Enzyme-Directed Functionalization of Designed, Two-Dimensional Protein Lattices

The design and construction of crystalline protein arrays to selectively assemble ordered nanoscale materials have potential applications in sensing, catalysis, and medicine. Whereas numerous designs have been implemented for the bottom-up construction of protein assemblies, the generation of artificial functional materials has been relatively unexplored. Enzyme-directed post-translational modifications are responsible for the functional diversity of the proteome and, thus, could be harnessed to selectively modify artificial protein assemblies. In this study, we describe the use of phosphopantetheinyl transferases (PPTases), a class of enzymes that covalently modify proteins using coenzyme A (CoA), to site-selectively tailor the surface of designed, two-dimensional (2D) protein crystals. We demonstrate that a short peptide (ybbR) or a molecular tag (CoA) can be covalently tethered to 2D arrays to enable enzymatic functionalization using Sfp PPTase. The site-specific modification of two different protein array platforms is facilitated by PPTases to afford both small molecule- and protein-functionalized surfaces with no loss of crystalline order. This work highlights the potential for chemoenzymatic modification of large protein surfaces toward the generation of sophisticated protein platforms reminiscent of the complex landscape of cell surfaces.

Hepatitis C virus NS3-4A protease regulates the lipid environment for RNA replication by cleaving host enzyme 24-dehydrocholesterol reductase

Many RNA viruses create specialized membranes for genome replication by manipulating host lipid metabolism and trafficking, but in most cases, we do not know the molecular mechanisms responsible or how specific lipids may impact the associated membrane and viral process. For example, hepatitis C virus (HCV) causes a specific, large-fold increase in the steady-state abundance of intracellular desmosterol, an immediate precursor of cholesterol, resulting in increased fluidity of the membrane where HCV RNA replication occurs. Here, we establish the mechanism responsible for HCV's effect on intracellular desmosterol, whereby the HCV NS3-4A protease controls activity of 24-dehydrocholesterol reductase (DHCR24), the enzyme that catalyzes conversion of desmosterol to cholesterol. Our cumulative evidence for the proposed mechanism includes immunofluorescence microscopy experiments showing co-occurrence of DHCR24 and HCV NS3-4A protease; formation of an additional, faster-migrating DHCR24 species (DHCR24*) in cells harboring a HCV subgenomic replicon RNA or ectopically expressing NS3-4A; and biochemical evidence that NS3-4A cleaves DHCR24 to produce DHCR24* in vitro and in vivo. We further demonstrate that NS3-4A cleaves DHCR24 between residues Cys⁹¹ and Thr⁹² and show that this reduces the intracellular conversion of desmosterol to cholesterol. Together, these studies demonstrate that NS3-4A directly cleaves DHCR24 and that this results in the enrichment of desmosterol in the membranes where NS3-4A and DHCR24 co-occur. Overall, this suggests a model in which HCV directly regulates the lipid environment for RNA replication through direct effects on the host lipid metabolism.

Discovering de novo peptide substrates for enzymes using machine learning

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 discovery of peptide substrates for enzymes with selective activities is a central goal in chemical biology. Here, the authors develop a hybrid method combining machine learning and experimental testing for fast optimization of peptides for specific, orthogononal functions.

Trapping of the Enoyl-Acyl Carrier Protein Reductase-Acyl Carrier Protein Interaction

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.

Sequential photooxidation of a Pt(II) (diimine)cysteamine complex: intermolecular oxygen atom transfer versus sulfinate formation

The thiolato complex [platinum(II) (bipyridine)(N,S-aminoethanethiolate)](+)Ch(-) (1) undergoes sequential reactions with singlet oxygen to initially form the corresponding sulfenato complex [platinum(II) (bipyridine)(N,S(═O)-aminoethansulfenate)](+) (2) followed by a much slower reaction to the corresponding sulfinato complex. In contrast with many platinum dithiolato complexes, 1 does not produce any singlet oxygen, but its rate constant for singlet oxygen removal (k(T)) is quite large (3.2 × 10(7) M(-1) s(-1)) and chemical reaction accounts for ca. 25% of the value of k(T). The behavior of 1 is strikingly different from that of the complex platinum(II) (bipyridine)(1,2-benzenditholate) (4). The latter complex reacts with (1)O(2) (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 (1)O(2) must involve O-O bond cleavage and intermolecular oxygen atom transfer, while the reactive intermediate in complex 4 collapses intramolecularly to the sulfinato moiety.