Computational design of mRNA vaccines.

mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.

RNA folding using quantum computers.

The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots.

mRNA codon optimization with quantum computers.

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP-hard combinatorial optimization problem. Each amino acid in the protein sequence can be represented by as many as six codons, and the process of selecting the combination that maximizes probability of expression is termed codon optimization. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. A Quantum Annealer (QA) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The QA is found to be competitive in identifying optimal solutions. The utility of gate-based systems is also evaluated using a simulator resulting in the finding that while current generations of devices lack the hardware requirements, in terms of both qubit count and connectivity, to solve realistic problems, future generation devices may be highly efficient.

Elucidating the Role of Hydroxylated Phenylalanine in the Formation and Structure of Cross-Linked Aß Oligomers.

Amyloid ß-protein (Aß) oligomers play a seminal role in Alzheimer's disease (AD). Cross-linking (X-linking), which can be used to determine Aß oligomer size distributions experimentally, was reported to stabilize Aß oligomers. Aß oligomers X-linked in the presence of copper and hydrogen peroxide may represent the proximate neurotoxic species in AD. Our previous computational study demonstrated that X-linking of Aß40 and Aß42 oligomers via tyrosines alone cannot explain experimental findings. Here, we explore three plausible X-linking mechanisms, which involve, in addition to tyrosine, also lysine (mechanism 1), histidine (mechanism 2), and hydroxylated phenylalanine (mechanism 3). By examining the effect of X-linking on oligomer size distributions, we show that only mechanism 3 is consistent with experimental data. Our findings provide important insights into the two-step X-linking via mechanism 3, which consists of a simple covalent bonding via tyrosines in the presence of hydroxylated phenylalanines, followed by covalent bonding among tyrosines and hydroxylated phenylalanines. Structural analysis of X-linked Aß oligomers revealed increased solvent exposure at the N-terminal region, which was previously associated with increased oligomer toxicity. Our results elucidate a potentially important role of phenylalanine hydroxylation and increased toxicity of Aß oligomers induced by X-linking.

Insights into Formation and Structure of Aß Oligomers Cross-Linked via Tyrosines.

Alzheimer's disease (AD) pathology is hypothesized to be triggered by amyloid ß-protein (Aß) assembly into oligomers. Oligomer size distributions of both predominant Aß alloforms, Aß40 and Aß42, can be determined in vitro using cross-linking followed by gel electrophoresis. Cross-linking, which can occur in vivo in the presence of copper and hydrogen peroxide, was recently shown to stabilize Aß oligomers by inhibiting their conversion into fibrils. Whereas several studies showed that cross-linking is facilitated by dityrosine bond formation, the molecular-level mechanism of cross-linking remains unclear. Here, we use efficient discrete molecular dynamics with DMD4B-HYDRA force field to examine the effect of cross-linking via tyrosines on Aß oligomer formation. Our results show that cross-linking via tyrosines promotes Aß self-assembly, in particular that of Aß40, but does not account for cross-linked oligomers larger than Aß40 trimers and Aß42 tetramers. Cross-linking via tyrosines profoundly alters Aß40 and Aß42 oligomer conformations by increasing the solvent exposure of hydrophobic residues, resulting in elongated oligomeric morphologies that differ from globular structures of noncross-linked oligomers. When compared to available experimental data, our findings imply that amino acids other than tyrosines are involved in Aß cross-linking, a proposition that is currently under investigation.