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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
Designer membraneless organelles enable codon reassignment of selected mRNAs in eukaryotes
A key step in the evolution of complex organisms like eukaryotes was the organization of specific tasks into organelles. Reinkemeier et al. designed an artificial, membraneless organelle into mammalian cells to perform orthogonal translation. In response to a specific codon in a selected messenger RNA, ribosomes confined to this organelle were able to introduce chemical functionalities site-specifically, expanding the canonical set of amino acids. This approach opens possibilities in synthetic cell engineering and biomedical research. Science , this issue p. eaaw2644 Orthogonal translation of specific proteins is enabled by a phase-separated synthetic organelle in eukaryotic cells. Nature regulates interference between cellular processes—allowing more complexity of life—by confining specific functions to organelles. Inspired by this concept, we designed an artificial organelle dedicated to protein engineering. We generated a membraneless organelle to translate only one type of messenger RNA—by recruiting an RNA-targeting system, stop codon–suppression machinery, and ribosomes—by means of phase separation and spatial targeting. This enables site-specific protein engineering with a tailored noncanonical function in response to one specific codon in the entire genome only in the protein of choice. Our results demonstrate a simple yet effective approach to the generation of artificial organelles that provides a route toward customized orthogonal translation and protein engineering in semisynthetic eukaryotic cells.
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
Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design
Adeno-associated virus (AAV) capsids can deliver transformative gene therapies, but our understanding of AAV biology remains incomplete. We generated the complete first-order AAV2 capsid fitness landscape, characterizing all single-codon substitutions, insertions, and deletions across multiple functions relevant for in vivo delivery. We discovered a frameshifted gene in the VP1 region that expresses a membrane-associated accessory protein that limits AAV production through competitive exclusion. Mutant biodistribution revealed the importance of both surface-exposed and buried residues, with a few phenotypic profiles characterizing most variants. Finally, we algorithmically designed and experimentally verified a diverse in vivo targeted capsid library with viability far exceeding random mutagenesis approaches. These results demonstrate the power of systematic mutagenesis for deciphering complex genomes and the potential of empirical machine-guided protein engineering.
Journal Article