Biology subjects

Yao, H.-T.

Publications and source records attributed to Yao, H.-T..

5 recordsLinked to original sources

bioRxiv2026

Rational design of mechanically active RNAs: de novo engineering of functional exoribonuclease-resistant RNAs

Mechanically active RNAs represent an emerging class of biomolecules whose function derives from resisting molecular forces. Among them, exoribonuclease-resistant RNAs (xr-RNAs) achieve this by folding into a ring-like topology that physically blocks 5 [->] 3 degra-dation. However, despite years of structural insight, the rational design of such mechanically functional RNA devices has remained elusive. Here, we describe a mechanics-aware RNA design approach that enables de novo engineering of functional xrRNAs. We first identify structural determinants of force resistance by perturbing pseudoknot architecture in a model xrRNA and quantifying resulting efficiencies in the stalling of exoribonuclease XRN1. We then implement these rules in a design framework that integrates explicit topological constraints with molecular dynamics-guided optimization. The resulting synthetic xrRNAs reproduce the ring-like architecture and stall exoribonuclease XRN1 with wild-type-like efficiency. Our top-performing constructs exhibit minimal sequence similarity to known xrRNAs and evade detection by covariance models, yet remain fully functional in vitro. Together, our results show that mechanical function can be rationally designed independent of evolutionary ancestry, laying the groundwork for the design of RNA elements that modulate decay and fine-tune the mechanical stability of engineered transcripts.

biochemistry↗

bioRxiv2023

Salt corrections for RNA secondary structures in the ViennaRNA package

BackgroundRNA features a highly negatively charged phosphate backbone that attracts a of cloud counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled by currently available secondary stucture prediction algorithms. Combining Debye-Huckel potentials for line charges and Mannings counter-ion condensation theory, Einert et al. [Biophys. J. 100: 2745-2753 (2011)] modeled the energetic effects contributions monovalent cations on loops and helices. ResultsThe model of Einert et al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the dependence salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in the ViennaRNA package in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments. ConclusionThe new feature in the ViennaRNA package makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner. AvailabilityCorrections for the concentration of monovalent cations are available in the ViennaRNA package starting from version 2.6.0.

bioinformatics↗

bioRxiv2019

Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Modules Identification

RNA structures possess multiple levels of structural organization. Secondary structures are made of canonical (i.e. Watson-Crick and Wobble) helices, connected by loops whose local conformations are critical determinants of global 3D architectures. Such local 3D structures consist of conserved sets of non-canonical base pairs, called RNA modules. Their prediction from sequence data is thus a milestone toward 3D structure modelling. Unfortunately, the computational efficiency and scope of the current 3D module identification methods are too limited yet to benefit from all the knowledge accumulated in modules databases. Here, we introduce BayesPairing 2, a new sequence search algorithm leveraging secondary structure tree decomposition which allows to reduce the computational complexity and improve predictions on new sequences. We benchmarked our methods on 75 modules and 6380 RNA sequences, and report accuracies that are comparable to the state of the art, with considerable running time improvements. When identifying 200 modules on a single sequence, BayesPairing 2 is over 100 times faster than its previous version, opening new doors for genome-wide applications.

bioinformatics↗

bioRxiv2019

Exponentially few RNA structures are designable

The problem of RNA design attempts to construct RNA sequences that perform a predefined biological function, identified by several additional constraints. One of the foremost objective of RNA design is that the designed RNA sequence should adopt a predefined target secondary structure preferentially to any alternative structure, according to a given metrics and folding model. It was observed in several works that some secondary structures are undesignable, i.e. no RNA sequence can fold into the target structure while satisfying some criterion measuring how preferential this folding is compared to alternative conformations.\n\nIn this paper, we show that the proportion of designable secondary structures decreases exponentially with the size of the target secondary structure, for various popular combinations of energy models and design objectives. This exponential decay is, at least in part, due to the existence of undesignable motifs, which can be generically constructed, and jointly analyzed to yield asymptotic upper-bounds on the number of designable structures.

bioinformatics↗

bioRxiv2017

MentaLiST - A fast MLST caller for large wgMLST schemes

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing (WGS) data, MLST methods have evolved toward larger typing schemes, based on a few hundred genes (core genome MLST, cgMLST) to a few thousand genes (whole genome MLST, wgMLST). Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes.\n\nWe introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST scheme with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST.

bioinformatics↗