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The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calculations, as well as machine learning tools, have dramatically improved protein engineering and design. In this Review, we discuss how these methods have enabled the design of increasingly complex structures and therapeutically relevant activities. Additionally, protein optimization methods have improved the stability and activity of complex eukaryotic proteins. Thanks to their increased reliability, computational design methods have been applied to improve therapeutics and enzymes for green chemistry and have generated vaccine antigens, antivirals and drug-delivery nano-vehicles. Moreover, the high success of design methods reflects an increased understanding of basic rules that govern the relationships among protein sequence, structure and function. However, de novo design is still limited mostly to α-helix bundles, restricting its potential to generate sophisticated enzymes and diverse protein and small-molecule binders. Designing complex protein structures is a challenging but necessary next step if we are to realize our objective of generating new-to-nature activities.Recent combinations of structure-based and sequence-based calculations and machine learning tools have dramatically improved protein engineering and design. Although designing complex protein structures remains challenging, these methods have enabled the design of therapeutically relevant activities, including vaccine antigens, antivirals and drug-delivery nano-vehicles.

Evolved SpCas9 variant evoCas9 has improved specificity and retains near wild-type on-target activity. Despite the utility of CRISPR–Cas9 nucleases for genome editing, the potential for off-target activity limits their application, especially for therapeutic purposes 1 , 2 . We developed a yeast-based assay to identify optimized Streptococcus pyogenes Cas9 (SpCas9) variants that enables simultaneous evaluation of on- and off-target activity. We screened a library of SpCas9 variants carrying random mutations in the REC3 domain and identified mutations that increased editing accuracy while maintaining editing efficiency. We combined four beneficial mutations to generate evoCas9, a variant that has fidelity exceeding both wild-type (79-fold improvement) and rationally designed Cas9 variants 3 , 4 (fourfold average improvement), while maintaining near wild-type on-target editing efficiency (90% median residual activity). Evaluating evoCas9 on endogenous genomic loci, we demonstrated a substantially improved specificity and observed no off-target sites for four of the eight single guide RNAs (sgRNAs) tested. Finally, we showed that following long-term expression (40 d), evoCas9 strongly limited the nonspecific cleavage of a difficult-to-discriminate off-target site and fully abrogated the cleavage of two additional off-target sites.

Peptides and proteins are widely used to treat a range of medical conditions; however, they often have to be injected and their effects are short-lived. These shortcomings of the native structure can be addressed by molecular engineering, but this is a complex undertaking. A molecular engineering technology initially applied to insulin — and which has now been successfully applied to several biopharmaceuticals — entails the derivatization of peptides and proteins with fatty acids. Various protraction mechanisms are enabled by the specific characteristics and positions of the attached fatty acid. Furthermore, the technology can ensure a long half-life following oral administration of peptide drugs, can alter the distribution of peptides and may hold potential for tissue targeting. Due to the inherent safety and well-defined chemical nature of the fatty acids, this technology provides a versatile approach to peptide and protein drug discovery.Peptide and protein drugs have proven successful in the treatment of a wide range of diseases, but their use can be limited by their inherent short-life and need for parenteral administration. Here, Kurtzhals et al. discuss how fatty acid derivatization can be applied to address these issues and optimize the pharmacological properties of peptide and protein drugs, highlighting associated considerations and future directions.

Genetically encoded fluorescent biosensors are powerful tools used to track chemical processes in intact biological systems. However, the development and optimization of biosensors remains a challenging and labor-intensive process, primarily due to technical limitations of methods for screening candidate biosensors. Here we describe a screening modality that combines droplet microfluidics and automated fluorescence imaging to provide an order of magnitude increase in screening throughput. Moreover, unlike current techniques that are limited to screening for a single biosensor feature at a time (e.g. brightness), our method enables evaluation of multiple features (e.g. contrast, affinity, specificity) in parallel. Because biosensor features can covary, this capability is essential for rapid optimization. We use this system to generate a high-performance biosensor for lactate that can be used to quantify intracellular lactate concentrations. This biosensor, named LiLac, constitutes a significant advance in metabolite sensing and demonstrates the power of our screening approach. Fluorescent biosensors are important tools for studying cellular metabolism, but development and optimization are challenging. Koveal et al. present a high-throughput multiparameter screen for sensor performance, and used it to generate LiLac, a high-performance, quantitative lactate sensor.

Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods. We apply ALDE to an engineering landscape that is challenging for DE: optimization of five epistatic residues in the active site of an enzyme. In three rounds of wet-lab experimentation, we improve the yield of a desired product of a non-native cyclopropanation reaction from 12% to 93%. We also perform computational simulations on existing protein sequence-fitness datasets to support our argument that ALDE can be more effective than DE. Overall, ALDE is a practical and broadly applicable strategy to unlock improved protein engineering outcomes. Directed evolution is a powerful method to optimize protein fitness. Here, authors develop an active learning workflow using machine learning to more efficiently explore the design space of proteins.

Red light penetrates deep into mammalian tissues and has low phototoxicity, but few optogenetic tools that use red light have been developed. Here we present MagRed, a red light–activatable photoswitch that consists of a red light–absorbing bacterial phytochrome incorporating a mammalian endogenous chromophore, biliverdin and a photo-state-specific binder that we developed using Affibody library selection. Red light illumination triggers the binding of the two components of MagRed and the assembly of split-proteins fused to them. Using MagRed, we developed a red light–activatable Cre recombinase, which enables light-activatable DNA recombination deep in mammalian tissues. We also created red light–inducible transcriptional regulators based on CRISPR–Cas9 that enable an up to 378-fold activation (average, 135-fold induction) of multiple endogenous target genes. MagRed will facilitate optogenetic applications deep in mammalian organisms in a variety of biological research areas. A photoswitch based on a bacterial phytochrome enables optogenetic manipulations using red light.

A genetically encoded peroxidase with improved sensitivity, APEX2, is reported for electron microscopy and proximity labeling at low expression levels. APEX is an engineered peroxidase that functions as an electron microscopy tag and a promiscuous labeling enzyme for live-cell proteomics. Because limited sensitivity precludes applications requiring low APEX expression, we used yeast-display evolution to improve its catalytic efficiency. APEX2 is far more active in cells, enabling the use of electron microscopy to resolve the submitochondrial localization of calcium uptake regulatory protein MICU1. APEX2 also permits superior enrichment of endogenous mitochondrial and endoplasmic reticulum membrane proteins.

Self-complementing split fluorescent proteins (FPs) have been widely used for protein labeling, visualization of subcellular protein localization, and detection of cell–cell contact. To expand this toolset, we have developed a screening strategy for the direct engineering of self-complementing split FPs. Via this strategy, we have generated a yellow–green split-mNeonGreen2 1–10/11 that improves the ratio of complemented signal to the background of FP 1–10 -expressing cells compared to the commonly used split GFP 1–10/11 ; as well as a 10-fold brighter red-colored split-sfCherry2 1–10/11 . Based on split sfCherry2, we have engineered a photoactivatable variant that enables single-molecule localization-based super-resolution microscopy. We have demonstrated dual-color endogenous protein tagging with sfCherry2 11 and GFP 11 , revealing that endoplasmic reticulum translocon complex Sec61B has reduced abundance in certain peripheral tubules. These new split FPs not only offer multiple colors for imaging interaction networks of endogenous proteins, but also hold the potential to provide orthogonal handles for biochemical isolation of native protein complexes. Split fluorescent proteins (FPs) have been widely used to visualise proteins in cells. Here the authors develop a screen for engineering new split FPs, and report a yellow-green split-mNeonGreen2 with reduced background, a red split-sfCherry2 for multicolour labeling, and its photoactivatable variant for super-resolution use.

Succinic acid (SA), a dicarboxylic acid of industrial importance, can be efficiently produced by metabolically engineered Mannheimia succiniciproducens . Malate dehydrogenase (MDH) is one of the key enzymes for SA production, but has not been well characterized. Here we report biochemical and structural analyses of various MDHs and development of hyper-SA producing M. succiniciproducens by introducing the best MDH. Corynebacterium glutamicum MDH ( Cg MDH) shows the highest specific activity and least substrate inhibition, whereas M. succiniciproducens MDH ( Ms MDH) shows low specific activity at physiological pH and strong uncompetitive inhibition toward oxaloacetate ( ki of 67.4 and 588.9 μM for Ms MDH and Cg MDH, respectively). Structural comparison of the two MDHs reveals a key residue influencing the specific activity and susceptibility to substrate inhibition. A high-inoculum fed-batch fermentation of the final strain expressing cgmdh produces 134.25 g L −1 of SA with the maximum productivity of 21.3 g L −1  h −1 , demonstrating the importance of enzyme optimization in strain development. Malate dehydrogenase (MDH) is one of the key enzymes for succinic acid (SA) bioproduction. Here, the authors report biochemical and structural analyses of various MDHs to reveal amino acids influencing the specific activity and susceptibility to substrate inhibition, and achieve industrial-level SA production.

While calcium imaging has become a mainstay of modern neuroscience, the spectral properties of current fluorescent calcium indicators limit deep-tissue imaging as well as simultaneous use with other probes. Using two monomeric near-infrared (NIR) fluorescent proteins (FPs), we engineered an NIR Förster resonance energy transfer (FRET)-based genetically encoded calcium indicator (iGECI). iGECI exhibits high levels of brightness and photostability and an increase up to 600% in the fluorescence response to calcium. In dissociated neurons, iGECI detects spontaneous neuronal activity and electrically and optogenetically induced firing. We validated the performance of iGECI up to a depth of almost 400 µm in acute brain slices using one-photon light-sheet imaging. Applying hybrid photoacoustic and fluorescence microscopy, we simultaneously monitored neuronal and hemodynamic activities in the mouse brain through an intact skull, with resolutions of ~3 μm (lateral) and ~25–50 μm (axial). Using two-photon imaging, we detected evoked and spontaneous neuronal activity in the mouse visual cortex, with fluorescence changes of up to 25%. iGECI allows biosensors and optogenetic actuators to be multiplexed without spectral crosstalk. A near-infrared fluorescent calcium indicator can be combined with other optogenetic tools in vivo without spectral crosstalk.