Protein tolerance to random amino acid change.

By H. Guo, Juno Choe, Lawrence Loeb. 2004

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Citations in the corpus

Computational Protein Design with Deep Learning Neural Networks (2018, background): “62 Moreover, residues at active sites are subject to functional restraints and are not necessary the most stable ones.”
Exploring protein fitness landscapes by directed evolution (2009, positive): “Proteins are in fact robust to mutation — a significant fraction of possible mutants retain their fold and functio”
A decade and a half of protein intrinsic disorder: Biology still waits for physics (2013, background): “[56] [57] [58] [59] [60] [61] [62] [63] In other words, folding alphabet can be noticeably reduced, 55, 64 and amino acids can be clustered based on some shared features such as homolog substitution frequency, 65 local structural environments, 66 or peculiarities of the tertiary structural environme”
How Protein Stability and New Functions Trade Off (2008, result): “The DDG values of bsheets appeared to be more destabilizing than those of random coils and a-helices (Figure S1), as previously observed [47,48].”
Evolvability as a function of purifying selection in TEM-1 β-lactamase. (2015, positive): “…to mutations has been assessed in a number of important studies with high-throughput site-directed and random mutagenesis (Fowler et al., 2010; Guo et al., 2004; Huang et al., 1996; Jacquier et al., 2013; Loeb et al., 1989; McLaughlin et al., 2012; Melnikov et al., 2014; Rennell et al., 1991;…”
Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations (2015, background): “Experiments that systematically map the effects of mutations in a given protein are generally conducted through either saturation mutagenesis, using NNS codons to diversify individual sites [18–21], or random mutagenesis along the entire gene using error-prone replication [22].”
High Resolution Mapping of Protein Sequence–Function Relationships (2010, result): “These data are consistent with studies showing that mutation-intolerant regions in vitro are generally conserved in evolutio”
Prevalence of Epistasis in the Evolution of Influenza A Surface Proteins (2011, background): “Yet, numerous studies of protein evolution in vitro [27–29] as well as studies in bacteria [30] and viruses [20,31,32] have shown that beneficial mutations are often pleiotropic: in addition to their original beneficial effect, they cause some, usually negative, side effects on other protein propert”
Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution (2008, positive): “Because residues directly involved in protein function tend not to tolerate substitutions (Anfinsen, 1959; Guo et al., 2004; Wilson et al., 1977; Zuckerkandl, 1976), it has long been hypothesized that slow-evolving proteins have more sites committed to function (Zuckerkandl, 1976) or are more functi”
A model of substitution trajectories in sequence space and long-term protein evolution. (2015, result): “This estimate is consistent with the observation of slow rate of sequence divergence of sequences in our data set, with K d / K 4 ~ 0.02 and with mutational studies (Guo et al. 2004).”