An in-silico glimpse into the pH dependent structural changes of T7 RNA polymerase: a protein with simplicity

Comprehensive evaluation of T7 promoter for enhanced yield and quality in mRNA production

The manufacturing of mRNA vaccines relies on cell-free based systems that are easily scalable and flexible compared with the traditional vaccine manufacturing processes. Typically, standard processes yield 2 to 5 g L-1 of mRNA, with recent process optimisations increasing yields to 12 g L-1. However, increasing yields can lead to an increase in the production of unwanted by-products, namely dsRNA. It is therefore imperative to reduce dsRNA to residual levels in order to avoid intensive purification steps, enabling cost-effective manufacturing processes. In this work, we exploit sequence modifications downstream of the T7 RNA polymerase promoter to increase mRNA yields whilst simultaneously minimising dsRNA. In particular, transcription performance was optimised by modifying the sequence downstream of the T7 promoter with additional AT-rich sequences. We have identified variants that were able to produce higher amounts of mRNA (up to 14 g L-1) in 45 min of reaction. These variants exhibited up to a 30% reduction in dsRNA byproduct levels compared to a wildtype T7 promoter, and have similar EGFP protein expression. The results show that optimising the non-coding regions can have an impact on mRNA production yields and quality, reducing overall manufacturing costs.

Regulating pri/pre-microRNA up/down expressed in cancer proliferation, angiogenesis and metastasis using selected potent triterpenoids

MicroRNAs (miRNAs) play a crucial role in cancer progression by selectively inducing translational degradation of messenger RNA (mRNA) via sequence-specific interactions with the 3'-untranslated region (3'-UTR). The potential targeting of miRNA has been recognized as a significant avenue for investigating the biological progression of diverse cancer types. Consequently, targeting of pri-miRNA and pre-miRNA by phytochemicals emerges as a viable strategy in the realm of anticancer therapies. Among phytochemicals, triterpenoids have garnered significant recognition for their chemotherapeutic and chemopreventive capabilities in combating multiple cancers. To date, there is a dearth of literature about the molecular interactions between triterpenoids and miRNAs. The primary objective of this investigation is to discern the potential triterpenoids that can function as modulators for specific miRNAs, namely pri-miRNA-19b-2, pre-miR21, microRNA 20b, pri-miRNA-208a, pri-miRNA-378a, pri-miRNA-320b-2, and pri-miRNA-300, achieved through the use of in silico investigations. The study primarily focused on performing drug-likeness, computer-aided toxicity, and pharmacokinetic prediction studies for triterpenoids. Furthermore, molecular docking and simulation techniques were employed to investigate these compounds. The triterpenoids studied were shown to have drug-likeness characteristics, although asiatic acid, lupeol, and pristimerin were able to pass all toxicity tests. Among the triterpenoids that underwent docking, pristimerin had a significant binding energy of -10.9 kcal/mol during its interaction with pri-miR-378a. The stable interaction between the pristimerin and miRNA complex was demonstrated by molecular dynamics simulation. As a result, pristimerin has the potential to act as a modulator of carcinogenic miRNAs, making it a promising candidate for cancer prevention and treatment due to its tailored modulation of miRNA activity.

Integration of DNA-RNA-triplex-based regulation of transcription into molecular logic gates

In recent years, increasing numbers of noncoding RNA molecules were identified as possible components of endogenous DNA-RNA hybrid triplexes involved in gene regulation. Triplexes are potentially involved in complex molecular signaling networks that, if understood, would allow the engineering of biological computing components. Here, by making use of the enhancing and inhibiting effects of such triplexes, we demonstrate in vitro the construction of triplex-based molecular gates: 'exclusive OR' (XOR), 'exclusive NOT-OR' (XNOR), and a threshold gate, via transcription of a fluorogenic RNA aptamer. Precise modulation was displayed by the biomolecular-integrated systems over a wide interval of transcriptional outputs, ranging from drastic inhibition to significant enhancement. The present contribution represents a first example of molecular gates developed using DNA-RNA triplex nanostructures.

pH modulates the role of SP6 RNA polymerase in transcription process: an in silico study

SP6 RNA polymerase (SP6 RNAP) is an essential enzyme for the transcription process in SP6 bacteriophage. SP6 RNAP plays a vital role in mRNA vaccine designing technology and other translational biotechnology research due to the high specificity towards its promoter. The self-replicating performance also put this polymerase to study extensively. Despite of the reports emphasizing the function of this enzyme, a detailed structural and functional understanding of RNA polymerase is not reported so far. Here, we report the first-ever information about SP6RNAP structure and its effect on promoter binding at different pH environments using molecular docking and molecular dynamics simulation (MDS) study. We also report the changes in polymerase conformations in different pH conditions using in-silico approach. The docking study was also performed for SP6 RNAP with SP6 promoter at different pH environments using the in-silico docking tools and conducted the MDS study for complexes. MM/PBSA and per residue energy contribution has been performed at three different pH environments. The structural aspects confirmed that the pH 7.9 state favors the polymerase functional activity in the transcription process which was in the range reported using transcription assay. This polymerase's unique features may play its emerging role as an efficient transcription factor in translational biological research.Communicated by Ramaswamy H. Sarma.

Maximizing mRNA vaccine production with bayesian optimization

Messenger RNA (mRNA) vaccines are a new alternative to conventional vaccines with a prominent role in infectious disease control. These vaccines are produced in in vitro transcription (IVT) reactions, catalyzed by RNA polymerase in cascade reactions. To ensure an efficient and cost‐effective manufacturing process, essential for a large‐scale production and effective vaccine supply chain, the IVT reaction needs to be optimized. IVT is a complex reaction that contains a large number of variables that can affect its outcome. Traditional optimization methods rely on classic Design of Experiments methods, which are time‐consuming and can present human bias or based on simplified assumptions. In this contribution, we propose the use of Machine Learning approaches to perform a data‐driven optimization of an mRNA IVT reaction. A Bayesian optimization method and model interpretability techniques were used to automate experiment design, providing a feedback loop. IVT reaction conditions were found under 60 optimization runs that produced 12 g · L⁻¹ in solely 2 h. The results obtained outperform published industry standards and data reported in literature in terms of both achievable reaction yield and reduction of production time. Furthermore, this shows the potential of Bayesian optimization as a cost‐effective optimization tool within (bio)chemical applications.

Concerted motion of structure and active site charge is required for tyrosine aminotransferase activity in Leishmania parasite

Leishmania donovani tyrosine aminotransferase (LdTAT) is an essential enzyme that catalyzes the first step of amino acid catabolism. To understand LdTAT activity at different pH, molecular dynamics simulations were performed and trajectory and T-pad analysis pad were conducted. Fluorescence spectroscopy of LdTAT at various pH was measured to understand structural stability. UV studies on PLP were performed to determine the binding of the enzyme to cofactor PLP at different pH. The MD simulations showed that the structure of LdTAT was stable and no structural denaturation was observed at pH 2, 7 and 12. LdTAT exhibited the highest activity at pH -8 and fluorescent spectroscopy also corroborated by exhibiting the highest intensity at pH -8. Moreover, no structural denaturation was observed during the pH gradient. UV studies concluded that the aldimine bond forms only around neutral pH and redshift was observed on enzyme binding. From our observation, we hypothesize that the activity of LdTAT is a close interplay between the structure and charges of K286 and PLP. This study may provide significant insight into understanding parasitic enzymes like LdTAT during the life-cycle of Leishmania parasite. Knowledge of such enzyme mechanisms can pave the way for the design and delivery of enzyme-specific inhibitors.

Critical test of isotropic periodic sum techniques with group-based cut-off schemes

Truncation is still chosen for many long-range intermolecular interaction calculations to efficiently compute free-boundary systems, macromolecular systems and net-charge molecular systems, for example. Advanced truncation methods have been developed for long-range intermolecular interactions. Every truncation method can be implemented as one of two basic cut-off schemes, namely either an atom-based or a group-based cut-off scheme. The former computes interactions of “atoms” inside the cut-off radius, whereas the latter computes interactions of “molecules” inside the cut-off radius. In this work, the effect of group-based cut-off is investigated for isotropic periodic sum (IPS) techniques, which are promising cut-off treatments to attain advanced accuracy for many types of molecular system. The effect of group-based cut-off is clearly different from that of atom-based cut-off, and severe artefacts are observed in some cases. However, no severe discrepancy from the Ewald sum is observed with the extended IPS techniques.