integrated system for identifying multiple specificity from very large peptide or nucleic acid data sets. Nucleic Acids Res 2012;40:e47.
12. Bloom JD. Software for the analysis and visualization of deep mutational scanning data. BMC Bioinformatics 2015;16:168.
13. Matochko WL, Chu K, Jin B, Lee SW, Whitesides GM, Derda R. Deep sequencing analysis of phage libraries using Illumina platform. Methods 2012;58:47–55.
14. Jolma A, Kivioja T, Toivonen J, Cheng L, Wei G, Enge M, Taipale M, Vaquerizas JM, Yan J, Sillanpää MJ, Bonke M, Palin K, Talukder S, Hughes TR, Luscombe NM, Ukkonen E, Taipale J. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res 2010;20:861–873.
15. Alam KK, Chang JL, Burke DH. FASTAptamer: a bioinformatic toolkit for high-throughput sequence analysis of combinatorial selections. Mol Ther Acids 2015; 4:e230.
16. Ravn U, Didelot G, Venet S, Ng KT, Gueneau F, Rousseau F, Calloud S, Kosco-Vilbois M, Fischer N. Deep sequencing of phage display libraries to support antibody discovery. Methods 2013;60:99–110.
17. Dickson RJ, Gloor GB. Bioinformatics identification of coevolving residues. Methods Mol Biol 2014;1123:223–243.
18. Feldwisch J, Tolmachev V, Lendel C, Herne N, Sjöberg A, Larsson B, Rosik D, Lindqvist E, Fant G, Höidén-Guthenberg I, Galli J, Jonasson P, Abrahmsén L. Design of an optimized scaffold for affibody molecules. J Mol Biol 2010;398:232–247.
19. Binz HK, Stumpp MT, Forrer P, Amstutz P, Plückthun A. Designing repeat proteins: well-expressed, soluble and stable proteins from combinatorial libraries of consensus ankyrin repeat proteins. J Mol Biol 2003;332:489–503.
20. Getz JA, Rice JJ, Daugherty PS. Protease-resistant peptide ligands from a knottin scaffold library. ACS Chem Biol 2011;6:837–844.
21. Mahon CM, Lambert MA, Glanville J, Wade JM, Fennell BJ, Krebs MR, Armellino D, Yang S, Liu X, O'Sullivan CM, Autin B, Oficjalska K, Bloom L, Paulsen J, Gill D, Damelin M, Cunningham O, Finlay WJ. Comprehensive interrogation of a minimalist synthetic CDR-H3 library and its ability to generate antibodies with therapeutic potential. J Mol Biol 2013;425:1712–1730.
22. Lamminmäki U, Paupério S, Westerlund-Karlsson A, Karvinen J, Virtanen PL, Lövgren T, Saviranta P. Expanding the conformational diversity by random insertions to CDRH2 results in improved anti-estradiol antibodies. J Mol Biol 1999;291:589–602.
23. Hackel BJ, Kapila A, Wittrup KD. Picomolar affinity fibronectin domains engineered utilizing loop length diversity, recursive mutagenesis, and loop shuffling. J Mol Biol 2008;381:1238–1252.
24. Parmley SF, Smith GP. Antibody-selectable filamentous fd phage vectors: affinity purification of target genes. Gene 1988;73:305–318.
25. Seelig B. mRNA display for the selection and evolution of enzymes from in vitro-translated protein libraries. Nat Protoc 2011;6:540–552.
26. Mattheakis LC, Bhatt RR, Dower WJ. An in vitro polysome display system for identifying ligands from very large peptide libraries. Proc Natl Acad Sci USA 1994;91:9022–9026.
27. Boder ET, Wittrup KD. Yeast surface display for screening combinatorial polypeptide libraries. Nat Biotechnol 1997;15:553–557.
28. Marks JD, Ouwehand WH, Bye JM, Finnern R, Gorick BD, Voak D, Thorpe SJ, Hughes-Jones NC, Winter G. Human antibody fragments specific for human blood group antigens from a phage display library. Bio/Technology 1993;11:1145–1149.
29. Ackerman M, Levary D, Tobon G, Hackel B, Orcutt KD, Wittrup KD. Highly avid magnetic bead capture: an efficient selection method for de novo protein engineering utilizing yeast surface display. Biotechnol Prog 2009;25:774–783.
30. Chao G, Lau WL, Hackel BJ, Sazinsky SL, Lippow SM, Wittrup KD. Isolating and engineering human antibodies using yeast surface display. Nat Protoc 2006;1:755–768.
31. Quail M, Smith ME, Coupland P, Otto TD, Harris SR, Connor TR, Bertoni A, Swerdlow HP, Gu Y. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 2012;13:1.
32. Morcos F, Pagnani A, Lunt B, Bertolino A, Marks DS, Sander C, Zecchina R, Onuchic JN, Hwa T, Weigt M. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc Natl Acad Sci USA 2011;108:E1293–E1301.
33. Hobohm U, Scharf M, Schneider R, Sander C. Selection of representative protein data sets. Protein Sci 1992;1:409–417.
34. Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948;27:623–656.
35. Hinkley T, Martins J, Chappey C, Haddad M, Stawiski E, Whitcomb JM, Petropoulos CJ, Bonhoeffer S. A systems analysis of mutational effects in HIV-1 protease and reverse transcriptase. Nat Genet 2011;43:487–489.
36. Gong LI, Bloom JD. Epistatically interacting substitutions are enriched during adaptive protein evolution. PLoS Genet 2014;10:e1004328.
37. de Juan D, Pazos F, Valencia A. Emerging methods in protein co-evolution. Nat Rev Genet 2013;14:249–261.
38. Martin LC, Gloor GB, Dunn SD, Wahl LM. Using information theory to search for co-evolving residues in proteins. Bioinformatics 2005;21:4116–4124.
39. Fodor AA, Aldrich RW. Influence of conservation on calculations of amino acid covariance in multiple sequence alignments. Proteins Struct Funct Genet 2004;56:211–221.
40. Brown CA, Brown KS. Validation of coevolving residue algorithms via pipeline sensitivity analysis: ELSC and OMES and ZNMI, oh my!. PLoS One 2010;5:e10779.
41. Durani V, Magliery TJ. Protein engineering and stabilization from sequence statistics: variation and covariation analysis., Methods Enzymol 2013;523:237–256.
42. Little DY, Chen L. Identification of coevolving residues and coevolution potentials emphasizing structure, bond formation and catalytic coordination in protein evolution. Shiu S-H, Ed. PLoS One 2009;4:e4762
43. Dunn SD, Wahl LM, Gloor GB. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics 2008;24:333–340.
44. Buslje CM, Santos J, Delfino JM, Nielsen M. Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information. Bioinformatics 2009;25:1125–1131.
45. Atchley WR, Wollenberg KR, Fitch WM, Terhalle W, Dress AW. Correlations among amino acid sites in bHLH protein domains: an information theoretic analysis. Mol Biol Evol 2000;17:164–178.
46. Woldring DR, Holec P V, Hackel BJ. Hackel Lab GitHub. April 5, 2016. Available at: https://github.com/HackelLab-UMN.
47. Styczynski MP, Jensen KL, Rigoutsos I, Stephanopoulos G. BLOSUM62 miscalculations improve search performance. Nat Biotechnol 2008;26:274–275.