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Alcohol-forming fatty acyl reductases (FARs) catalyze the reduction of thioesters to alcohols and are key enzymes for microbial production of fatty alcohols. Many metabolic engineering strategies utilize FARs to produce fatty alcohols from intracellular acyl-CoA and acyl-ACP pools; however, enzyme activity, especially on acyl-ACPs, remains a significant bottleneck to high-flux production. Here, we engineer FARs with enhanced activity on acyl-ACP substrates by implementing a machine learning (ML)-driven approach to iteratively search the protein fitness landscape. Over the course of ten design-test-learn rounds, we engineer enzymes that produce over twofold more fatty alcohols than the starting natural sequences. We characterize the top sequence and show that it has an enhanced catalytic rate on palmitoyl-ACP. Finally, we analyze the sequence-function data to identify features, like the net charge near the substrate-binding site, that correlate with in vivo activity. This work demonstrates the power of ML to navigate the fitness landscape of traditionally difficult-to-engineer proteins., Fatty acyl reductases (FARs) are critical enzymes in the biosynthesis of fatty alcohols and have the ability to directly acces acyl-ACP substrates. Here, authors couple machine learning-based protein engineering framework with gene shuffling to optimize a FAR for the activity on acyl-ACP and improve fatty alcohol production.

Understanding how SARS-CoV-2 interacts with different mammalian angiotensin-converting enzyme II (ACE2) cell entry receptors will help elucidate determinants of intra- and cross-species virus transmission, facilitate development of effective new vaccines for both humans and livestock animals, and guide livestock farming and coronavirus screening procedures to ensure food supply security. In this work we applied laboratory directed evolution to several mammalian ACE2s with the goal of identifying conserved ACE2 mutations that increase spike binding affinity across multiple species. We found the Gln42Leu mutation increased ACE2-spike binding for human as well as four of four other mammalian ACE2s, while the Leu79Ile mutation had a similar effect for human and three of three mammalian ACE2 orthologs. These results are especially notable given the residues' high levels of representation, i.e, 83% for Gln42 and 56% for Leu79, among annotated mammalian ACE2s. We also found that substitutions at ACE2 position 34, which is relatively variable across mammalian ACE2s, increased binding for multiple ACE2 orthologs. Taken together, these results speak strongly to the plausibility of SARS-CoV-2 strains with increased ability to cross species transmission barriers. Our results can guide further computational and experimental studies to develop biomedical technologies and animal husbandry practices that help protect both humans and livestock from existing and future SARS-CoV-2 variants.

Single-nucleotide polymorphisms (SNPs) are the most common source of genetic variation between individuals and have implications in human disease, pathogen drug resistance, and agriculture. SNPs are typically detected using DNA sequencing, which requires advanced sample preparation and instrumentation, and thus cannot be deployed for on-site testing or in low-resource settings. In this work we have developed a simple and robust assay to rapidly detect SNPs in nucleic acid samples. Our approach combines LAMP-based target amplification with fluorescent probes to detect SNPs with high specificity in a one-pot reaction format. A competitive “sink” strand preferentially binds to off-target products and shifts the free energy landscape to favor specific activation by SNP products. We demonstrated the broad utility and reliability of our SNP-LAMP method by detecting three distinct SNPs across the human genome. We also designed an assay to rapidly detect highly transmissible SARS-CoV-2 variants. This work demonstrates that competitive SNP-LAMP is a powerful and universal method that could be applied in point-of-care settings to detect any target SNP with high specificity and sensitivity.

The human caspase family comprises 12 cysteine proteases that are centrally involved in cell death and inflammation responses. The members of this family have conserved sequences and structures, highly similar enzymatic activities and substrate preferences, and overlapping physiological roles. In this paper, we present a deep mutational scan of the executioner caspases CASP3 and CASP7 to dissect differences in their structure, function, and regulation. Our approach leverages high-throughput microfluidic screening to analyze hundreds of thousands of caspase variants in tightly controlled in vitro reactions. The resulting data provides a large-scale and unbiased view of the impact of amino acid substitutions on the proteolytic activity of CASP3 and CASP7. We use this data to pinpoint key functional differences between CASP3 and CASP7, including a secondary internal cleavage site, CASP7 Q196 that is not present in CASP3. Our results will open avenues for inquiry in caspase function and regulation that could potentially inform the development of future caspasespecific therapeutics.

Understanding how human ACE2 genetic variants differ in their recognition by SARS-CoV-2 can facilitate the leveraging of ACE2 as an axis for treating and preventing COVID-19. In this work, we experimentally interrogate thousands of ACE2 mutants to identify over one hundred human single-nucleotide variants (SNVs) that are likely to have altered recognition by the virus, and make the complementary discovery that ACE2 residues distant from the spike interface influence the ACE2-spike interaction. These findings illuminate new links between ACE2 sequence and spike recognition, and could find substantial utility in further fundamental research that augments epidemiological analyses and clinical trial design in the contexts of both existing strains of SARS-CoV-2 and novel variants that may arise in the future.

Directed evolution continues to expand the capabilities of complex biomolecules for a range of applications, such as adeno-associated virus vectors for gene therapy; however, advances in library design and selection strategies are key to develop variants that overcome barriers to clinical translation. To address this need, we applied structure-guided SCHEMA recombination of the multimeric adeno-associated virus (AAV) capsid to generate a highly diversified chimeric library with minimal structural disruption. A stringent in vivo Cre-dependent selection strategy was implemented to identify variants that transduce adult neural stem cells (NSCs) in the subventricular zone. A novel variant, SCH9, infected 60% of NSCs and mediated 24-fold higher GFP expression and a 12-fold greater transduction volume than AAV9. SCH9 utilizes both galactose and heparan sulfate as cell surface receptors and exhibits increased resistance to neutralizing antibodies. These results establish the SCHEMA library as a valuable tool for directed evolution and SCH9 as an effective gene delivery vector to investigate subventricular NSCs.

Experimental methods that capture the individual properties of single cells are revealing the key role of cell-to-cell variability in countless biological processes. These single-cell methods are becoming increasingly important across the life sciences in fields such as immunology, regenerative medicine, and cancer biology. Existing single-cell analysis methods are often limited by their low analysis throughput, their inability to profile high-dimensional phenotypes, and complicated experimental workflows with slow turnaround times. In this work, we present Single-cell Nucleic Acid Profiling in Droplets (SNAPD) to analyze the transcriptional states of hundreds of thousands of single mammalian cells. Individual cells are encapsulated in aqueous droplets on a microfluidic chip and the content of each cell is profiled by amplifying a targeted panel of transcriptional markers. Molecular logic circuits then integrate this multi-dimensional information to categorize cells based on their transcriptional profile and produce a detectable fluorescence output. SNAPD analyzes over 100,000 cells per hour and can be used to quantify distinct cell types within populations, detect rare cells at frequencies down to 0.1%, and enrich specific cell types using microfluidic sorting. SNAPD provides a simple, rapid, low cost, and scalable approach to study complex phenotypes in heterogeneous cell populations.

We introduce a method for identifying elements of a protein structure that can be shuffled to make chimeric proteins from two or more homologous parents. Formulating recombination as a graph-partitioning problem allows us to identify noncontiguous segments of the sequence that should be inherited together in the progeny proteins. We demonstrate this noncontiguous recombination approach by constructing a chimera of β-glucosidases from two different kingdoms of life. Although the protein's alpha–beta barrel fold has no obvious subdomains for recombination, noncontiguous SCHEMA recombination generated a functional chimera that takes approximately half its structure from each parent. The X-ray crystal structure shows that the structural blocks that make up the chimera maintain the backbone conformations found in their respective parental structures. Although the chimera has lower β-glucosidase activity than the parent enzymes, the activity was easily recovered by directed evolution. This simple method, which does not rely on detailed atomic models, can be used to design chimeras that take structural, and functional, elements from distantly-related proteins.

Arginases catalyze the divalent cation-dependent hydrolysis of l-arginine to urea and l-ornithine. There is significant interest in using arginase as a therapeutic anti-neogenic agent against l-arginine auxotrophic tumors and in enzyme replacement therapy for treating hyperargininemia. Both therapeutic applications require enzymes with sufficient stability under physiological conditions. To explore sequence elements that contribute to arginase stability we used SCHEMA-guided recombination to design a library of chimeric enzymes composed of sequence fragments from the two human isozymes Arginase I and II. We then developed a novel active learning algorithm that selects sequences from this library that are both highly informative and functional. Using high-throughput gene synthesis and our two-step active learning algorithm, we were able to rapidly create a small but highly informative set of seven enzymatically active chimeras that had an average variant distance of 40 mutations from the closest parent arginase. Within this set of sequences, linear regression was used to identify the sequence elements that contribute to the long-term stability of human arginase under physiological conditions. This approach revealed a striking correlation between the isoelectric point and the long-term stability of the enzyme to deactivation under physiological conditions.

We describe an efficient SCHEMA recombination-based approach for screening homologous enzymes to identify stabilizing amino acid sequence blocks. This approach has been used to generate active, thermostable cellobiohydrolase class I (CBH I) enzymes from the 390 625 possible chimeras that can be made by swapping eight blocks from five fungal homologs. Constructing and characterizing the parent enzymes and just 32 ‘monomeras’ containing a single block from a homologous enzyme allowed stability contributions to be assigned to 36 of the 40 blocks from which the CBH I chimeras can be assembled. Sixteen of 16 predicted thermostable chimeras, with an average of 37 mutations relative to the closest parent, are more thermostable than the most stable parent CBH I, from the thermophilic fungus Talaromyces emersonii. Whereas none of the parent CBH Is were active >65°C, stable CBH I chimeras hydrolyzed solid cellulose at 70°C. In addition to providing a collection of diverse, thermostable CBH Is that can complement previously described stable CBH II chimeras (Heinzelman et al., Proc. Natl Acad. Sci. USA 2009;106:5610–5615) in formulating application-specific cellulase mixtures, the results show the utility of SCHEMA recombination for screening large swaths of natural enzyme sequence space for desirable amino acid blocks.

Directed evolution circumvents our profound ignorance of how a protein's sequence encodes its function by using iterative rounds of random mutation and artificial selection to discover new and useful proteins. Proteins can be tuned to adapt to new functions or environments via simple adaptive walks involving small numbers of mutations. Directed evolution studies have demonstrated how rapidly at least some proteins can evolve under strong selection pressures, and, because the entire ‘fossil record’ of evolutionary intermediates is available for detailed study, they have provided new insight into the relationship between sequence and function. Directed evolution has also shown how mutations that are functionally neutral can set the stage for further adaptation.

Natural enzymes are incredibly proficient catalysts, but engineering them to have new or improved functions is challenging due to the complexity of how an enzyme's sequence relates to its biochemical properties. Here, we present an ultrahigh-throughput method for mapping enzyme sequence-function relationships that combines droplet microfluidic screening with next-generation DNA sequencing. We apply our method to map the activity of millions of glycosidase sequence variants. Microfluidic-based deep mutational scanning provides a comprehensive and unbiased view of the enzyme function landscape. The mapping displays expected patterns of mutational tolerance and a strong correspondence to sequence variation within the enzyme family, but also reveals previously unreported sites that are crucial for glycosidase function. We modified the screening protocol to include a high-temperature incubation step, and the resulting thermotolerance landscape allowed the discovery of mutations that enhance enzyme thermostability. Droplet microfluidics provides a general platform for enzyme screening that, when combined with DNA-sequencing technologies, enables high-throughput mapping of enzyme sequence space.

Knowing how protein sequence maps to function (the “fitness landscape”) is critical for understanding protein evolution as well as for engineering proteins with new and useful properties. We demonstrate that the protein fitness landscape can be inferred from experimental data, using Gaussian processes, a Bayesian learning technique. Gaussian process landscapes can model various protein sequence properties, including functional status, thermostability, enzyme activity, and ligand binding affinity. Trained on experimental data, these models achieve unrivaled quantitative accuracy. Furthermore, the explicit representation of model uncertainty allows for efficient searches through the vast space of possible sequences. We develop and test two protein sequence design algorithms motivated by Bayesian decision theory. The first one identifies small sets of sequences that are informative about the landscape; the second one identifies optimized sequences by iteratively improving the Gaussian process model in regions of the landscape that are predicted to be optimized. We demonstrate the ability of Gaussian processes to guide the search through protein sequence space by designing, constructing, and testing chimeric cytochrome P450s. These algorithms allowed us to engineer active P450 enzymes that are more thermostable than any previously made by chimeragenesis, rational design, or directed evolution.

SCHEMA structure-guided recombination is an effective method for producing families of protein chimeras having high sequence diversity, functional diversity, and thermostabilities greater than any of the parent proteins from which the chimeras are made. A key feature of SCHEMA chimera families is their amenability to a “sample, model, and predict” operation that allows one to characterize members of a small chimera sample set and use those data to construct models that accurately predict the properties of every member of the family. In this chapter, we describe applications of this “sample, model, and predict” approach and outline methods for designing chimera sample sets that enable efficient construction of models to identify useful sequence elements. With these models we can also predict the sequences and properties of the most desirable chimeras.

The production of contrast agents sensitive to neuronal signaling events is a rate-limiting step in the development of molecular-level functional magnetic resonance imaging (molecular fMRI) approaches for studying the brain. High-throughput generation and evaluation of potential probes are possible using techniques for macromolecular engineering of protein-based contrast agents. In an initial exploration of this strategy, we used the method of directed evolution to identify mutants of a bacterial heme protein that allowed detection of the neurotransmitter dopamine in vitro and in living animals. The directed evolution method involves successive cycles of mutagenesis and screening that could be generalized to produce contrast agents sensitive to a variety of molecular targets in the nervous system.

We are interested in how intragenic recombination contributes to the evolution of proteins and how this mechanism complements and enhances the diversity generated by random mutation. Experiments have revealed that proteins are highly tolerant to recombination with homologous sequences (mutation by recombination is conservative); more surprisingly, they have also shown that homologous sequence fragments make largely additive contributions to biophysical properties such as stability. Here, we develop a random field model to describe the statistical features of the subset of protein space accessible by recombination, which we refer to as the recombinational landscape. This model shows quantitative agreement with experimental results compiled from eight libraries of proteins that were generated by recombining gene fragments from homologous proteins. The model reveals a recombinational landscape that is highly enriched in functional sequences, with properties dominated by a large-scale additive structure. It also quantifies the relative contributions of parent sequence identity, crossover locations, and protein fold to the tolerance of proteins to recombination. Intragenic recombination explores a unique subset of sequence space that promotes rapid molecular diversification and functional adaptation., Author Summary Mutation and recombination are the primary sources of genetic variation in evolving populations. The relative benefit of these two diversification mechanisms and how they complement each other has been a long-standing question in evolutionary biology. While it is clear what types of genetic diversity these two mechanisms can create, a significant challenge is relating these sequence changes to changes in fitness. The fitness landscape, which describes this mapping from genotype to phenotype, is extraordinarily complex and defined over an incomprehensibly large space of sequences. Here, we develop a model of the landscape that relies not on the details of this mapping, but rather on the statistical relationships between sequences. By studying the expected values of landscape properties, we can gain insights into the structure of the landscape that are independent of the details of how genotype dictates phenotype. We use this random field model to understand how recombination explores a functionally enriched and diverse subset of protein sequence space.