Stamm, M. | Search bioRxiv

bioRxiv2026

mRNAfold: Co-optimization of Global Stability, Local Structure, and Codon Choice via Suboptimal Folding

mRNA medicines hold great promise, but designing sequences with high translation efficiency, robust in-solution stability, and manufacturability remains a major challenge due to the vast combinatorial space of synonymous coding sequences. Computational approaches such as mRNA folding algorithms have emerged as powerful tools by co-optimizing for in-solution stability and translation efficiency, yet current methods face important limitations. Here, we present "mRNAfold", an improved mRNA folding algorithm and software package that addresses these gaps by enabling efficient exploration of diverse near-optimal solutions, incorporating untranslated regions (UTRs), parallel execution, and supporting tunable control over local structural features across the mRNA. Thermodynamically optimized mRNAs from mRNAfold were more stable ({approx} 2-fold) in-solution than those generated by simple GC maximization for the same encoded protein. In addition, mRNAs designed to vary local structure near the start codon while maintaining consistent structure and codon optimality elsewhere showed a complex relationship between local structure near the start codon and protein production in cells. We observed no impact of structure in the start codon region for a set of mRNAs with high codon optimality, but it did impact protein production for a set of mRNAs with lower codon optimality. Together, these results underscore the potential of structure-aware, multi-objective design to improve mRNA medicines and offer a framework for exploring how sequence, structure, and expression are interrelated.

bioinformatics↗

bioRxiv2020

Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors

The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently, if those positions are not correctly aligned by a standard pairwise alignment procedure, the incorporation of such information into the alignment is typically addressed in an ad hoc manner, with manual adjustments. However, such modifications are problematic because they reduce the robustness and reproducibility of the alignment. An alternative approach is the use of restraints, or anchors, to incorporate such position-matching explicitly during alignment. Here we introduce position anchoring in the alignment tool AlignMe as an aid to pairwise sequence alignment of membrane proteins. Applying this approach to realistic scenarios involving distantly-related and low complexity sequences, we illustrate how the addition of even a single anchor can dramatically improve the accuracy of the alignments, while maintaining the reproducibility and rigor of the overall alignment.

bioinformatics↗