Rhodium(II) Proximity-Labeling Identifies a Novel Target Site on STAT3 for Inhibitors with Potent Anti-Leukemia Activity

Nearly 40 % of children with acute myeloid leukemia (AML) suffer relapse arising from chemoresistance, often involving upregulation of the oncoprotein STAT3 (signal transducer and activator of transcription 3). Herein, rhodium(II)-catalyzed, proximity-driven modification identifies the STAT3 coiled-coil domain (CCD) as a novel ligand-binding site, and we describe a new naphthalene sulfonamide inhibitor that targets the CCD, blocks STAT3 function, and halts its disease-promoting effects in vitro, in tumor growth models, and in a leukemia mouse model, validating this new therapeutic target for resistant AML

Histidine-Directed Arylation/Alkenylation of Backbone N–H Bonds Mediated by Copper(II)

Chemical modification of proteins and peptides represents a challenge of reaction design as well as an important biological tool. In contrast to side-chain modification, synthetic methods to alter backbone structure are extremely limited. In this communication, copper-mediated backbone <i>N</i>-alkenylation or <i>N</i>-arylation of peptides and proteins by direct modification of natural sequences is described. Histidine residues direct oxidative coupling of boronic acids at the backbone NH of a neighboring amino acid. The mild reaction conditions in common physiological buffers, at ambient temperature, are compatible with proteins and biological systems. This simple reaction demonstrates the potential for directed reactions in complex systems to allow modification of N−H bonds that directly affect polypeptide structure, stability, and function

Biosensor and machine learning-aided engineering of an amaryllidaceae enzyme

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