Methods of artificial evolution such as SELEX and in vitro selection have made it possible to isolate RNA and DNA motifs with a wide range of functions from large random sequence libraries. Once the primary sequence of a functional motif is known, the sequence space around it can be comprehensively explored using a combination of random mutagenesis and selection. However, methods to explore the sequence space of a secondary structure are not as well characterized. Here we address this question by describing a method to construct libraries in a single synthesis which are enriched for sequences with the potential to form a specific secondary structure, such as that of an aptamer, ribozyme, or deoxyribozyme. Although interactions such as base pairs cannot be encoded in a library using conventional DNA synthesizers, it is possible to modulate the probability that two positions will have the potential to pair by biasing the nucleotide composition at these positions. Here we show how to maximize this probability for each of the possible ways to encode a pair (in this study defined as A-U or U-A or C-G or G-C or G.U or U.G). We then use these optimized coding schemes to calculate the number of different variants of model stems and secondary structures expected to occur in a library for a series of structures in which the number of pairs and the extent of conservation of unpaired positions is systematically varied. Our calculations reveal a tradeoff between maximizing the probability of forming a pair and maximizing the number of possible variants of a desired secondary structure that can occur in the library. They also indicate that the optimal coding strategy for a library depends on the complexity of the motif being characterized. Because this approach provides a simple way to generate libraries enriched for sequences with the potential to form a specific secondary structure, we anticipate that it should be useful for the optimization and structural characterization of functional nucleic acid motifs.
- MeSH
- aptamery nukleotidové genetika MeSH
- DNA katalytická genetika MeSH
- genová knihovna * MeSH
- konformace nukleové kyseliny MeSH
- mutageneze MeSH
- nukleotidové motivy genetika MeSH
- obrácené repetice genetika MeSH
- párování bází MeSH
- pravděpodobnost MeSH
- řízená evoluce molekul metody MeSH
- RNA katalytická genetika MeSH
- syntetická biologie metody MeSH
- techniky in vitro MeSH
- Publikační typ
- časopisecké články MeSH
Engineered small non-antibody protein scaffolds are a promising alternative to antibodies and are especially attractive for use in protein therapeutics and diagnostics. The advantages include smaller size and a more robust, single-domain structural framework with a defined binding surface amenable to mutation. This calls for a more systematic approach in designing new scaffolds suitable for use in one or more methods of directed evolution. We hereby describe a process based on an analysis of protein structures from the Protein Data Bank and their experimental examination. The candidate protein scaffolds were subjected to a thorough screening including computational evaluation of the mutability, and experimental determination of their expression yield in E. coli, solubility, and thermostability. In the next step, we examined several variants of the candidate scaffolds including their wild types and alanine mutants. We proved the applicability of this systematic procedure by selecting a monomeric single-domain human protein with a fold different from previously known scaffolds. The newly developed scaffold, called ProBi (Protein Binder), contains two independently mutable surface patches. We demonstrated its functionality by training it as a binder against human interleukin-10, a medically important cytokine. The procedure yielded scaffold-related variants with nanomolar affinity.
- MeSH
- databáze proteinů MeSH
- interleukin-10 metabolismus MeSH
- konformace proteinů MeSH
- počítačová simulace MeSH
- proteinové inženýrství MeSH
- proteiny chemie genetika metabolismus MeSH
- rekombinantní proteiny chemie genetika metabolismus MeSH
- ribozomy metabolismus MeSH
- řízená evoluce molekul metody MeSH
- sekvence aminokyselin MeSH
- stabilita proteinů MeSH
- vazba proteinů MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.
Protein engineering is the discipline of developing useful proteins for applications in research, therapeutic, and industrial processes by modification of naturally occurring proteins or by invention of de novo proteins. Modern protein engineering relies on the ability to rapidly generate and screen diverse libraries of mutant proteins. However, design of mutant libraries is typically hampered by scale and complexity, necessitating development of advanced automation and optimization tools that can improve efficiency and accuracy. At present, automated library design tools are functionally limited or not freely available. To address these issues, we developed Mutation Maker, an open source mutagenic oligo design software for large-scale protein engineering experiments. Mutation Maker is not only specifically tailored to multisite random and directed mutagenesis protocols, but also pioneers bespoke mutagenic oligo design for de novo gene synthesis workflows. Enabled by a novel bundle of orchestrated heuristics, optimization, constraint-satisfaction and backtracking algorithms, Mutation Maker offers a versatile toolbox for gene diversification design at industrial scale. Supported by in silico simulations and compelling experimental validation data, Mutation Maker oligos produce diverse gene libraries at high success rates irrespective of genes or vectors used. Finally, Mutation Maker was created as an extensible platform on the notion that directed evolution techniques will continue to evolve and revolutionize current and future-oriented applications.
- MeSH
- algoritmy MeSH
- Escherichia coli genetika MeSH
- genová knihovna MeSH
- kodon genetika MeSH
- mutace * MeSH
- mutageneze cílená metody MeSH
- mutageneze * MeSH
- mutantní proteiny MeSH
- oligonukleotidy genetika MeSH
- počítačová simulace MeSH
- proteiny genetika MeSH
- řízená evoluce molekul metody MeSH
- software * MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Fibroblast growth factors (FGFs) serve numerous regulatory functions in complex organisms, and their corresponding therapeutic potential is of growing interest to academics and industrial researchers alike. However, applications of these proteins are limited due to their low stability. Here we tackle this problem using a generalizable computer-assisted protein engineering strategy to create a unique modified FGF2 with nine mutations displaying unprecedented stability and uncompromised biological function. The data from the characterization of stabilized FGF2 showed a remarkable prediction potential of in silico methods and provided insight into the unfolding mechanism of the protein. The molecule holds a considerable promise for stem cell research and medical or pharmaceutical applications.
- MeSH
- bodová mutace MeSH
- design s pomocí počítače * MeSH
- embryonální kmenové buňky cytologie metabolismus MeSH
- fibroblastový růstový faktor 2 chemie genetika metabolismus MeSH
- lidé MeSH
- počítačová simulace MeSH
- proteinové inženýrství * MeSH
- řízená evoluce molekul MeSH
- sbalování proteinů MeSH
- sekvence aminokyselin MeSH
- stabilita proteinů * MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Within five years, the CRISPR-Cas system has emerged as the dominating tool for genome engineering, while also changing the speed and efficiency of metabolic engineering in conventional (Saccharomyces cerevisiae and Schizosaccharomyces pombe) and non-conventional (Yarrowia lipolytica, Pichia pastoris syn. Komagataella phaffii, Kluyveromyces lactis, Candida albicans and C. glabrata) yeasts. Especially in S. cerevisiae, an extensive toolbox of advanced CRISPR-related applications has been established, including crisprTFs and gene drives. The comparison of innovative CRISPR-Cas expression strategies in yeasts presented here may also serve as guideline to implement and refine CRISPR-Cas systems for highly efficient genome editing in other eukaryotic organisms.
- MeSH
- bodová mutace MeSH
- chromozomy hub MeSH
- CRISPR-Cas systémy * MeSH
- editace genu metody MeSH
- geneticky modifikované mikroorganismy MeSH
- guide RNA, Kinetoplastida MeSH
- klonování DNA MeSH
- kvasinky genetika MeSH
- metabolické inženýrství MeSH
- Pichia genetika MeSH
- regulace genové exprese u hub MeSH
- Saccharomyces cerevisiae genetika MeSH
- technologie gene drive MeSH
- Yarrowia genetika MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
Ancestral sequence reconstruction (ASR) represents a powerful approach for empirical testing structure-function relationships of diverse proteins. We employed ASR to predict sequences of five ancestral haloalkane dehalogenases (HLDs) from the HLD-II subfamily. Genes encoding the inferred ancestral sequences were synthesized and expressed in Escherichia coli, and the resurrected ancestral enzymes (AncHLD1-5) were experimentally characterized. Strikingly, the ancestral HLDs exhibited significantly enhanced thermodynamic stability compared to extant enzymes (ΔTm up to 24 °C), as well as higher specific activities with preference for short multi-substituted halogenated substrates. Moreover, multivariate statistical analysis revealed a shift in the substrate specificity profiles of AncHLD1 and AncHLD2. This is extremely difficult to achieve by rational protein engineering. The study highlights that ASR is an efficient approach for the development of novel biocatalysts and robust templates for directed evolution.
Traditional directed evolution experiments are often time-, labor- and cost-intensive because they involve repeated rounds of random mutagenesis and the selection or screening of large mutant libraries. The efficiency of directed evolution experiments can be significantly improved by targeting mutagenesis to a limited number of hot-spot positions and/or selecting a limited set of substitutions. The design of such "smart" libraries can be greatly facilitated by in silico analyses and predictions. Here we provide an overview of computational tools applicable for (a) the identification of hot-spots for engineering enzyme properties, and (b) the evaluation of predicted hot-spots and selection of suitable amino acids for substitutions. The selected tools do not require any specific expertise and can easily be implemented by the wider scientific community.
