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"Protein Engineering"
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The road to fully programmable protein catalysis
The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.
Recent progress in computational enzyme design, active site engineering and directed evolution are reviewed, highlighting methodological innovations needed to deliver improved designer biocatalysts.
Journal Article
Photosynthetic protein-based photovoltaics
\"Photosynthetic protein complexes have an overall quantum yield close to 100%. Photovoltaic devices using protein complexes can provide an economical alternative to existing solar cells. This book explains how to build and improve the efficiency of protein solar energy conversion devices based on the present understanding of the photosynthetic proteins\"-- Provided by publisher.
Book
Unified rational protein engineering with sequence-based deep representation learning
Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded. We show that the simplest models built on top of this unified representation (UniRep) are broadly applicable and generalize to unseen regions of sequence space. Our data-driven approach predicts the stability of natural and de novo designed proteins, and the quantitative function of molecularly diverse mutants, competitively with the state-of-the-art methods. UniRep further enables two orders of magnitude efficiency improvement in a protein engineering task. UniRep is a versatile summary of fundamental protein features that can be applied across protein engineering informatics.
Journal Article
The coming of age of de novo protein design
There are 20
200
possible amino-acid sequences for a 200-residue protein, of which the natural evolutionary process has sampled only an infinitesimal subset.
De novo
protein design explores the full sequence space, guided by the physical principles that underlie protein folding. Computational methodology has advanced to the point that a wide range of structures can be designed from scratch with atomic-level accuracy. Almost all protein engineering so far has involved the modification of naturally occurring proteins; it should now be possible to design new functional proteins from the ground up to tackle current challenges in biomedicine and nanotechnology.
Journal Article
TRUPATH, an open-source biosensor platform for interrogating the GPCR transducerome
G-protein-coupled receptors (GPCRs) remain major drug targets, despite our incomplete understanding of how they signal through 16 non-visual G-protein signal transducers (collectively named the transducerome) to exert their actions. To address this gap, we have developed an open-source suite of 14 optimized bioluminescence resonance energy transfer (BRET) Gαβγ biosensors (named TRUPATH) to interrogate the transducerome with single pathway resolution in cells. Generated through exhaustive protein engineering and empirical testing, the TRUPATH suite of Gαβγ biosensors includes the first Gα15 and GαGustducin probes. In head-to-head studies, TRUPATH biosensors outperformed first-generation sensors at multiple GPCRs and in different cell lines. Benchmarking studies with TRUPATH biosensors recapitulated previously documented signaling bias and revealed new coupling preferences for prototypic and understudied GPCRs with potential in vivo relevance. To enable a greater understanding of GPCR molecular pharmacology by the scientific community, we have made TRUPATH biosensors easily accessible as a kit through Addgene.
Development of BRET sensors for nearly all major G proteins show that GPCR–G-protein coupling ranges from promiscuous to extremely specific, Switch III is a novel site for G-protein engineering, and optimal donor–acceptor positioning is non-obvious.
Journal Article
Rationally engineered Cas9 nucleases with improved specificity
The RNA-guided endonuclease Cas9 is a versatile genome-editing tool with a broad range of applications from therapeutics to functional annotation of genes. Cas9 creates double-strand breaks (DSBs) at targeted genomic loci complementary to a short RNA guide. However, Cas9 can cleave off-target sites that are not fully complementary to the guide, which poses a major challenge for genome editing. Here, we use structure-guided protein engineering to improve the specificity of Streptococcus pyogenes Cas9 (SpCas9). Using targeted deep sequencing and unbiased whole-genome off-target analysis to assess Cas9-mediated DNA cleavage in human cells, we demonstrate that \"enhanced specificity\" SpCas9 (eSpCas9) variants reduce off-target effects and maintain robust on-target cleavage. Thus, eSpCas9 could be broadly useful for genome-editing applications requiring a high level of specificity.
Journal Article
RNA editing with CRISPR-Cas13
Nucleic acid editing holds promise for treating genetic disease, particularly at the RNA level, where disease-relevant sequences can be rescued to yield functional protein products. Type VI CRISPR-Cas systems contain the programmable single-effector RNA-guided ribonuclease Cas13. We profiled type VI systems in order to engineer a Cas13 ortholog capable of robust knockdown and demonstrated RNA editing by using catalytically inactive Cas13 (dCas13) to direct adenosine-to-inosine deaminase activity by ADAR2 (adenosine deaminase acting on RNA type 2) to transcripts in mammalian cells. This system, referred to as RNA Editing for Programmable A to I Replacement (REPAIR), which has no strict sequence constraints, can be used to edit full-length transcripts containing pathogenic mutations. We further engineered this system to create a high-specificity variant and minimized the system to facilitate viral delivery. REPAIR presents a promising RNA-editing platform with broad applicability for research, therapeutics, and biotechnology.
Journal Article
Protein expression in Pichia pastoris: recent achievements and perspectives for heterologous protein production
Pichia pastoris is an established protein expression host mainly applied for the production of biopharmaceuticals and industrial enzymes. This methylotrophic yeast is a distinguished production system for its growth to very high cell densities, for the available strong and tightly regulated promoters, and for the options to produce gram amounts of recombinant protein per litre of culture both intracellularly and in secretory fashion. However, not every protein of interest is produced in or secreted by P. pastoris to such high titres. Frequently, protein yields are clearly lower, particularly if complex proteins are expressed that are hetero-oligomers, membrane-attached or prone to proteolytic degradation. The last few years have been particularly fruitful because of numerous activities in improving the expression of such complex proteins with a focus on either protein engineering or on engineering the protein expression host P. pastoris. This review refers to established tools in protein expression in P. pastoris and highlights novel developments in the areas of expression vector design, host strain engineering and screening for high-level expression strains. Breakthroughs in membrane protein expression are discussed alongside numerous commercial applications of P. pastoris derived proteins.
Journal Article
Expanding and reprogramming the genetic code
Nature uses a limited, conservative set of amino acids to synthesize proteins. The ability to genetically encode an expanded set of building blocks with new chemical and physical properties is transforming the study, manipulation and evolution of proteins, and is enabling diverse applications, including approaches to probe, image and control protein function, and to precisely engineer therapeutics. Underpinning this transformation are strategies to engineer and rewire translation. Emerging strategies aim to reprogram the genetic code so that noncanonical biopolymers can be synthesized and evolved, and to test the limits of our ability to engineer the translational machinery and systematically recode genomes.
A review of the recent developments in reprogramming the genetic code of cells and organisms to include non-canonical amino acids in precisely engineered proteins.
Rewriting genetic guidelines
In living organisms, proteins that are encoded by DNA are composed of 20 canonical amino acids, with some organisms using up to two additional derivatives. Theoretically, other molecules that are related to these amino acids could form a similar protein backbone and might confer new properties on the proteins. Jason Chin reviews the most recent studies on reprogramming the genetic code, where progress is being made in incorporating these non-canonical (that is, not naturally occurring) amino acids into proteins, even using the cell's own machinery to do so, and without disrupting overall protein function.
Journal Article