title: Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein.
abstract: Proteins evolve through two primary mechanisms: substitution, where mutations alter a protein's amino-acid sequence, and insertions and deletions (indels), where amino acids are either added to or removed from the sequence. Protein structure has been shown to influence the rate at which substitutions accumulate across sites in proteins, but whether structure similarly constrains the occurrence of indels has not been rigorously studied. Here, we investigate the extent to which structural properties known to covary with protein evolutionary rates might also predict protein tolerance to indels. Specifically, we analyze a publicly available dataset of single-amino-acid deletion mutations in enhanced green fluorescent protein (eGFP) to assess how well the functional effect of deletions can be predicted from protein structure. We find that weighted contact number (WCN), which measures how densely packed a residue is within the protein's three-dimensional structure, provides the best single predictor for whether eGFP will tolerate a given deletion. We additionally find that using protein design to explicitly model deletions results in improved predictions of functional status when combined with other structural predictors. Our work suggests that structure plays fundamental role in constraining deletions at sites in proteins, and further that similar biophysical constraints influence both substitutions and deletions. This study therefore provides a solid foundation for future work to examine how protein structure influences tolerance of more complex indel events, such as insertions or large deletions.
authors.affiliation: Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America.|Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America.|Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Eleisha L
authors.initials: EL
authors.lastname: Jackson
authors.affiliation: Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, Pennsylvania, United States of America.
authors.forename: Stephanie J
authors.initials: SJ
authors.lastname: Spielman
authors.affiliation: Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America.|Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, Texas, United States of America.|Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America.
authors.forename: Claus O
authors.identifier: http://orcid.org/0000-0002-7470-9261
authors.initials: CO
authors.lastname: Wilke
mesh_terms: D000595:Amino Acid Sequence; D019020:Directed Molecular Evolution; D005453:Fluorescence; D049452:Green Fluorescent Proteins / Q000737:chemistry* / Q000235:genetics*; D054643:INDEL Mutation; D016015:Logistic Models; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D017384:Sequence Deletion; D060388:Support Vector Machine
chemical_list: C485184:enhanced green fluorescent protein; D049452:Green Fluorescent Proteins