Engineering supramolecular dynamics of self-assembly and turnover of oncogenic microRNAs to drive their synergistic destruction in tumor models

Rationally-engineered functional biomaterials offer the opportunity to interface with complex biology in a predictive, precise, yet dynamic way to reprogram their behaviour and correct shortcomings. Success here may lead to a desired therapeutic effect against life-threatening diseases, such as cancer. Here, we engineered "Crab"-like artificial ribonucleases through coupling of peptide and nucleic acid building blocks, capable of operating alongside and synergistically with intracellular enzymes (RNase H and AGO2) for potent destruction of oncogenic microRNAs. "Crab"-like configuration of two catalytic peptides ("pincers") flanking the recognition oligonucleotide was instrumental here in providing increased catalytic turnover, leading to ≈30-fold decrease in miRNA half-life as compared with that for "single-pincer" conjugates. Dynamic modeling of miRNA cleavage illustrated how such design enabled "Crabs" to drive catalytic turnover through simultaneous attacks at different locations of the RNA-DNA heteroduplex, presumably by producing smaller cleavage products and by providing toeholds for competitive displacement by intact miRNA strands. miRNA cleavage at the 5'-site, spreading further into double-stranded region, likely provided a synergy for RNase H1 through demolition of its loading region, thus facilitating enzyme turnover. Such synergy was critical for sustaining persistent disposal of continually-emerging oncogenic miRNAs. A single exposure to the best structural variant (Crab-p-21) prior to transplantation into mice suppressed their malignant properties and reduced primary tumor volume (by 85 %) in MCF-7 murine xenograft models.

Reducing the vicissitudes of heterologous prochiral substrate catalysis by alcohol dehydrogenases through machine learning algorithms

Alcohol dehydrogenases (ADHs) are popular catalysts for synthesizing chiral synthons a vital step for active pharmaceutical intermediate (API) production. They are grouped into three superfamilies namely, medium-chain (MDRs), short-chain dehydrogenase/reductases (SDRs), and iron-containing alcohol dehydrogenases. The former two are used extensively for producing various chiral synthons. Many studies screen multiple enzymes or engineer a specific enzyme for catalyzing a substrate of interest. These processes are resource-intensive and intricate. The current study attempts to decipher the ability to match different ADHs with their ideal substrates using machine learning algorithms. We explore the catalysis of 284 antibacterial ketone intermediates, against MDRs and SDRs to demonstrate a unique pattern of activity. To facilitate machine learning we curated a dataset comprising 33 features, encompassing 4 descriptors for each compound. Subsequently, an ensemble of machine learning techniques viz. Partial Least Squares (PLS) regression, k-Nearest Neighbors (kNN) regression, and Support Vector Machine (SVM) regression, was harnessed. Moreover, the assimilation of Principal Component Analysis (PCA) augmented precision and accuracy, thereby refining and demarcating diverse compound classes. As such, this classification is useful for discerning substrates amenable to diverse alcohol dehydrogenases, thereby mitigating the reliance on high-throughput screening or engineering in identifying the optimal enzyme for specific substrate.

Substrate-dependent inactivation of recombinant paraoxonase 1 during catalytic dihydrocoumarin turnover and the protective properties of surfactants

Human paraoxonase-1 (PON1) is the most studied member of the paraoxonases (PONs) family and catalyzes the hydrolysis of various substrates (lactones, aryl esters, and paraoxon). Numerous studies link PON1 to oxidative stress-related diseases such as cardiovascular disease, diabetes, HIV infection, autism, Parkinson's, and Alzheimer's, where the kinetic behavior of an enzyme is characterized by initial rates or by modern methods that obtain enzyme kinetic parameters by fitting the computed curves over the entire time-courses of product formation (progress curves). In the analysis of progress curves, the behavior of PON1 during hydrolytically catalyzed turnover cycles is unknown. Hence, progress curves for enzyme-catalyzed hydrolysis of the lactone substrate dihydrocoumarin (DHC) by recombinant PON1 (rePON1) were analyzed to investigate the effect of catalytic DHC turnover on the stability of rePON1. Although rePON1 was significantly inactivated during the catalytic DHC turnover, its activity was not lost due to the product inhibition or spontaneous inactivation of rePON1 in the sample buffers. Examination of the progress curves of DHC hydrolysis by rePON1 led to the conclusion that rePON1 inactivates itself during catalytic DHC turnover hydrolysis. Moreover, human serum albumin or surfactants protected rePON1 from inactivation during this catalytic process, which is significant because the activity of PON1 in clinical samples is measured in the presence of albumin.

Kinetic Methods for Studying DNA Glycosylases Functioning in Base Excision Repair

Base excision repair (BER) is a conserved and ubiquitous pathway that is initiated by DNA glycosylases, which recognize and remove damaged or mismatched nucleobases, setting the stage for restoration of the correct DNA sequence by follow-on BER enzymes. DNA glycosylases employ a nucleotide-flipping step prior to cleavage of the N-glycosyl bond, and most exhibit slow release of the abasic DNA product and/or strong product inhibition. As such, studying the catalytic mechanism of these enzymes requires care in the design, execution, and interpretation of single- and multiple-turnover kinetics experiments, which is the topic of this chapter.

Activity Assays for Rhomboid Proteases

Rhomboids are ubiquitous intramembrane serine proteases that are involved in various signaling pathways. This fascinating class of proteases harbors an active site buried within the lipid milieu. High-resolution structures of the Escherichia coli rhomboid GlpG with various inhibitors revealed the catalytic mechanism for rhomboid-mediated proteolysis; however, a quantitative characterization was lacking. Assessing an enzyme's catalytic parameters is important for understanding the details of its proteolytic reaction and regulatory mechanisms. To assay rhomboid protease activity, many challenges exist such as the lipid environment and lack of known substrates. Here, we summarize various enzymatic assays developed over the last decade to study rhomboid protease activity. We present detailed protocols for gel-shift and FRET-based assays, and calculation of K_M and V_max to measure catalytic parameters, using detergent solubilized rhomboids with TatA, the only known substrate for bacterial rhomboids, and the model substrate fluorescently labeled casein.