The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal biochemical functions. The method is an iterative machine learning process by which experimental data is deposited into a mathematical algorithm that selects potential peptide substrates to be tested experimentally. Once tested, the algorithm uses the experimental data to refine future selections. This process is repeated until a suitable set of de novo peptide substrates are discovered. We employed this technology to discover orthogonal peptide substrates for 4’-phosphopantetheinyl transferase, an enzyme class that covalently modifies proteins. In this manner, we have demonstrated that machine learning can be leveraged to guide peptide optimization for specific biochemical functions not immediately accessible by biological screening techniques, such as phage display and random mutagenesis.

The thiolato complex [Pt(II)(bipyridine)(N,S-aminoethanethiolate)]+ Cl- (1) undergoes sequential reactions with singlet oxygen to initially form the corresponding sulfenato complex [Pt(II)(bipyridine)(N,S(=O)-aminoethansulfenate)]+ (2) followed by a much slower reaction to the corresponding sulfinato complex. In contrast with many Pt dithiolato complexes, 1 does not produce any singlet oxygen, but its rate constant for singlet oxygen removal (kT) is quite large (3.2 × 107 M-1sec-1) and chemical reaction accounts for ca. 25 % of the value of kT). The behavior of 1 is strikingly different from the complex Pt(II)(bipyridine)(1,2-benzenditholate) (4). The latter complex reacts with 1O2 (either from an external sensitizer or via a self-sensitized pathway) to form a sulfinato complex. These two very different reactivity pathways imply different mechanistic pathways: The reaction of 1 with 1O2 must involve O-O bond cleavage and intermolecular oxygen atom transfer, while the reactive intermediate in complex 4 collapses intramolecularly to the sulfinato moiety.

An ideal target for metabolic engineering, fatty acid biosynthesis remains poorly understood on a molecular level. These carrier protein-dependent pathways require fundamental protein–protein interactions to guide reactivity and processivity, and their control has become one of the major hurdles in successfully adapting these biological machines. Our laboratory has developed methods to prepare acyl carrier proteins (ACPs) loaded with substrate mimetics and cross-linkers to visualize and trap interactions with partner enzymes, and we continue to expand the tools for studying these pathways. We now describe application of the slow-onset, tight-binding inhibitor triclosan to explore the interactions between the type II fatty acid ACP from Escherichia coli, AcpP, and its corresponding enoyl-ACP reductase, FabI. We show that the AcpP–triclosan complex demonstrates nM binding, inhibits in vitro activity, and can be used to isolate FabI in complex proteomes.

Enoyl-acyl carrier protein reductase (ENR), a critical enzyme in type II fatty acid biosynthesis, is a promising target for drug discovery against hepatocyte-stage Plasmodium falciparum. In order to identify PfENR-specific inhibitors, we docked 70 FDA-approved, bioactive, and/or natural product small molecules known to inhibit the growth of whole-cell blood-stage P. falciparum into several PfENR crystallographic structures. Subsequent in vitro activity assays identified a noncompetitive low-micromolar PfENR inhibitor, celastrol, from this set of compounds.

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