Protein Engineering and Stabilization from Sequence Statistics

Durani, Venuka; Magliery, Thomas J.

doi:10.1016/b978-0-12-394292-0.00011-4

Cited by 27 publications

(30 citation statements)

References 43 publications

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“…These observations also suggested an algorithm to improve consensus design, by making consensus mutations at more-conserved positions and eliminating mutations at highly correlated positions [63,64]. The result in yeast TIM was a 90+% success rate in identifying stabilizing mutations, and large numbers of the mutations could be productively aggregated.…”

Section: Effects Of Sequence Correlationmentioning

confidence: 99%

Protein stability: computation, sequence statistics, and new experimental methods

Magliery

2015

Current Opinion in Structural Biology

136

132

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Calculating protein stability and predicting stabilizing mutations remain exceedingly difficult tasks, largely due to the inadequacy of potential functions, the difficulty of modeling entropy and the unfolded state, and challenges of sampling, particularly of backbone conformations. Yet, computational design has produced some remarkably stable proteins in recent years, apparently owing to near ideality in structure and sequence features. With caveats, computational prediction of stability can be used to guide mutation, and mutations derived from consensus sequence analysis, especially improved by recent co-variation filters, are very likely to stabilize without sacrificing function. The combination of computational and statistical approaches with library approaches, including new technologies such as deep sequencing and high throughput stability measurements, point to a very exciting near term future for stability engineering, even with difficult computational issues remaining.

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Section: Effects Of Sequence Correlationmentioning

confidence: 99%

Protein stability: computation, sequence statistics, and new experimental methods

Magliery

2015

Current Opinion in Structural Biology

136

132

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“…Due to sequence conservation, it is reasonable to expect a substantial number of positions to have identical residues when comparing ancestral sequences with consensus sequences, but how these residues contribute to function is not always clear. Consensus mutations have been studied in many protein systems over the years and these mutations are sometimes found to be stabilizing and, in some cases, capable of modulating biomolecular function . Thus, the possibility arises that consensus mutations are responsible for the unique and extreme properties of laboratory resurrections of Precambrian proteins and that consensus variants are, in fact, good approximations of ancestral proteins.…”

Section: Introductionmentioning

confidence: 99%

Phenotypic comparisons of consensus variants versus laboratory resurrections of Precambrian proteins

et al. 2014

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Consensus-sequence engineering has generated protein variants with enhanced stability, and sometimes, with modulated biological function. Consensus mutations are often interpreted as the introduction of ancestral amino acid residues. However, the precise relationship between consensus engineering and ancestral protein resurrection is not fully understood. Here, we report the properties of proteins encoded by consensus sequences derived from a multiple sequence alignment of extant, class A β-lactamases, as compared with the properties of ancient Precambrian β-lactamases resurrected in the laboratory. These comparisons considered primary sequence, secondary, and tertiary structure, as well as stability and catalysis against different antibiotics. Out of the three consensus variants generated, one could not be expressed and purified (likely due to misfolding and/or low stability) and only one displayed substantial stability having substrate promiscuity, although to a lower extent than ancient β-lactamases. These results: (i) highlight the phenotypic differences between consensus variants and laboratory resurrections of ancestral proteins; (ii) question interpretations of consensus proteins as phenotypic proxies of ancestral proteins; and (iii) support the notion that ancient proteins provide a robust approach toward the preparation of protein variants having large numbers of mutational changes while possessing unique biomolecular properties.

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“…Sequences were downloaded from Pfam version 25 and curated by removing short fragments and identical sequences. The resulting MSA had 1343 sequences and was analyzed for correlation by mutual information using methods described previously . Following conventions from a study by Sawyer et al on repeat proteins, correlation values >3 standard deviations above the mean were deemed important and plotted on a correlation map (Figure A, inspired from StickWRLD utility and from use of the representation by Sawyer et al).…”

Section: Resultsmentioning

confidence: 99%

Phylogenetic spread of sequence data affects fitness of SOD1 consensus enzymes: Insights from sequence statistics and structural analyses

Goyal

Magliery

2018

Proteins

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Non-natural protein sequences with native-like structures and functions can be constructed successfully using consensus design. This design strategy is relatively well understood in repeat proteins with simple binding function, however detailed studies are lacking in globular enzymes. The SOD1 family is a good model for such studies due to the availability of large amount of sequence and structure data motivated by involvement of human SOD1 in the fatal motor neuron disease amyotrophic lateral sclerosis (ALS). We constructed two consensus SOD1 enzymes from multiple sequence alignments from all organisms and eukaryotic organisms. A significant difference in their catalytic activities shows that the phylogenetic spread of the sequences used affects the fitness of the construct obtained. A mutation in an electrostatic loop and overall design incompatibilities between bacterial and eukaryotic sequences were implicated in this disparity. Based on this analysis, a bioinformatics approach was used to classify mutations thought to cause familial ALS providing a unique high level view of the physical basis of disease-causing aggregation of human SOD1.

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Protein Engineering and Stabilization from Sequence Statistics

Cited by 27 publications

References 43 publications

Protein stability: computation, sequence statistics, and new experimental methods

Protein stability: computation, sequence statistics, and new experimental methods

Phenotypic comparisons of consensus variants versus laboratory resurrections of Precambrian proteins

Phylogenetic spread of sequence data affects fitness of SOD1 consensus enzymes: Insights from sequence statistics and structural analyses

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