Biocatalysis offers an expanding and powerful strategy to construct and diversify complex molecules by C-H bond functionalization. Due to their high selectivity, enzymes have become an essential tool for C-H bond functionalization and offer complementary reactivity to small-molecule catalysts. Hemoproteins, particularly cytochromes P450, have proven effective for selective oxidation of unactivated C-H bonds. Previously, we reported the in vitro characterization of an oxidative tailoring cascade in which TamI, a multifunctional P450 functions co-dependently with the TamL flavoprotein to catalyze regio-and stereoselective hydroxylations and epoxidation to yield tirandamycin A and tirandamycin B. TamI follows a defined order including 1) C10 hydroxylation, 2) C11/C12 epoxidation, and 3) C18 hydroxylation. Here we present a structural, biochemical, and computational investigation of TamI to understand the molecular basis of its substrate binding, diverse reactivity, and specific reaction sequence. The crystal structure of TamI in complex with tirandamycin C together with molecular dynamics simulations and targeted mutagenesis suggest that hydrophobic interactions with the polyene chain of its natural substrate are critical for molecular recognition. QM/MM calculations and molecular dynamics simulations of TamI with variant substrates provided detailed information on the molecular basis of sequential reactivity, and pattern of regio-and stereo-selectivity in catalyzing the three-step oxidative cascade. File list (2) download file view on ChemRxiv TamI_structure_function_V15.pdf (1.66 MiB) download file view on ChemRxiv Supporting Information_V7.pdf (2.74 MiB) Molecular basis of iterative C-H oxidation by TamI, a multifunctional P450 monooxygenase from the tirandamycin biosynthetic pathway

The rate of reconfiguration—or intramolecular diffusion—of monomeric Alzheimer (Aβ) peptides is measured and, under conditions that aggregation is more likely, peptide diffusion slows down significantly, which allows bimolecular associations to be initiated. By using the method of Trp–Cys contact quenching, the rate of reconfiguration is observed to be about five times faster for Aβ40, which aggregates slowly, than that for Aβ42, which aggregates quickly. Furthermore, the rate of reconfiguration for Aβ42 speeds up at higher pH, which slows aggregation, and in the presence of the aggregation inhibitor curcumin. The measured reconfiguration rates are able to predict the early aggregation behavior of the Aβ peptide and provide a kinetic basis for why Aβ42 is more prone to aggregation than Aβ40, despite a difference of only two amino acids.

Prion diseases, like Alzheimer's disease and Parkinson disease, are rapidly progressive neurodegenerative disorders caused by misfolding followed by aggregation and accumulation of protein deposits in neuronal cells. Here we measure intramolecular polypeptide backbone reconfiguration as a way to understand the molecular basis of prion aggregation. Our hypothesis is that when reconfiguration is either much faster or much slower than bimolecular diffusion, biomolecular association is not stable, but as the reconfiguration rate becomes similar to the rate of biomolecular diffusion, the association is more stable and subsequent aggregation is faster. Using the technique of Trp-Cys contact quenching, we investigate the effects of various conditions on reconfiguration dynamics of the Syrian hamster and rabbit prion proteins. This protein exhibits behavior in all three reconfiguration regimes. We conclude that the hamster prion is prone to aggregation at pH 4.4 because its reconfiguration rate is slow enough to expose hydrophobic residues on the same time scale that bimolecular association occurs, whereas the rabbit sequence avoids aggregation by reconfiguring 10 times faster than the hamster sequence.protein folding | protein aggregation | intramolecular diffusion | prion disease | astemizole P rion disease, also known as transmissible spongiform encephalopathy (TSE) is attributed to misfolding followed by ordered aggregation and accumulation of protein deposits in neuronal cells (1-3). According to the "protein-only" hypothesis, the central event in prion (PrP) pathogenesis is the conformational conversion of the cellular α-helical rich isoform, PrP C , into misfolded β-sheet rich isoform, PrP Sc (3-6). The infectious form PrP Sc is believed to catalyze the conversion of PrP C , thereby permitting transmission between individuals and species (3, 7-10). However, there is very little known about PrP C before ordered aggregation. No putative aggregation precursor structure has been definitively identified; the protein may even be completely unstructured. It is believed that PrP C repeatedly cycles between the cell surface (∼pH 7.0) and endocytic compartment that has a pH as low as 4. 4 (11, 12). Aggregated prion has been isolated from this compartment so it has been suggested that aggregation initiates in late endosomes (13,14). Also, the reduction of the disulfide bridge has been reported to augment misfolding in vitro and may play a significant role in prion pathogenesis (15)(16)(17)(18)(19)(20). However, the physical basis for aggregation of prion, particularly under certain solvent conditions, is still poorly understood.Protein folding is a diffusive search of the unfolded polypeptide chain over the energy landscape in conformational space for a minimum energy state (21). Intramolecular diffusion (diffusion of one part of the chain relative to another) plays a critical role by determining the rate at which an unfolded polypeptide chain finds its native state (22-25). It has been measured for various peptides and pro...

The pathology of Parkinson’s disease and other synucleinopathies is characterized by the formation of intracellular inclusions comprised primarily of misfolded, fibrillar α-synuclein (α-syn). One strategy to slow disease progression is to prevent the misfolding and aggregation of its native monomeric form. Here we present findings that support the contention that the tricyclic antidepressant compound nortriptyline (NOR) has disease-modifying potential for synucleinopathies. Findings from in vitro aggregation and kinetics assays support the view that NOR inhibits aggregation of α-syn by directly binding to the soluble, monomeric form, and by enhancing reconfiguration of the monomer, inhibits formation of toxic conformations of the protein. We go on to demonstrate that NOR inhibits the accumulation, aggregation and neurotoxicity of α-syn in multiple cell and animal models. These findings suggest that NOR, a compound with established safety and efficacy for treatment of depression, may slow progression of α-syn pathology by directly binding to soluble, native, α-syn, thereby inhibiting pathological aggregation and preserving its normal functions.

The combinatorial space of an enzyme sequence has astronomical possibilities and exploring it with contemporary experimental techniques is arduous and often ineffective. Multi-target objectives such as concomitantly achieving improved selectivity, solubility and activity of an enzyme have narrow plausibility under approaches of restricted mutagenesis and combinatorial search. Traditional enzyme engineering approaches have a limited scope for complex optimization due to the requirement of a priori knowledge or experimental burden of screening huge protein libraries. The recent surge in high-throughput experimental methods including Next Generation Sequencing and automated screening has flooded the field of molecular biology with big-data, which requires us to re-think our concurrent approaches towards enzyme engineering. Artificial Intelligence (AI) and Machine Learning (ML) have great potential to revolutionize smart enzyme engineering without the explicit need for a complete understanding of the underlying molecular system. Here, we portray the role and position of AI techniques in the field of enzyme engineering along with their scope and limitations. In addition, we explain how the traditional approaches of directed evolution and rational design can be extended through AI tools. Recent successful examples of AI-assisted enzyme engineering projects and their deviation from traditional approaches are highlighted. A comprehensive picture of current challenges and future avenues for AI in enzyme engineering are also discussed.