Biology subjects

Almhjell, P. J.

Publications and source records attributed to Almhjell, P. J..

5 recordsLinked to original sources

bioRxiv2026

Continuous hypermutation and evolution of noncanonical amino acid synthases

Genetic code expansion (GCE) enables the site-specific incorporation of noncanonical amino acids (ncAAs) into proteins but is constrained by reliance on exogenously supplied chiral ncAAs. Achieving intracellular ncAA biosynthesis would enable more scalable and cost-effective GCE. Here, we report the continuous hypermutation and evolution of amino acid synthases that produce high levels of ncAAs inside yeast, thus supporting GCE from simple ncAA precursors. We encoded an engineered tyrosine synthase (TmTyrS) on an error-prone orthogonal DNA replication system (OrthoRep) and selected variants based on ncAA biosynthesis from readily available phenol analogs and intracellular L-serine. Our selection employed orthogonal ncAA-specific aminoacyl-tRNA synthetases (aaRSs) as biosensors whereby target ncAA production leads to aminoacylation of an amber suppressor tRNA and the translation of a selectable reporter containing an amber stop codon. Our evolution successfully yielded TmTyrS variants that efficiently produced 3-iodo-, 3-bromo-, 3-chloro-, and 3-methyl-L-tyrosine, enabling amber codon-specified ncAA-dependent translation, in some cases at levels comparable to sense codon-specified natural amino acid translation. This work reduces barriers for expressing proteins containing substituted tyrosines. Moreover, because aaRSs can themselves be evolved (including with OrthoRep) for a flexible range of ncAA specificities, these results establish an end-to-end framework for evolving ncAA biosynthetic enzymes in vivo. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=50 SRC="FIGDIR/small/711232v1_ufig1.gif" ALT="Figure 1"> View larger version (10K): org.highwire.dtl.DTLVardef@a7fe66org.highwire.dtl.DTLVardef@12eed57org.highwire.dtl.DTLVardef@1dff391org.highwire.dtl.DTLVardef@1efc4f0_HPS_FORMAT_FIGEXP M_FIG C_FIG We describe an OrthoRep-driven platform for evolving noncanonical amino acid (ncAA) synthases. Hypermutation of ncAA synthase genes enables evolution of ncAA biosynthesis from simple precursors, while intracellular ncAA production is linked to fluorescence via an orthogonal aaRS/tRNA system, allowing FACS enrichment of improved variants through iterative cycles.

synthetic biology↗

bioRxiv2026

uSort-M: Scalable isolation of user-defined sequences from diverse pooled libraries

Advances in high-throughput sequencing and computational protein design are expanding the catalog of known protein sequences far more rapidly than they can be functionally characterized. Functional characterization through biochemical and biophysical assays often requires isolating variants to profile them individually, a process which is often laborious and expensive. To address this, we developed user-defined Sorted Mutants (uSort-M), which can rapidly isolate and identify individual variants from widely available pools of genes by leveraging automated cell sorting and long-read sequencing technologies. To develop the uSort-M pipeline, we first parsed a 328-member scanning mutagenesis library of a 300-bp gene. Direct comparison between short-read and long-read sequencing demonstrated that both methods resolve variants with high fidelity, enabling recovery of 96% of desired library members by sorting eight 384-well plates at fivefold lower cost than traditional synthesis. After optimizing for long-read sequencing, we demonstrated uSort-Ms generalizability to complex libraries by parsing a sequence- and length-diverse 500-member library, recovering 88% of variants from shallow oversampling (<3-fold). Library recoveries could be accurately predicted by simulating library uniformity, transformation number, sorting efficiency, and per-base error rates, and we extrapolated these simulations to predict the sampling depth needed to process large libraries containing thousands of members. To facilitate adoption, these simulations are packaged alongside data analysis and workflow management tools in an open-source Python toolkit with an interactive dashboard. By implementing standard instrumentation in a generalizable workflow, uSort-M provides an efficient and cost-effective solution for large library generation, thereby removing a key barrier to large-scale protein functional characterization.

synthetic biology↗

bioRxiv2024

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-- non-additive interactions between amino acid substitutions--and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable {beta}-subunit of tryptophan synthase (TrpB) in a non-native environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences--a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a new, large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling. Significance statementPredictive models for protein engineering seek to capture the relationship between protein sequence and function. While many methods and datasets exist for predicting the effects of single substitutions across a range of protein functions, fewer capture interactions among substitutions, which are much more difficult to predict. Even fewer do this comprehensively for a catalytic function. To provide a testbed for evaluating predictive models for enzyme engineering, we constructed and analyzed a 160,000-member enzyme sequence-fitness dataset at four interacting residues near the active site of tryptophan synthase, capturing significant non-additive effects of substitutions on catalytic function. It is necessary to predict and understand such interactions in order to efficiently traverse evolutionary landscapes and build machine learning models that accelerate protein engineering.

bioengineering↗

bioRxiv2021

evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library

Widespread availability of protein sequence-fitness data would revolutionize both our biochemical understanding of proteins and our ability to engineer them. Unfortunately, even though thousands of protein variants are generated and evaluated for fitness during a typical protein engineering campaign, most are never sequenced, leaving a wealth of potential sequence-fitness information untapped. This largely stems from the fact that sequencing is unnecessary for many protein engineering strategies; the added cost and effort of sequencing is thus unjustified. Here, we present every variant sequencing (evSeq), an efficient protocol for sequencing a variable region within every variant gene produced during a protein engineering campaign at a cost of cents per variant. Execution of evSeq is simple, requires no sequencing experience to perform, relies only on resources and services typically available to biology labs, and slots neatly into existing protein engineering workflows. Analysis of evSeq data is likewise made simple by its accompanying software (found at github.com/fhalab/evSeq, documentation at fhalab.github.io/evSeq), which can be run on a personal laptop and was designed to be accessible to users with no computational experience. Low-cost and easy to use, evSeq makes collection of extensive protein variant sequence-fitness data practical.

bioengineering↗

bioRxiv2020

Scalable, continuous evolution for the generation of diverse enzyme variants encompassing promiscuous activities

Enzyme orthologs sharing identical primary functions can have different promiscuous activities. While it is possible to mine this natural diversity to obtain useful biocatalysts, generating comparably rich ortholog diversity is difficult, as it is the product of deep evolutionary processes occurring in a multitude of separate species and populations. Here, we take a first step in recapitulating the depth and scale of natural ortholog evolution on laboratory timescales. Using a continuous directed evolution platform called OrthoRep, we rapidly evolved the Thermotoga maritima tryptophan synthase β-subunit (TmTrpB) through multi-mutation pathways in many independent replicates, selecting only onTmTrpB’s primary activity (synthesizing L-tryptophan from indole and L-serine). We find that the resulting sequence-diverseTmTrpB variants span a range of substrate profiles useful in industrial biocatalysis and suggest that the depth and scale of evolution that OrthoRep affords will be generally valuable in enzyme engineering and the evolution of new biomolecular functions.Competing Interest StatementC.E.B and F.H.A. are co-founders of Aralez Bio, focusing on the enzymatic synthesis of unnatural amino acids.View Full Text

synthetic biology↗