MoiRNAiFold: a novel tool for complex in silico RNA design

Partial RNA design

MOTIVATION

RNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space.

RESULTS

In this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations. Partial RNA design describes the problem of designing RNAs from arbitrary RNA sequences and structure motifs with multiple design goals. By separating the design space from the objectives, our formulation enables the design of RNAs with variable lengths and desired properties, while still allowing precise control over sequence and structure constraints at individual positions. Based on this formulation, we introduce a new algorithm, libLEARNA, capable of efficiently solving different constraint RNA design tasks. A comprehensive analysis of various problems, including a realistic riboswitch design task, reveals the outstanding performance of libLEARNA and its robustness.

AVAILABILITY AND IMPLEMENTATION

libLEARNA is open-source and publicly available at: https://github.com/automl/learna_tools.

RNABPDB: Molecular Modeling of RNA Structure-From Base Pair Analysis in Crystals to Structure Prediction

The stable three-dimensional structure of RNA is known to play several important biochemical roles, from post-transcriptional gene regulation to enzymatic action. These structures contain double-helical regions, which often have different types of non-canonical base pairs in addition to Watson-Crick base pairs. Hence, it is important to study their structures from experimentally obtained or even predicted ones, to understand their role, or to develop a drug against the potential targets. Molecular Modeling of RNA double helices containing non-canonical base pairs is a difficult process, particularly due to the unavailability of structural features of non-Watson-Crick base pairs. Here we show a composite web-server with an associated database that allows one to generate the structure of RNA double helix containing non-canonical base pairs using consensus parameters obtained from the database. The database classification is followed by an evaluation of the central tendency of the structural parameters as well as a quantitative estimation of interaction strengths. These parameters are used to construct three-dimensional structures of double helices composed of Watson-Crick and/or non-canonical base pairs. Our benchmark study to regenerate double-helical fragments of many experimentally derived RNA structures indicate very high accuracy. This composite server is expected to be highly useful in understanding functions of various pre-miRNA by modeling structures of the molecules and estimating binding efficiency. The database can be accessed from http://hdrnas.saha.ac.in/rnabpdb .

aRNAque: an evolutionary algorithm for inverse pseudoknotted RNA folding inspired by Lévy flights

BACKGROUND

We study in this work the inverse folding problem for RNA, which is the discovery of sequences that fold into given target secondary structures.

RESULTS

We implement a Lévy mutation scheme in an updated version of aRNAque an evolutionary inverse folding algorithm and apply it to the design of RNAs with and without pseudoknots. We find that the Lévy mutation scheme increases the diversity of designed RNA sequences and reduces the average number of evaluations of the evolutionary algorithm. Compared to antaRNA, aRNAque CPU time is higher but more successful in finding designed sequences that fold correctly into the target structures.

CONCLUSION

We propose that a Lévy flight offers a better standard mutation scheme for optimizing RNA design. Our new version of aRNAque is available on GitHub as a python script and the benchmark results show improved performance on both Pseudobase++ and the Eterna100 datasets, compared to existing inverse folding tools.

End-to-end computational approach to the design of RNA biosensors for detecting miRNA biomarkers of cervical cancer

Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Relatively scant awareness and limited access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative unified pipeline for diagnosis has been developed, beginning with identification of optimal biomarkers, concurrent design of toehold switch sensors, and finally simulation of the designed diagnostic circuits to assess performance. Using miRNA expression data in the public domain, we identified miR-21–5p and miR-20a-5p as blood-based miRNA biomarkers specific to early-stage cervical cancer employing a multi-tier algorithmic screening. Synthetic riboregulators called toehold switches specific to the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we used a generic grammar of these switches, and built a neural network model of dynamic range using thermodynamic features derived from mRNA secondary structure and interaction. Second-generation toehold switches were used to overcome the design challenges associated with miRNA biomarkers. The resultant model yielded an adj. R² ∼0.71, outperforming earlier models of toehold-switch dynamic range. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10 nM and 100 nM before saturation. Our study demonstrates an end-to-end computational workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic/nucleic-acid signatures. The approach has the potential to replace iterative experimental trial and error, and focus time, money, and efforts. All software including the toehold grammar parser, neural network model and reaction kinetics simulation are available as open-source software (https://github.com/SASTRA-iGEM2019) under GNU GPLv3 licence.