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<titleInfo><title>Heterogeneity of the GFP fitness landscape and data-driven protein design</title></titleInfo>


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<name type="personal">
  <namePart type="given">Louisa</namePart>
  <namePart type="family">Gonzalez Somermeyer</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">4720D23C-F248-11E8-B48F-1D18A9856A87</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0001-9139-5383</description></name>
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  <namePart type="given">Aubin</namePart>
  <namePart type="family">Fleiss</namePart>
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  <namePart type="given">Alexander S</namePart>
  <namePart type="family">Mishin</namePart>
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  <namePart type="given">Nina G</namePart>
  <namePart type="family">Bozhanova</namePart>
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  <namePart type="given">Anna A</namePart>
  <namePart type="family">Igolkina</namePart>
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  <namePart type="given">Jens</namePart>
  <namePart type="family">Meiler</namePart>
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  <namePart type="given">Maria-Elisenda</namePart>
  <namePart type="family">Alaball Pujol</namePart>
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<name type="personal">
  <namePart type="given">Ekaterina V</namePart>
  <namePart type="family">Putintseva</namePart>
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<name type="personal">
  <namePart type="given">Karen S</namePart>
  <namePart type="family">Sarkisyan</namePart>
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  <namePart type="given">Fyodor</namePart>
  <namePart type="family">Kondrashov</namePart>
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  <namePart>Characterizing the fitness landscape on population and global scales</namePart>
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  <namePart>International IST Doctoral Program</namePart>
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<abstract lang="eng">Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design – instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.</abstract>

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<originInfo><publisher>eLife Sciences Publications</publisher><dateIssued encoding="w3cdtf">2022</dateIssued>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<subject><topic>General Immunology and Microbiology</topic><topic>General Biochemistry</topic><topic>Genetics and Molecular Biology</topic><topic>General Medicine</topic><topic>General Neuroscience</topic>
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<relatedItem type="host"><titleInfo><title>eLife</title></titleInfo>
  <identifier type="issn">2050-084X</identifier>
  <identifier type="MEDLINE">35510622</identifier>
  <identifier type="ISI">000799197200001</identifier><identifier type="doi">10.7554/elife.75842</identifier>
<part><detail type="volume"><number>11</number></detail>
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  <location>     <url>https://research-explorer.ista.ac.at/record/17850</url>  </location>
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     <url>https://github.com/aequorea238/Orthologous_GFP_Fitness_Peaks</url>
  
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<short>L. Gonzalez Somermeyer, A. Fleiss, A.S. Mishin, N.G. Bozhanova, A.A. Igolkina, J. Meiler, M.-E. Alaball Pujol, E.V. Putintseva, K.S. Sarkisyan, F. Kondrashov, ELife 11 (2022).</short>
<ama>Gonzalez Somermeyer L, Fleiss A, Mishin AS, et al. Heterogeneity of the GFP fitness landscape and data-driven protein design. &lt;i&gt;eLife&lt;/i&gt;. 2022;11. doi:&lt;a href=&quot;https://doi.org/10.7554/elife.75842&quot;&gt;10.7554/elife.75842&lt;/a&gt;</ama>
<ista>Gonzalez Somermeyer L, Fleiss A, Mishin AS, Bozhanova NG, Igolkina AA, Meiler J, Alaball Pujol M-E, Putintseva EV, Sarkisyan KS, Kondrashov F. 2022. Heterogeneity of the GFP fitness landscape and data-driven protein design. eLife. 11, 75842.</ista>
<ieee>L. Gonzalez Somermeyer &lt;i&gt;et al.&lt;/i&gt;, “Heterogeneity of the GFP fitness landscape and data-driven protein design,” &lt;i&gt;eLife&lt;/i&gt;, vol. 11. eLife Sciences Publications, 2022.</ieee>
<apa>Gonzalez Somermeyer, L., Fleiss, A., Mishin, A. S., Bozhanova, N. G., Igolkina, A. A., Meiler, J., … Kondrashov, F. (2022). Heterogeneity of the GFP fitness landscape and data-driven protein design. &lt;i&gt;ELife&lt;/i&gt;. eLife Sciences Publications. &lt;a href=&quot;https://doi.org/10.7554/elife.75842&quot;&gt;https://doi.org/10.7554/elife.75842&lt;/a&gt;</apa>
<chicago>Gonzalez Somermeyer, Louisa, Aubin Fleiss, Alexander S Mishin, Nina G Bozhanova, Anna A Igolkina, Jens Meiler, Maria-Elisenda Alaball Pujol, Ekaterina V Putintseva, Karen S Sarkisyan, and Fyodor Kondrashov. “Heterogeneity of the GFP Fitness Landscape and Data-Driven Protein Design.” &lt;i&gt;ELife&lt;/i&gt;. eLife Sciences Publications, 2022. &lt;a href=&quot;https://doi.org/10.7554/elife.75842&quot;&gt;https://doi.org/10.7554/elife.75842&lt;/a&gt;.</chicago>
<mla>Gonzalez Somermeyer, Louisa, et al. “Heterogeneity of the GFP Fitness Landscape and Data-Driven Protein Design.” &lt;i&gt;ELife&lt;/i&gt;, vol. 11, 75842, eLife Sciences Publications, 2022, doi:&lt;a href=&quot;https://doi.org/10.7554/elife.75842&quot;&gt;10.7554/elife.75842&lt;/a&gt;.</mla>
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