Rational evolutionary design: The theory of in vitro protein evolution

Ca, Voigt; Kauffman, Stuart; Zg, Wang

doi:10.1016/s0065-3233(01)55003-2

Cited by 122 publications

(69 citation statements)

References 149 publications

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“…This method has proven to be efficient for the improvement of a variety of enzymatic properties (Bornscheuer and Pohl, 2001;Schmidt et al, 2009;Voigt et al, 2001).…”

Section: Discussionmentioning

confidence: 99%

Mutations at the putative active cavity of styrene monooxygenase: Enhanced activity and reversed enantioselectivity

Lin

Tang

Ahmed

et al. 2012

Journal of Biotechnology

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“…This method has proven to be efficient for the improvement of a variety of enzymatic properties (Bornscheuer and Pohl, 2001;Schmidt et al, 2009;Voigt et al, 2001).…”

Section: Discussionmentioning

confidence: 99%

Mutations at the putative active cavity of styrene monooxygenase: Enhanced activity and reversed enantioselectivity

Lin

Tang

Ahmed

et al. 2012

Journal of Biotechnology

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“…The resulting sequence-space representations may be called ''inverse-fitness'' or ''mortality'' landscapes. We choose to use these depictions instead of the conventional fitness landscape, because picturing a locally optimized sequence as a fitness peak (7,37) does not quite convey its mutational stability (18,38) in light of Earthings' experience with gravity. In contrast, on the mortality landscape such a sequence and the homologous sequences around it form a basin of attraction (18,38) or superfunnel (18), which may be viewed as a sequence-space generalization of the conformational-space folding funnel on the energy landscape (39)(40)(41)(42).…”

Section: Modeling the Mortality Landscapementioning

confidence: 99%

“…Such efforts would benefit the development of in vitro evolution for protein engineering (7)(8)(9) as well. In a recent insightful study, Bogarad and Deem (10) used a generalized (block) NK model, with terms in a potential function (interpreted to represent protein interactions and substrate binding affinities) assigned randomly to a large collection of sequences.…”

mentioning

confidence: 99%

Recombinatoric exploration of novel folded structures: A heteropolymer-based model of protein evolutionary landscapes

Cui

Wong

Bornberg‐Bauer

et al. 2002

Proc. Natl. Acad. Sci. U.S.A.

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The role of recombination in evolution is compared with that of point mutations (substitutions) in the context of a simple, polymer physics-based model mapping between sequence (genotype) and conformational (phenotype) spaces. Crossovers and point mutations of lattice chains with a hydrophobic polar code are investigated. Sequences encoding for a single ground-state conformation are considered viable and used as model proteins. Point mutations lead to diffusive walks on the evolutionary landscape, whereas crossovers can ''tunnel'' through barriers of diminished fitness. The degree to which crossovers allow for more efficient sequence and structural exploration depends on the relative rates of point mutations versus that of crossovers and the dispersion in fitness that characterizes the ruggedness of the evolutionary landscape. The probability that a crossover between a pair of viable sequences results in viable sequences is an order of magnitude higher than random, implying that a sequence's overall propensity to encode uniquely is embodied partially in local signals. Consistent with this observation, certain hydrophobicity patterns are significantly more favored than others among fragments (i.e., subsequences) of sequences that encode uniquely, and examples reminiscent of autonomous folding units in real proteins are found. The number of structures explored by both crossovers and point mutations is always substantially larger than that via point mutations alone, but the corresponding numbers of sequences explored can be comparable when the evolutionary landscape is rugged. Efficient structural exploration requires intermediate nonextreme ratios between point-mutation and crossover rates.crossovers ͉ neutral nets ͉ sequence space ͉ thermodynamic stability ͉ lattice protein models I t is widely recognized that key events in evolution may involve large-scale genomic rearrangements (1-6). Experiments on plants suggest that dramatic restructuring of the genome in response to traumas may underlie formations of many new species (1). The presence of introns in the genes of higher organisms implies that even a single base change can result in the deletion or insertion of whole sequences in the protein product (2). It has been argued that cellular ''natural genetic engineering'' machineries have evolved to modulate genomic reorganization in lower organisms (3). Moreover, certain peculiarities in present-day genomes and cellular organizations may be explained best by ''lateral'' or ''horizontal'' transfers in the past (4, 5). Indeed, it is large-scale genomic rearrangements rather than the accumulation of point mutations that bear the main responsibility for the alarmingly quick emergence of bacterial antibiotic resistance.Therefore, to capture evolutionary complexities better, theoretical perspectives that focus exclusively on point mutations should be augmented to include other types of sequence transformations. Such efforts would benefit the development of in vitro evolution for protein engineering (7-9) as well. In a ...

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“…Less associated (but equally applicable) with biological fitness landscapes are landscape studies considering combinatorial chemical space (Stadler & Stadler 2002), where rules associated with neighbourhood and fitness can be directly applied to guiding chemical synthesis. While mapping the entire sequence space of an average protein is intractable owing to the 'hyper-astronomical' number of variants (Voigt et al 2000), even limited sampling of the sequence space has previously been prohibitive because of the cost of DNA sequencing and (especially) of synthesis. With advances in sequencing technology this is no longer the case, and we can now begin to fully appreciate the diversity displayed by many biological fitness landscapes (Poelwijk et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…The sampling of sequences from biological fitness landscapes has often gone hand-in-hand with the family of methods known as directed evolution (Voigt et al 2000;Poelwijk et al 2007). The procedure works by iteratively modifying, selecting and amplifying biological macromolecules to induce phenotypic improvements.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a complete DNA–protein affinity landscape

Rowe

Platt

Wedge

et al. 2009

J. R. Soc. Interface.

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Properties of biological fitness landscapes are of interest to a wide sector of the life sciences, from ecology to genetics to synthetic biology. For biomolecular fitness landscapes, the information we currently possess comes primarily from two sources: sparse samples obtained from directed evolution experiments; and more fine-grained but less authentic information from 'in silico' models (such as NK-landscapes). Here we present the entire protein-binding profile of all variants of a nucleic acid oligomer 10 bases in length, which we have obtained experimentally by a series of highly parallel on-chip assays. The resulting complete landscape of sequence-binding pairs, comprising more than one million binding measurements in duplicate, has been analysed statistically using a number of metrics commonly applied to synthetic landscapes. These metrics show that the landscape is rugged, with many local optima, and that this arises from a combination of experimental variation and the natural structural properties of the oligonucleotides.

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Rational evolutionary design: The theory of in vitro protein evolution

Cited by 122 publications

References 149 publications

Mutations at the putative active cavity of styrene monooxygenase: Enhanced activity and reversed enantioselectivity

Mutations at the putative active cavity of styrene monooxygenase: Enhanced activity and reversed enantioselectivity

Recombinatoric exploration of novel folded structures: A heteropolymer-based model of protein evolutionary landscapes

Analysis of a complete DNA–protein affinity landscape

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