The ß-subunit of tryptophan synthase is a latent tyrosine synthase.

Aromatic amino acids and their derivatives are diverse primary and secondary metabolites with critical roles in protein synthesis, cell structure and integrity, defense and signaling. All de novo aromatic amino acid production relies on a set of ancient and highly conserved chemistries. Here we introduce a new enzymatic transformation for L-tyrosine synthesis by demonstrating that the ß-subunit of tryptophan synthase-which natively couples indole and L-serine to form L-tryptophan-can act as a latent 'tyrosine synthase'. A single substitution of a near-universally conserved catalytic residue unlocks activity toward simple phenol analogs and yields exclusive para carbon-carbon bond formation to furnish L-tyrosines. Structural and mechanistic studies show how a new active-site water molecule orients phenols for a nonnative mechanism of alkylation, with additional directed evolution resulting in a net >30,000-fold rate enhancement. This new biocatalyst can be used to efficiently prepare valuable L-tyrosine analogs at gram scales and provides the missing chemistry for a conceptually different pathway to L-tyrosine.

Directed evolution of enzymatic silicon-carbon bond cleavage in siloxanes.

Volatile methylsiloxanes (VMS) are man-made, nonbiodegradable chemicals produced at a megaton-per-year scale, which leads to concern over their potential for environmental persistence, long-range transport, and bioaccumulation. We used directed evolution to engineer a variant of bacterial cytochrome P450BM3 to break silicon-carbon bonds in linear and cyclic VMS. To accomplish silicon-carbon bond cleavage, the enzyme catalyzes two tandem oxidations of a siloxane methyl group, which is followed by putative [1,2]-Brook rearrangement and hydrolysis. Discovery of this so-called siloxane oxidase opens possibilities for the eventual biodegradation of VMS.

DeCOIL: Optimization of Degenerate Codon Libraries for Machine Learning-Assisted Protein Engineering.

With advances in machine learning (ML)-assisted protein engineering, models based on data, biophysics, and natural evolution are being used to propose informed libraries of protein variants to explore. Synthesizing these libraries for experimental screens is a major bottleneck, as the cost of obtaining large numbers of exact gene sequences is often prohibitive. Degenerate codon (DC) libraries are a cost-effective alternative for generating combinatorial mutagenesis libraries where mutations are targeted to a handful of amino acid sites. However, existing computational methods to optimize DC libraries to include desired protein variants are not well suited to design libraries for ML-assisted protein engineering. To address these drawbacks, we present DEgenerate Codon Optimization for Informed Libraries (DeCOIL), a generalized method that directly optimizes DC libraries to be useful for protein engineering: to sample protein variants that are likely to have both high fitness and high diversity in the sequence search space. Using computational simulations and wet-lab experiments, we demonstrate that DeCOIL is effective across two specific case studies, with the potential to be applied to many other use cases. DeCOIL offers several advantages over existing methods, as it is direct, easy to use, generalizable, and scalable. With accompanying software (https://github.com/jsunn-y/DeCOIL), DeCOIL can be readily implemented to generate desired informed libraries.

Machine Learning for Protein Engineering.

Directed evolution of proteins has been the most effective method for protein engineering. However, a new paradigm is emerging, fusing the library generation and screening approaches of traditional directed evolution with computation through the training of machine learning models on protein sequence fitness data. This chapter highlights successful applications of machine learning to protein engineering and directed evolution, organized by the improvements that have been made with respect to each step of the directed evolution cycle. Additionally, we provide an outlook for the future based on the current direction of the field, namely in the development of calibrated models and in incorporating other modalities, such as protein structure.

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. Primarily, this is because sequencing is unnecessary for many protein engineering strategies; the added cost and effort of sequencing are thus unjustified. It also results from the fact that, even though many lower-cost sequencing strategies have been developed, they often require at least some access to and experience with sequencing or computational resources, both of which can be barriers to access. Here, we present every variant sequencing (evSeq), a method and collection of tools/standardized components for sequencing a variable region within every variant gene produced during a protein engineering campaign at a cost of cents per variant. evSeq was designed to democratize low-cost sequencing for protein engineers and, indeed, anyone interested in engineering biological systems. Execution of its wet-lab component 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 the collection of extensive protein variant sequence-fitness data practical.

Protein sequence design with deep generative models.

Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.

Advances in machine learning for directed evolution.

Machine learning (ML) can expedite directed evolution by allowing researchers to move expensive experimental screens in silico. Gathering sequence-function data for training ML models, however, can still be costly. In contrast, raw protein sequence data is widely available. Recent advances in ML approaches use protein sequences to augment limited sequence-function data for directed evolution. We highlight contributions in a growing effort to use sequences to reduce or eliminate the amount of sequence-function data needed for effective in silico screening. We also highlight approaches that use ML models trained on sequences to generate new functional sequence diversity, focusing on strategies that use these generative models to efficiently explore vast regions of protein space.