[{"day":"14","month":"12","article_processing_charge":"No","date_published":"2016-12-14T00:00:00Z","doi":"10.6084/m9.figshare.4315652.v1","oa":1,"main_file_link":[{"url":"https://doi.org/10.6084/m9.figshare.4315652.v1","open_access":"1"}],"citation":{"chicago":"Fernandes Redondo, Rodrigo A, Harold de Vladar, Tomasz Włodarski, and Jonathan P Bollback. “Data from Evolutionary Interplay between Structure, Energy and Epistasis in the Coat Protein of the ΦX174 Phage Family.” The Royal Society, 2016. https://doi.org/10.6084/m9.figshare.4315652.v1.","short":"R.A. Fernandes Redondo, H. de Vladar, T. Włodarski, J.P. Bollback, (2016).","mla":"Fernandes Redondo, Rodrigo A., et al. Data from Evolutionary Interplay between Structure, Energy and Epistasis in the Coat Protein of the ΦX174 Phage Family. The Royal Society, 2016, doi:10.6084/m9.figshare.4315652.v1.","ieee":"R. A. Fernandes Redondo, H. de Vladar, T. Włodarski, and J. P. Bollback, “Data from evolutionary interplay between structure, energy and epistasis in the coat protein of the ϕX174 phage family.” The Royal Society, 2016.","apa":"Fernandes Redondo, R. A., de Vladar, H., Włodarski, T., & Bollback, J. P. (2016). Data from evolutionary interplay between structure, energy and epistasis in the coat protein of the ϕX174 phage family. The Royal Society. https://doi.org/10.6084/m9.figshare.4315652.v1","ista":"Fernandes Redondo RA, de Vladar H, Włodarski T, Bollback JP. 2016. Data from evolutionary interplay between structure, energy and epistasis in the coat protein of the ϕX174 phage family, The Royal Society, 10.6084/m9.figshare.4315652.v1.","ama":"Fernandes Redondo RA, de Vladar H, Włodarski T, Bollback JP. Data from evolutionary interplay between structure, energy and epistasis in the coat protein of the ϕX174 phage family. 2016. doi:10.6084/m9.figshare.4315652.v1"},"abstract":[{"text":"Viral capsids are structurally constrained by interactions among the amino acids (AAs) of their constituent proteins. Therefore, epistasis is expected to evolve among physically interacting sites and to influence the rates of substitution. To study the evolution of epistasis, we focused on the major structural protein of the ϕX174 phage family by, first, reconstructing the ancestral protein sequences of 18 species using a Bayesian statistical framework. The inferred ancestral reconstruction differed at eight AAs, for a total of 256 possible ancestral haplotypes. For each ancestral haplotype and the extant species, we estimated, in silico, the distribution of free energies and epistasis of the capsid structure. We found that free energy has not significantly increased but epistasis has. We decomposed epistasis up to fifth order and found that higher-order epistasis sometimes compensates pairwise interactions making the free energy seem additive. The dN/dS ratio is low, suggesting strong purifying selection, and that structure is under stabilizing selection. We synthesized phages carrying ancestral haplotypes of the coat protein gene and measured their fitness experimentally. Our findings indicate that stabilizing mutations can have higher fitness, and that fitness optima do not necessarily coincide with energy minima.","lang":"eng"}],"type":"research_data_reference","date_updated":"2023-09-20T11:56:33Z","date_created":"2021-08-10T08:29:47Z","oa_version":"Published Version","author":[{"orcid":"0000-0002-5837-2793","id":"409D5C96-F248-11E8-B48F-1D18A9856A87","last_name":"Fernandes Redondo","first_name":"Rodrigo A","full_name":"Fernandes Redondo, Rodrigo A"},{"orcid":"0000-0002-5985-7653","id":"2A181218-F248-11E8-B48F-1D18A9856A87","last_name":"de Vladar","first_name":"Harold","full_name":"de Vladar, Harold"},{"full_name":"Włodarski, Tomasz","last_name":"Włodarski","first_name":"Tomasz"},{"first_name":"Jonathan P","last_name":"Bollback","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4624-4612","full_name":"Bollback, Jonathan P"}],"related_material":{"record":[{"id":"1077","relation":"used_in_publication","status":"public"}]},"title":"Data from evolutionary interplay between structure, energy and epistasis in the coat protein of the ϕX174 phage family","status":"public","publisher":"The Royal Society","department":[{"_id":"NiBa"},{"_id":"JoBo"}],"_id":"9864","year":"2016","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf"},{"keyword":["RNAP binding","de novo promoter evolution","lac promoter"],"day":"12","month":"05","article_processing_charge":"No","has_accepted_license":"1","citation":{"ama":"Tugrul M. Experimental Data for Binding Site Evolution of Bacterial RNA Polymerase. 2016. doi:10.15479/AT:ISTA:43","ieee":"M. Tugrul, “Experimental Data for Binding Site Evolution of Bacterial RNA Polymerase.” Institute of Science and Technology Austria, 2016.","apa":"Tugrul, M. (2016). Experimental Data for Binding Site Evolution of Bacterial RNA Polymerase. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:43","ista":"Tugrul M. 2016. Experimental Data for Binding Site Evolution of Bacterial RNA Polymerase, Institute of Science and Technology Austria, 10.15479/AT:ISTA:43.","short":"M. Tugrul, (2016).","mla":"Tugrul, Murat. Experimental Data for Binding Site Evolution of Bacterial RNA Polymerase. Institute of Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:43.","chicago":"Tugrul, Murat. “Experimental Data for Binding Site Evolution of Bacterial RNA Polymerase.” Institute of Science and Technology Austria, 2016. https://doi.org/10.15479/AT:ISTA:43."},"tmp":{"short":"CC0 (1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"oa":1,"doi":"10.15479/AT:ISTA:43","date_published":"2016-05-12T00:00:00Z","datarep_id":"43","type":"research_data","file_date_updated":"2020-07-14T12:47:01Z","abstract":[{"text":"The data stored here is used in Murat Tugrul's PhD thesis (Chapter 3), which is related to the evolution of bacterial RNA polymerase binding.\r\nMagdalena Steinrueck (PhD Student in Calin Guet's group at IST Austria) performed the experiments and created the data on de novo promoter evolution. Fabienne Jesse (PhD Student in Jon Bollback's group at IST Austria) performed the experiments and created the data on lac promoter evolution.","lang":"eng"}],"license":"https://creativecommons.org/publicdomain/zero/1.0/","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"5554","year":"2016","title":"Experimental Data for Binding Site Evolution of Bacterial RNA Polymerase","status":"public","department":[{"_id":"NiBa"},{"_id":"JoBo"}],"publisher":"Institute of Science and Technology Austria","author":[{"last_name":"Tugrul","first_name":"Murat","orcid":"0000-0002-8523-0758","id":"37C323C6-F248-11E8-B48F-1D18A9856A87","full_name":"Tugrul, Murat"}],"related_material":{"record":[{"id":"1131","relation":"used_in_publication","status":"public"}]},"contributor":[{"id":"2C023F40-F248-11E8-B48F-1D18A9856A87","last_name":"Steinrück","contributor_type":"researcher","first_name":"Magdalena"},{"first_name":"Fabienne","last_name":"Jesse","contributor_type":"researcher","id":"4C8C26A4-F248-11E8-B48F-1D18A9856A87"}],"date_updated":"2024-02-21T13:50:34Z","date_created":"2018-12-12T12:31:30Z","file":[{"file_size":1123495,"content_type":"application/zip","creator":"system","access_level":"open_access","file_name":"IST-2016-43-v1+1_DATA_MTugrul_PhDThesis_Chapter3.zip","checksum":"1fc0a10bb7ce110fcb5e1fbe3cf0c4e2","date_created":"2018-12-12T13:03:08Z","date_updated":"2020-07-14T12:47:01Z","relation":"main_file","file_id":"5626"}],"oa_version":"Published Version"},{"publication_identifier":{"eissn":["1537-1719"],"issn":["0737-4038"]},"month":"01","external_id":{"pmid":["24170494"]},"quality_controlled":"1","doi":"10.1093/molbev/mst187","language":[{"iso":"eng"}],"publist_id":"5193","pmid":1,"year":"2014","publisher":"Oxford University Press","department":[{"_id":"JoBo"}],"publication_status":"published","author":[{"full_name":"Hall, Barry","first_name":"Barry","last_name":"Hall"},{"full_name":"Acar, Hande","id":"2DDF136A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1986-9753","first_name":"Hande","last_name":"Acar"},{"last_name":"Nandipati","first_name":"Anna","full_name":"Nandipati, Anna"},{"full_name":"Barlow, Miriam","last_name":"Barlow","first_name":"Miriam"}],"volume":31,"date_created":"2018-12-11T11:54:37Z","date_updated":"2022-06-07T11:08:13Z","scopus_import":"1","article_processing_charge":"No","day":"01","citation":{"ama":"Hall B, Acar H, Nandipati A, Barlow M. Growth rates made easy. Molecular Biology and Evolution. 2014;31(1):232-238. doi:10.1093/molbev/mst187","ista":"Hall B, Acar H, Nandipati A, Barlow M. 2014. Growth rates made easy. Molecular Biology and Evolution. 31(1), 232–238.","apa":"Hall, B., Acar, H., Nandipati, A., & Barlow, M. (2014). Growth rates made easy. Molecular Biology and Evolution. Oxford University Press. https://doi.org/10.1093/molbev/mst187","ieee":"B. Hall, H. Acar, A. Nandipati, and M. Barlow, “Growth rates made easy,” Molecular Biology and Evolution, vol. 31, no. 1. Oxford University Press, pp. 232–238, 2014.","mla":"Hall, Barry, et al. “Growth Rates Made Easy.” Molecular Biology and Evolution, vol. 31, no. 1, Oxford University Press, 2014, pp. 232–38, doi:10.1093/molbev/mst187.","short":"B. Hall, H. Acar, A. Nandipati, M. Barlow, Molecular Biology and Evolution 31 (2014) 232–238.","chicago":"Hall, Barry, Hande Acar, Anna Nandipati, and Miriam Barlow. “Growth Rates Made Easy.” Molecular Biology and Evolution. Oxford University Press, 2014. https://doi.org/10.1093/molbev/mst187."},"publication":"Molecular Biology and Evolution","page":"232 - 238","article_type":"original","date_published":"2014-01-01T00:00:00Z","type":"journal_article","issue":"1","abstract":[{"lang":"eng","text":"In the 1960s-1980s, determination of bacterial growth rates was an important tool in microbial genetics, biochemistry, molecular biology, and microbial physiology. The exciting technical developments of the 1990s and the 2000s eclipsed that tool; as a result, many investigators today lack experience with growth rate measurements. Recently, investigators in a number of areas have started to use measurements of bacterial growth rates for a variety of purposes. Those measurements have been greatly facilitated by the availability of microwell plate readers that permit the simultaneous measurements on up to 384 different cultures. Only the exponential (logarithmic) portions of the resulting growth curves are useful for determining growth rates, and manual determination of that portion and calculation of growth rates can be tedious for high-throughput purposes. Here, we introduce the program GrowthRates that uses plate reader output files to automatically determine the exponential portion of the curve and to automatically calculate the growth rate, the maximum culture density, and the duration of the growth lag phase. GrowthRates is freely available for Macintosh, Windows, and Linux.We discuss the effects of culture volume, the classical bacterial growth curve, and the differences between determinations in rich media and minimal (mineral salts) media. This protocol covers calibration of the plate reader, growth of culture inocula for both rich and minimal media, and experimental setup. As a guide to reliability, we report typical day-to-day variation in growth rates and variation within experiments with respect to position of wells within the plates."}],"_id":"1902","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 31","status":"public","title":"Growth rates made easy","oa_version":"None"},{"oa":1,"tmp":{"short":"CC0 (1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"quality_controlled":"1","doi":"10.1186/1471-2164-15-663","language":[{"iso":"eng"}],"month":"08","year":"2014","department":[{"_id":"JoBo"}],"publisher":"BioMed Central","publication_status":"published","author":[{"full_name":"Kupczok, Anne","id":"2BB22BC2-F248-11E8-B48F-1D18A9856A87","first_name":"Anne","last_name":"Kupczok"},{"full_name":"Bollback, Jonathan P","first_name":"Jonathan P","last_name":"Bollback","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4624-4612"}],"volume":15,"date_created":"2018-12-11T11:55:23Z","date_updated":"2021-01-12T06:54:56Z","article_number":"663","publist_id":"5009","file_date_updated":"2020-07-14T12:45:26Z","citation":{"chicago":"Kupczok, Anne, and Jonathan P Bollback. “Motif Depletion in Bacteriophages Infecting Hosts with CRISPR Systems.” BMC Genomics. BioMed Central, 2014. https://doi.org/10.1186/1471-2164-15-663.","mla":"Kupczok, Anne, and Jonathan P. Bollback. “Motif Depletion in Bacteriophages Infecting Hosts with CRISPR Systems.” BMC Genomics, vol. 15, no. 1, 663, BioMed Central, 2014, doi:10.1186/1471-2164-15-663.","short":"A. Kupczok, J.P. Bollback, BMC Genomics 15 (2014).","ista":"Kupczok A, Bollback JP. 2014. Motif depletion in bacteriophages infecting hosts with CRISPR systems. BMC Genomics. 15(1), 663.","ieee":"A. Kupczok and J. P. Bollback, “Motif depletion in bacteriophages infecting hosts with CRISPR systems,” BMC Genomics, vol. 15, no. 1. BioMed Central, 2014.","apa":"Kupczok, A., & Bollback, J. P. (2014). Motif depletion in bacteriophages infecting hosts with CRISPR systems. BMC Genomics. BioMed Central. https://doi.org/10.1186/1471-2164-15-663","ama":"Kupczok A, Bollback JP. Motif depletion in bacteriophages infecting hosts with CRISPR systems. BMC Genomics. 2014;15(1). doi:10.1186/1471-2164-15-663"},"publication":"BMC Genomics","date_published":"2014-08-08T00:00:00Z","scopus_import":1,"has_accepted_license":"1","day":"08","_id":"2042","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","intvolume":" 15","ddc":["570"],"status":"public","title":"Motif depletion in bacteriophages infecting hosts with CRISPR systems","pubrep_id":"396","file":[{"access_level":"open_access","file_name":"IST-2015-396-v1+1_1471-2164-15-663.pdf","content_type":"application/pdf","file_size":1489769,"creator":"system","relation":"main_file","file_id":"4878","checksum":"3f6d2776b90a842a28359cc957d3d04b","date_updated":"2020-07-14T12:45:26Z","date_created":"2018-12-12T10:11:24Z"}],"oa_version":"Published Version","type":"journal_article","issue":"1","abstract":[{"text":"Background: CRISPR is a microbial immune system likely to be involved in host-parasite coevolution. It functions using target sequences encoded by the bacterial genome, which interfere with invading nucleic acids using a homology-dependent system. The system also requires protospacer associated motifs (PAMs), short motifs close to the target sequence that are required for interference in CRISPR types I and II. Here, we investigate whether PAMs are depleted in phage genomes due to selection pressure to escape recognition.Results: To this end, we analyzed two data sets. Phages infecting all bacterial hosts were analyzed first, followed by a detailed analysis of phages infecting the genus Streptococcus, where PAMs are best understood. We use two different measures of motif underrepresentation that control for codon bias and the frequency of submotifs. We compare phages infecting species with a particular CRISPR type to those infecting species without that type. Since only known PAMs were investigated, the analysis is restricted to CRISPR types I-C and I-E and in Streptococcus to types I-C and II. We found evidence for PAM depletion in Streptococcus phages infecting hosts with CRISPR type I-C, in Vibrio phages infecting hosts with CRISPR type I-E and in Streptococcus thermopilus phages infecting hosts with type II-A, known as CRISPR3.Conclusions: The observed motif depletion in phages with hosts having CRISPR can be attributed to selection rather than to mutational bias, as mutational bias should affect the phages of all hosts. This observation implies that the CRISPR system has been efficient in the groups discussed here.","lang":"eng"}]},{"quality_controlled":"1","oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"language":[{"iso":"eng"}],"doi":"10.1186/1471-2148-13-54","month":"02","publication_status":"published","publisher":"BioMed Central","department":[{"_id":"JoBo"}],"year":"2013","date_created":"2018-12-11T11:57:31Z","date_updated":"2021-01-12T06:57:20Z","volume":13,"author":[{"full_name":"Kupczok, Anne","first_name":"Anne","last_name":"Kupczok","id":"2BB22BC2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Bollback, Jonathan P","last_name":"Bollback","first_name":"Jonathan P","orcid":"0000-0002-4624-4612","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87"}],"file_date_updated":"2020-07-14T12:45:40Z","publist_id":"4514","page":"54 - 54","publication":"BMC Evolutionary Biology","citation":{"chicago":"Kupczok, Anne, and Jonathan P Bollback. “Probabilistic Models for CRISPR Spacer Content Evolution .” BMC Evolutionary Biology. BioMed Central, 2013. https://doi.org/10.1186/1471-2148-13-54.","short":"A. Kupczok, J.P. Bollback, BMC Evolutionary Biology 13 (2013) 54–54.","mla":"Kupczok, Anne, and Jonathan P. Bollback. “Probabilistic Models for CRISPR Spacer Content Evolution .” BMC Evolutionary Biology, vol. 13, no. 1, BioMed Central, 2013, pp. 54–54, doi:10.1186/1471-2148-13-54.","apa":"Kupczok, A., & Bollback, J. P. (2013). Probabilistic models for CRISPR spacer content evolution . BMC Evolutionary Biology. BioMed Central. https://doi.org/10.1186/1471-2148-13-54","ieee":"A. Kupczok and J. P. Bollback, “Probabilistic models for CRISPR spacer content evolution ,” BMC Evolutionary Biology, vol. 13, no. 1. BioMed Central, pp. 54–54, 2013.","ista":"Kupczok A, Bollback JP. 2013. Probabilistic models for CRISPR spacer content evolution . BMC Evolutionary Biology. 13(1), 54–54.","ama":"Kupczok A, Bollback JP. Probabilistic models for CRISPR spacer content evolution . BMC Evolutionary Biology. 2013;13(1):54-54. doi:10.1186/1471-2148-13-54"},"date_published":"2013-02-26T00:00:00Z","scopus_import":1,"day":"26","has_accepted_license":"1","title":"Probabilistic models for CRISPR spacer content evolution ","status":"public","ddc":["576"],"intvolume":" 13","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"2412","file":[{"access_level":"open_access","file_name":"IST-2015-397-v1+1_1471-2148-13-54.pdf","file_size":518729,"content_type":"application/pdf","creator":"system","relation":"main_file","file_id":"5268","checksum":"029c7e0b198c19312b66ecce3cabb22f","date_created":"2018-12-12T10:17:15Z","date_updated":"2020-07-14T12:45:40Z"}],"oa_version":"Published Version","pubrep_id":"397","type":"journal_article","abstract":[{"text":"Background: The CRISPR/Cas system is known to act as an adaptive and heritable immune system in Eubacteria and Archaea. Immunity is encoded in an array of spacer sequences. Each spacer can provide specific immunity to invasive elements that carry the same or a similar sequence. Even in closely related strains, spacer content is very dynamic and evolves quickly. Standard models of nucleotide evolutioncannot be applied to quantify its rate of change since processes other than single nucleotide changes determine its evolution.Methods We present probabilistic models that are specific for spacer content evolution. They account for the different processes of insertion and deletion. Insertions can be constrained to occur on one end only or are allowed to occur throughout the array. One deletion event can affect one spacer or a whole fragment of adjacent spacers. Parameters of the underlying models are estimated for a pair of arrays by maximum likelihood using explicit ancestor enumeration.Results Simulations show that parameters are well estimated on average under the models presented here. There is a bias in the rate estimation when including fragment deletions. The models also estimate times between pairs of strains. But with increasing time, spacer overlap goes to zero, and thus there is an upper bound on the distance that can be estimated. Spacer content similarities are displayed in a distance based phylogeny using the estimated times.We use the presented models to analyze different Yersinia pestis data sets and find that the results among them are largely congruent. The models also capture the variation in diversity of spacers among the data sets. A comparison of spacer-based phylogenies and Cas gene phylogenies shows that they resolve very different time scales for this data set.Conclusions The simulations and data analyses show that the presented models are useful for quantifying spacer content evolution and for displaying spacer content similarities of closely related strains in a phylogeny. This allows for comparisons of different CRISPR arrays or for comparisons between CRISPR arrays and nucleotide substitution rates.","lang":"eng"}],"issue":"1"}]