Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis

Nisthal, Alex; Wang, Connie Y.; Ary, Marylouise; Mayo, Stephen L.

doi:10.1073/pnas.1903888116

Cited by 144 publications

(171 citation statements)

References 61 publications

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“…One reason for the limited success of stability predictors in the identification of disease mutations is that predictions of ΔΔG values are still far from perfect. For example, a number of studies have compared ΔΔG predictors, showing heterogeneous correlations with experimental values on the order of R=0.5 for many predictors 12,13,60 . However, a recent work has also revealed problems with the noise in experimental stability data used to benchmark the prediction methods, generally assessed through correlation values 61 .…”

Section: Discussionmentioning

confidence: 99%

Identification of pathogenic missense mutations using protein stability predictors

Gerasimavicius

Liu

Marsh

2020

Preprint

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Attempts at using protein structures to identify disease-causing mutations have been dominated by the idea that most pathogenic mutations are disruptive at a structural level. Therefore, computational stability predictors, which assess whether a mutation is likely to be stabilising or destabilising to protein structure, have been commonly used when evaluating new candidate disease variants, despite not having been developed specifically for this purpose. We therefore tested 12 different stability predictors for their ability to discriminate between pathogenic and putatively benign missense variants. We find that one method, FoldX, considerably outperforms all others in the identification of disease variants. Moreover, we demonstrate that employing absolute energy change scores improves performance of nearly all predictors. Importantly, however, we observe that the utility of computational stability predictors is highly heterogeneous across different proteins, and that they are all are inferior to the best performing variant effect predictors for identifying pathogenic mutations. We suggest that this is largely due to alternate molecular mechanisms other than protein destabilisation underlying many pathogenic mutations. Thus, better ways of incorporating protein structural information and molecular mechanisms into computational variant effect predictors will be required for improved disease variant prioritisation.

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Section: Discussionmentioning

confidence: 99%

Identification of pathogenic missense mutations using protein stability predictors

Gerasimavicius

Liu

Marsh

2020

Preprint

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“…Deep mutational scanning is increasingly used to analyze the contributions that individual residues make to protein function (22,24,51). This approach has identified residues that underlie protein solubility (52), protein-protein interactions (53,54), membrane protein insertion (55), thermostability (56)(57)(58), substrate specificity (59), enzymatic function (56), and mutational epistasis (60). Such efforts have provided fundamental insight into sequence-function relationships within individual proteins.…”

Section: Discussionmentioning

confidence: 99%

Family permutation profiling identifies a dynamic protein domain as functionally tolerant to increased conformational entropy

Atkinson

Jones

Nanda

et al. 2019

Preprint

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To investigate whether adenylate kinase (AK) homologs differ in their functional tolerance to mutational lesions that alter dynamics, we subjected three homologs having a range of thermostabilities to random circular permutation and evaluated where new protein termini were non-disruptive to activity using a cellular selection and deep mutational scanning.Analysis of the positional tolerance to new termini, which increase local conformational entropy by breaking peptide bonds, showed that bonds were either functionally sensitive to cleavage across all three homologs, differentially sensitive, or uniformly tolerant. The mobile AMP binding domain, which displays the highest calculated contact energies (frustration), presented the greatest tolerance to new termini across all AKs. In contrast, retention of function in the lid and core domains was more dependent upon AK melting temperature. Thus, regions of high energetic frustration tolerated increases in conformational entropy in a manner that was less dependent on thermostability than regions of lower frustration. Our results suggest that family permutation profiling identifies primary structure that has been selected by evolution for high frustration that is critical to enzymatic activity. They also illustrate how deep mutational scanning can be applied to protein homologs in parallel to learn how topology and function govern mutational tolerance.3

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“…Application to protein G. Having explored the behavior of our interpolation method on simulated data, we now turn to analyzing empirical data. We begin by considering a combinatorial mutagenesis study of the IgG-binding domain of streptococcal protein G (GB1) 34 , which is a model system for studying protein folding stability and binding affinity 5,34,61,62 . By sequencing a library of protein variants before and after binding to IgG-Fc beads, this experiment 34 attempted to assay all possible combinations of mutations at four sites (V39, D40, G41, and V54; 20 4 = 160,000 protein variants) that had previously been shown to harbor a particularly strong and complex pattern of genetic interactions 5 .…”

Section: Caption)mentioning

confidence: 99%

Minimum epistasis interpolation for sequence-function relationships

Zhou

McCandlish

2020

Nat Commun

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Massively parallel phenotyping assays have provided unprecedented insight into how multiple mutations combine to determine biological function. While such assays can measure phenotypes for thousands to millions of genotypes in a single experiment, in practice these measurements are not exhaustive, so that there is a need for techniques to impute values for genotypes whose phenotypes have not been directly assayed. Here, we present an imputation method based on inferring the least epistatic possible sequence-function relationship compatible with the data. In particular, we infer the reconstruction where mutational effects change as little as possible across adjacent genetic backgrounds. The resulting models can capture complex higher-order genetic interactions near the data, but approach additivity where data is sparse or absent. We apply the method to high-throughput transcription factor binding assays and use it to explore a fitness landscape for protein G.

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Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis

Cited by 144 publications

References 61 publications

Identification of pathogenic missense mutations using protein stability predictors

Identification of pathogenic missense mutations using protein stability predictors

Family permutation profiling identifies a dynamic protein domain as functionally tolerant to increased conformational entropy

Minimum epistasis interpolation for sequence-function relationships

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