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.

Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression.

We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML(KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML(KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, and GDB-13-T) and open-shell (QMSpin) molecules.