Ancestral lysosomal enzymes with increased activity harbor therapeutic potential for treatment of Hunter syndrome

Degradation of PET microplastic particles to monomers in human serum by PETase

More than 8 billion tons of plastic waste has been generated, posing severe environmental consequences and health risks. Due to prolonged exposure, microplastic particles are found in human blood and other bodily fluids. Despite a lack of toxicity studies regarding microplastics, harmful effects for humans seem plausible and cannot be excluded. As small plastic particles readily translocate from the gut to body fluids, enzyme-based treatment of serum could constitute a promising future avenue to clear synthetic polymers and their corresponding oligomers via their degradation into monomers of lower toxicity than the material they originate from. Still, whereas it is known that the enzymatic depolymerization rate of synthetic polymers varies by orders of magnitude depending on the buffer and media composition, the activity of plastic-degrading enzymes in serum was unknown. Here, we report how an engineered PETase, which we show to be generally trans-selective via induced fit docking, can depolymerize two different microplastic-like substrates of the commodity polymer polyethylene terephthalate (PET) into its non-toxic monomer terephthalic acid (TPA) alongside mono(2-hydroxyethyl)terephthalate (MHET) in human serum at 37 °C. We show that the application of PETase does not influence cell viability in vitro. Our work highlights the potential of applying biocatalysis in biomedicine and represents a first step towards finding a future solution to the problem that microplastics in the bloodstream may pose.

Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation

Protein language models trained on the masked language modeling objective learn to predict the identity of hidden amino acid residues within a sequence using the remaining observable sequence as context. They do so by embedding the residues into a high dimensional space that encapsulates the relevant contextual cues. These embedding vectors serve as an informative context-sensitive representation that not only aids with the defined training objective, but can also be used for other tasks by downstream models. We propose a scheme to use the embeddings of an unmasked sequence to estimate the corresponding masked probability vectors for all the positions in a single forward pass through the language model. This One Fell Swoop (OFS) approach allows us to efficiently estimate the pseudo-perplexity of the sequence, a measure of the model's uncertainty in its predictions, that can also serve as a fitness estimate. We find that ESM2 OFS pseudo-perplexity performs nearly as well as the true pseudo-perplexity at fitness estimation, and more notably it defines a new state of the art on the ProteinGym Indels benchmark. The strong performance of the fitness measure prompted us to investigate if it could be used to detect the elevated stability reported in reconstructed ancestral sequences. We find that this measure ranks ancestral reconstructions as more fit than extant sequences. Finally, we show that the computational efficiency of the technique allows for the use of Monte Carlo methods that can rapidly explore functional sequence space.

Pseudo-perplexity in One Fell Swoop for Protein Fitness Estimation

Protein language models trained on the masked language modeling objective learn to predict the identity of hidden amino acid residues within a sequence using the remaining observable sequence as context. They do so by embedding the residues into a high dimensional space that encapsulates the relevant contextual cues. These embedding vectors serve as an informative context-sensitive representation that not only aids with the defined training objective, but can also be used for other tasks by downstream models. We propose a scheme to use the embeddings of an unmasked sequence to estimate the corresponding masked probability vectors for all the positions in a single forward pass through the language model. This One Fell Swoop (OFS) approach allows us to efficiently estimate the pseudo-perplexity of the sequence, a measure of the model's uncertainty in its predictions, that can also serve as a fitness estimate. We find that ESM2 OFS pseudo-perplexity performs nearly as well as the true pseudo-perplexity at fitness estimation, and more notably it defines a new state of the art on the ProteinGym Indels benchmark. The strong performance of the fitness measure prompted us to investigate if it could be used to detect the elevated stability reported in reconstructed ancestral sequences. We find that this measure ranks ancestral reconstructions as more fit than extant sequences. Finally, we show that the computational efficiency of the technique allows for the use of Monte Carlo methods that can rapidly explore functional sequence space.

Humanization and functional characterization of enhanced coagulation factor IX variants identified through ancestral sequence reconstruction

BACKGROUND

Laboratory resurrection of ancient coagulation factor (F) IX variants generated through ancestral sequence reconstruction led to the discovery of a FIX variant, designated An96, which possesses enhanced specific activity independent of and additive to that provided by human p.Arg384Lys, referred to as FIX-Padua.

OBJECTIVES

The goal of the current study was to identify the amino acid substitution(s) responsible for the enhanced activity of An96 and create a humanized An96 FIX transgene for gene therapy application.

METHODS

Reductionist screening approaches, including domain swapping and scanning residue substitution, were used and guided by one-stage FIX activity assays. In vitro characterization of top candidates included recombinant high-purity preparation, specific activity determination, and enzyme kinetic analysis. Final candidates were packaged into adeno-associated viral (AAV) vectors and delivered to hemophilia B mice.

RESULTS

Five of 42 total amino acid substitutions in An96 appear sufficient to retain the enhanced activity of An96 in an otherwise human FIX variant. Additional substitution of the Padua variant further increased the specific activity 5-fold. This candidate, designated ET9, demonstrated 51-fold greater specific activity than hFIX. AAV2/8-ET9 treated hemophilia B mice produced plasma FIX activities equivalent to those observed previously for AAV2/8-An96-Padua, which were 10-fold higher than AAV2/8-hFIX-Padua.

CONCLUSION

Starting from computationally inferred ancient FIX sequences, novel amino acid substitutions conferring activity enhancement were identified and translated into an AAV-FIX gene therapy cassette demonstrating high potency. This ancestral sequence reconstruction discovery and sequence mapping refinement approach represents a promising platform for broader protein drug and gene therapy candidate optimization.

Mechanistic Insight into Peptidyl-Cysteine Oxidation by the Copper-Dependent Formylglycine-Generating Enzyme

The copper-dependent formylglycine-generating enzyme (FGE) catalyzes the oxygen-dependent oxidation of specific peptidyl-cysteine residues to formylglycine. Our QM/MM calculations provide a very likely mechanism for this transformation. The reaction starts with dioxygen binding to the tris-thiolate Cu^I center to form a triplet Cu^II -superoxide complex. The rate-determining hydrogen atom abstraction involves a triplet-singlet crossing to form a Cu^II -OOH species that couples with the substrate radical, leading to a Cu^I -alkylperoxo intermediate. This is accompanied by proton transfer from the hydroperoxide to the S atom of the substrate via a nearby water molecule. The subsequent O-O bond cleavage is coupled with the C-S bond breaking that generates the formylglycine and a Cu^II -oxyl complex. Moreover, our results suggest that the aldehyde oxygen of the final product originates from O₂ , which will be useful for future experimental work.

Data-driven enzyme engineering to identify function-enhancing enzymes

Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.