Phage-assisted evolution and protein engineering yield compact, efficient prime editors.

Prime editing enables a wide variety of precise genome edits in living cells. Here we use protein evolution and engineering to generate prime editors with reduced size and improved efficiency. Using phage-assisted evolution, we improved editing efficiencies of compact reverse transcriptases by up to 22-fold and generated prime editors that are 516-810 base pairs smaller than the current-generation editor PEmax. We discovered that different reverse transcriptases specialize in different types of edits and used this insight to generate reverse transcriptases that outperform PEmax and PEmaxΔRNaseH, the truncated editor used in dual-AAV delivery systems. Finally, we generated Cas9 domains that improve prime editing. These resulting editors (PE6a-g) enhance therapeutically relevant editing in patient-derived fibroblasts and primary human T-cells. PE6 variants also enable longer insertions to be installed in vivo following dual-AAV delivery, achieving 40% loxP insertion in the cortex of the murine brain, a 24-fold improvement compared to previous state-of-the-art prime editors.

Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.

The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

Directed evolution of a family of AAV capsid variants enabling potent muscle-directed gene delivery across species.

Replacing or editing disease-causing mutations holds great promise for treating many human diseases. Yet, delivering therapeutic genetic modifiers to specific cells in vivo has been challenging, particularly in large, anatomically distributed tissues such as skeletal muscle. Here, we establish an in vivo strategy to evolve and stringently select capsid variants of adeno-associated viruses (AAVs) that enable potent delivery to desired tissues. Using this method, we identify a class of RGD motif-containing capsids that transduces muscle with superior efficiency and selectivity after intravenous injection in mice and non-human primates. We demonstrate substantially enhanced potency and therapeutic efficacy of these engineered vectors compared to naturally occurring AAV capsids in two mouse models of genetic muscle disease. The top capsid variants from our selection approach show conserved potency for delivery across a variety of inbred mouse strains, and in cynomolgus macaques and human primary myotubes, with transduction dependent on target cell expressed integrin heterodimers.

VEGAS as a Platform for Facile Directed Evolution in Mammalian Cells.

Directed evolution, artificial selection toward designed objectives, is routinely used to develop new molecular tools and therapeutics. Successful directed molecular evolution campaigns repeatedly test diverse sequences with a designed selective pressure. Unicellular organisms and their viral pathogens are exceptional for this purpose and have been used for decades. However, many desirable targets of directed evolution perform poorly or unnaturally in unicellular backgrounds. Here, we present a system for facile directed evolution in mammalian cells. Using the RNA alphavirus Sindbis as a vector for heredity and diversity, we achieved 24-h selection cycles surpassing 10-3 mutations per base. Selection is achieved through genetically actuated sequences internal to the host cell, thus the system's name: viral evolution of genetically actuating sequences, or "VEGAS." Using VEGAS, we evolve transcription factors, GPCRs, and allosteric nanobodies toward functional signaling endpoints each in less than 1 weeks' time.

Understanding and Improving the Activity of Flavin-Dependent Halogenases via Random and Targeted Mutagenesis.

Flavin-dependent halogenases (FDHs) catalyze the halogenation of organic substrates by coordinating reactions of reduced flavin, molecular oxygen, and chloride. Targeted and random mutagenesis of these enzymes have been used to both understand and alter their reactivity. These studies have led to insights into residues essential for catalysis and FDH variants with improved stability, expanded substrate scope, and altered site selectivity. Mutations throughout FDH structures have contributed to all of these advances. More recent studies have sought to rationalize the impact of these mutations on FDH function and to identify new FDHs to deepen our understanding of this enzyme class and to expand their utility for biocatalytic applications.

Scalable, Continuous Evolution of Genes at Mutation Rates above Genomic Error Thresholds.

Directed evolution is a powerful approach for engineering biomolecules and understanding adaptation. However, experimental strategies for directed evolution are notoriously labor intensive and low throughput, limiting access to demanding functions, multiple functions in parallel, and the study of molecular evolution in replicate. We report OrthoRep, an orthogonal DNA polymerase-plasmid pair in yeast that stably mutates ∼100,000-fold faster than the host genome in vivo, exceeding the error threshold of genomic replication that causes single-generation extinction. User-defined genes in OrthoRep continuously and rapidly evolve through serial passaging, a highly straightforward and scalable process. Using OrthoRep, we evolved drug-resistant malarial dihydrofolate reductases (DHFRs) in 90 independent replicates. We uncovered a more complex fitness landscape than previously realized, including common adaptive trajectories constrained by epistasis, rare outcomes that avoid a frequent early adaptive mutation, and a suboptimal fitness peak that occasionally traps evolving populations. OrthoRep enables a new paradigm of routine, high-throughput evolution of biomolecular and cellular function.

Computationally guided AAV engineering for enhanced gene delivery.

Gene delivery vehicles based on adeno-associated viruses (AAVs) are enabling increasing success in human clinical trials, and they offer the promise of treating a broad spectrum of both genetic and non-genetic disorders. However, delivery efficiency and targeting must be improved to enable safe and effective therapies. In recent years, considerable effort has been invested in creating AAV variants with improved delivery, and computational approaches have been increasingly harnessed for AAV engineering. In this review, we discuss how computationally designed AAV libraries are enabling directed evolution. Specifically, we highlight approaches that harness sequences outputted by next-generation sequencing (NGS) coupled with machine learning (ML) to generate new functional AAV capsids and related regulatory elements, pushing the frontier of what vector engineering and gene therapy may achieve.

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

RNA-catalyzed evolution of catalytic RNA.

An RNA polymerase ribozyme that was obtained by directed evolution can propagate a functional RNA through repeated rounds of replication and selection, thereby enabling Darwinian evolution. Earlier versions of the polymerase did not have sufficient copying fidelity to propagate functional information, but a new variant with improved fidelity can replicate the hammerhead ribozyme through reciprocal synthesis of both the hammerhead and its complement, with the products then being selected for RNA-cleavage activity. Two evolutionary lineages were carried out in parallel, using either the prior low-fidelity or the newer high-fidelity polymerase. The former lineage quickly lost hammerhead functionality as the population diverged toward random sequences, whereas the latter evolved new hammerhead variants with improved fitness compared to the starting RNA. The increase in fitness was attributable to specific mutations that improved the replicability of the hammerhead, counterbalanced by a small decrease in hammerhead activity. Deep sequencing analysis was used to follow the course of evolution, revealing the emergence of a succession of variants that progressively diverged from the starting hammerhead as fitness increased. This study demonstrates the critical importance of replication fidelity for maintaining heritable information in an RNA-based evolving system, such as is thought to have existed during the early history of life on Earth. Attempts to recreate RNA-based life in the laboratory must achieve further improvements in replication fidelity to enable the fully autonomous Darwinian evolution of RNA enzymes as complex as the polymerase itself.

Methods for the directed evolution of biomolecular interactions.

Noncovalent interactions between biomolecules such as proteins and nucleic acids coordinate all cellular processes through changes in proximity. Tools that perturb these interactions are and will continue to be highly valuable for basic and translational scientific endeavors. By taking cues from natural systems, such as the adaptive immune system, we can design directed evolution platforms that can generate proteins that bind to biomolecules of interest. In recent years, the platforms used to direct the evolution of biomolecular binders have greatly expanded the range of types of interactions one can evolve. Herein, we review recent advances in methods to evolve protein-protein, protein-RNA, and protein-DNA interactions.

Exploiting enzyme evolution for computational protein design.

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Perspectives on improving crop Rubisco by directed evolution.

Rubisco catalyses the entry of almost all CO2 into the biosphere and is often the rate-limiting step in plant photosynthesis and growth. Its notoriety as the most abundant protein on Earth stems from the slow and error-prone catalytic properties that require plants, cyanobacteria, algae and photosynthetic bacteria to produce it in high amounts. Efforts to improve the CO2-fixing properties of plant Rubisco has been spurred on by the discovery of more effective isoforms in some algae with the potential to significantly improve crop productivity. Incompatibilities between the protein folding machinery of leaf and algae chloroplasts have, so far, prevented efforts to transplant these more effective Rubisco variants into plants. There is therefore increasing interest in improving Rubisco catalysis by directed (laboratory) evolution. Here we review the advances being made in, and the ongoing challenges with, improving the solubility and/or carboxylation activity of differing non-plant Rubisco lineages. We provide perspectives on new opportunities for the directed evolution of crop Rubiscos and the existing plant transformation capabilities available to evaluate the extent to which Rubisco activity improvements can benefit agricultural productivity.

Influence of viral genome properties on polymerase fidelity.

The first step of viral evolution takes place during genome replication via the error-prone viral polymerase. Among the mutants that arise through this process, only a few well-adapted variants will be selected by natural selection, renewing the viral genome population. Viral polymerase-mediated errors are thought to occur stochastically. However, accumulating evidence suggests that viral polymerase-mediated mutations are heterogeneously distributed throughout the viral genome. Here, we review work that supports this concept and provides mechanistic insights into how specific features of the viral genome could modulate viral polymerase-mediated errors. A predisposition to accumulate viral polymerase-mediated errors at specific loci in the viral genome may guide evolution to specific pathways, thus opening new directions of research to better understand viral evolutionary dynamics.

Genotype-phenotype landscapes for immune-pathogen coevolution.

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

Design, synthesis, and characterization of protein origami based on self-assembly of a brick and staple artificial protein pair.

A versatile strategy to create an inducible protein assembly with predefined geometry is demonstrated. The assembly is triggered by a binding protein that staples two identical protein bricks together in a predictable spatial conformation. The brick and staple proteins are designed for mutual directional affinity and engineered by directed evolution from a synthetic modular repeat protein library. As a proof of concept, this article reports on the spontaneous, extremely fast and quantitative self-assembly of two designed alpha-repeat (αRep) brick and staple proteins into macroscopic tubular superhelices at room temperature. Small-angle X-ray scattering (SAXS) and transmission electron microscopy (TEM with staining agent and cryoTEM) elucidate the resulting superhelical arrangement that precisely matches the a priori intended 3D assembly. The highly ordered, macroscopic biomolecular construction sustains temperatures as high as 75 °C thanks to the robust αRep building blocks. Since the α-helices of the brick and staple proteins are highly programmable, their design allows encoding the geometry and chemical surfaces of the final supramolecular protein architecture. This work opens routes toward the design and fabrication of multiscale protein origami with arbitrarily programmed shapes and chemical functions.

Engineering a human P2X2 receptor with altered ligand selectivity in yeast.

P2X receptors are a family of ligand gated ion channels found in a range of eukaryotic species including humans but are not naturally present in the yeast Saccharomyces cerevisiae. We demonstrate the first recombinant expression and functional gating of the P2X2 receptor in baker's yeast. We leverage the yeast host for facile genetic screens of mutant P2X2 by performing site saturation mutagenesis at residues of interest, including SNPs implicated in deafness and at residues involved in native binding. Deep mutational analysis and rounds of genetic engineering yield mutant P2X2 F303Y A304W, which has altered ligand selectivity toward the ATP analog AMP-PNP. The F303Y A304W variant shows over 100-fold increased intracellular calcium amplitudes with AMP-PNP compared to the WT receptor and has a much lower desensitization rate. Since AMP-PNP does not naturally activate P2X receptors, the F303Y A304W P2X2 may be a starting point for downstream applications in chemogenetic cellular control. Interestingly, the A304W mutation selectively destabilizes the desensitized state, which may provide a mechanistic basis for receptor opening with suboptimal agonists. The yeast system represents an inexpensive, scalable platform for ion channel characterization and engineering by circumventing the more expensive and time-consuming methodologies involving mammalian hosts.

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments.

Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

Development of CNS tropic AAV1-like variants with reduced liver-targeting following systemic administration in mice.

Directed evolution of natural AAV9 using peptide display libraries have been widely used in the search for an optimal recombinant AAV (rAAV) for transgene delivery across the blood-brain barrier (BBB) to the CNS following intravenous ( IV) injection. In this study, we used a different approach by creating a shuffled rAAV capsid library based on parental AAV serotypes 1 through 12. Following selection in mice, 3 novel variants closely related to AAV1, AAV-BBB6, AAV-BBB28, and AAV-BBB31, emerged as top candidates. In direct comparisons with AAV9, our novel variants demonstrated an over 270-fold improvement in CNS transduction and exhibited a clear bias toward neuronal cells. Intriguingly, our AAV-BBB variants relied on the LY6A cellular receptor for CNS entry, similar to AAV9 peptide variants AAV-PHP.eB and AAV.CAP-B10, despite the different bioengineering methods used and parental backgrounds. The variants also showed reduced transduction of both mouse liver and human primary hepatocytes in vivo. To increase clinical translatability, we enhanced the immune escape properties of our new variants by introducing additional modifications based on rational design. Overall, our study highlights the potential of AAV1-like vectors for efficient CNS transduction with reduced liver tropism, offering promising prospects for CNS gene therapies.

Evolving membrane-associated accessory protein variants for improved adeno-associated virus production.

Manufacturing sufficient adeno-associated virus (AAV) to meet current and projected clinical needs is a significant hurdle to the growing gene therapy industry. The recently discovered membrane-associated accessory protein (MAAP) is encoded by an alternative open reading frame in the AAV cap gene that is found in all presently reported natural serotypes. Recent evidence has emerged supporting a functional role of MAAP in AAV egress, although the underlying mechanisms of MAAP function remain unknown. Here, we show that inactivation of MAAP from AAV2 by a single point mutation that is silent in the VP1 open reading frame (ORF) (AAV2-ΔMAAP) decreased exosome-associated and secreted vector genome production. We hypothesized that novel MAAP variants could be evolved to increase AAV production and thus subjected a library encoding over 1 × 106 MAAP protein variants to five rounds of packaging selection into the AAV2-ΔMAAP capsid. Between each successive packaging round, we observed a progressive increase in both overall titer and ratio of secreted vector genomes conferred by the bulk-selected MAAP library population. Next-generation sequencing uncovered enriched mutational features, and a resulting selected MAAP variant containing missense mutations and a frameshifted C-terminal domain increased overall GFP transgene packaging in AAV2, AAV6, and AAV9 capsids.

A conserved mechanism affecting hydride shifting and deprotonation in the synthesis of hopane triterpenes as compositions of wax in oat.

Triterpenoids are biologically active metabolites synthesized from a common linear precursor catalyzed by 2,3-oxidosqualene cyclases (OSCs) to form diverse triterpenoid skeletons. OSCs corresponding to many discovered triterpene alcohols in nature have not been functionally and mechanistically characterized due to the diversity of chemical structures and complexity of the cyclization mechanism. We carried out a genome-wide investigation of OSCs from Avena strigosa and discovered two triterpene synthases, namely, AsHS1 and AsHS2, using a Nicotiana benthamiana expression system. These synthases produce hopenol B and hop-17(21)-en-3ß-ol, which are components of surface wax in oat panicles and sheathes, respectively. We demonstrated that substitutions of two to three amino acid residues in AsHS1 with corresponding residues from AsHS2 allowed it to be completely converted into a hop-17(21)-en-3ß-ol synthase. AsHS2 mutants with a substitution at site 410 could synthesize hopenol B alone or mixed with a side product isomotiol. The combined quantum mechanics and molecular mechanics calculation demonstrated that the side chain size of the residue at site 410 regulated the relative orientations between the hopyl C22 cation and Phe257, leading to a difference in deprotonation positions through providing or not providing cation­π interaction between the aromatic ring of F257 and the carbocation intermediate. A similar mechanism could be applied to a hopenol B synthase from a dicotyledonous plant Aquilegia. This study provided mechanistic insight into triterpenoid synthesis and discovered key amino acid residues acting on hydride transfer and a deprotonation site to differentiate between hopane-type scaffolds in diverse plant species.