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Abstract A sustainable operation for harvesting metals in the lanthanide series is needed to meet the rising demand for rare earth elements across diverse global industries. However, existing methods are limited in their capacity for detection and capture at environmentally and industrially relevant lanthanide concentrations. Supercharged fluorescent proteins have solvent-exposed, negatively charged residues that potentially create multiple direct chelation pockets for free lanthanide cations. Here, we demonstrate that negatively supercharged proteins can bind and quantitatively report concentrations of lanthanides via an underutilized lanthanide-to-chromophore pathway of energy transfer. The top-performing sensors detect lanthanides in the micromolar to millimolar range and remain unperturbed by environmentally significant concentrations of competing metals. As a demonstration of the versatility and adaptability of this energy transfer method, we show proximity and signal transmission between the lanthanides and a supramolecular assembly of supercharged proteins, paving the way for the detection of lanthanides via programmable protein oligomers and materials.

Abstract Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias, generalization, and computation time, such as: Thermodynamic Permutations for data augmentation, structural amino acid embeddings to model a mutation with a single structure, a protein structure-specific attention-bias mechanism that makes transformers a viable alternative to graph neural networks. We provide training/test splits that mitigate data leakage and ensure proper model evaluation. Furthermore, to examine our data engineering contributions, we fine-tune ESM2 representations (Prostata-IFML) and achieve SOTA for sequence-based models. Notably, Stability Oracle outperforms Prostata-IFML even though it was pretrained on 2000X less proteins and has 548X less parameters. Our framework establishes a path for fine-tuning structure-based transformers to virtually any phenotype, a necessary task for accelerating the development of protein-based biotechnologies.

Abstract A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimer’s medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from the low-yielding daffodil Narcissus pseudonarcissus. Here, we propose an efficient biosensor-machine learning technology stack for biocatalyst development, which we apply to engineer an Amaryllidaceae enzyme in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC50 = 20 μM) and specific biosensor for the key Amaryllidaceae alkaloid branchpoint 4’-O-methylnorbelladine. A structure-based residual neural network (MutComputeX) is subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which are rapidly screened with the biosensor. Functional enzyme variants are identified that yield a 60% improvement in product titer, 2-fold higher catalytic activity, and 3-fold lower off-product regioisomer formation. A solved crystal structure elucidates the mechanism behind key beneficial mutations.

Ribosomes have evolved to polymerize L-α-amino acids into proteins comprising a peptide backbone. Here, a pyridazinone backbone is formed using ribosomes in vitro, producing a variety of sequence-defined alternating block-copolymers.

The yeast Saccharomyces cerevisiae is powerful for studying human G protein-coupled receptors as they can be coupled to its mating pathway. Here the authors engineer baker’s yeast to produce human sterols and show that vertebrate G protein coupled receptors are more sensitive in this membrane environment.

Flexizymes have been used to expand the scope of chemical substrates for ribosome-directed polymerization in vitro. Here the authors deduce design rules of Flexizyme-mediated tRNA acylation that more effectively predict the incorporation of new monomers into peptides.