LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

Huang, Liang; Zhang, He; Deng, Dezhong; Zhao, Kai; Liu, Kaibo; Hendrix, David A.; Mathews, David H.

doi:10.1093/bioinformatics/btz375

Cited by 128 publications

(130 citation statements)

References 62 publications

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“…In addition, our optimization methods face challenges with long mRNA lengths, where the search space of potential mRNA sequences and the computational costs of predicting structural properties are larger. For the full-length Spike mRNA and longer molecules like self-amplifying mRNAs, a ‘divide-and-conquer’ approach may achieve lower AUP solutions, as would linearization strategies developed in the LinearFold suite of methods ( 29 , 44 ). Both approaches require further exploration.…”

Section: Discussionmentioning

confidence: 99%

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Theoretical basis for stabilizing messenger RNA through secondary structure design

Wayment-Steele

Kim

Choe

et al. 2020

Preprint

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RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery, and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Motivated by the need for stabilized COVID-19 mRNA vaccines, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall rate of hydrolysis. To characterize the stabilization achievable through structure design, we compare optimization of AUP by conventional mRNA design methods to results from the LinearDesign algorithm, a new Monte Carlo tree search algorithm called RiboTree, and crowdsourcing through the OpenVaccine challenge on the Eterna platform. Tests were carried out on mRNAs encoding nanoluciferase, green fluorescent protein, and COVID-19 mRNA vaccine candidates encoding SARS-CoV-2 epitopes, spike receptor binding domain, and full-length spike protein. We find that Eterna and RiboTree significantly lower AUP while maintaining a large diversity of sequence and structure features that correlate with translation, biophysical size, and immunogenicity. Our results suggest that increases in in vitro mRNA half-life by at least two-fold are immediately achievable and that further stability improvements may be enabled with thorough experimental characterization of RNA hydrolysis.

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Section: Discussionmentioning

confidence: 99%

“…Each puzzle and lab features a short description of what participants should optimize for. MFE energy and MFE structure are calculated using LinearFold ( 44 ) implemented in EternaJS, and AUP is calculated using the LinearPartition ( 50 ) algorithm implemented in EternaJS.…”

Section: Methodsmentioning

confidence: 99%

Theoretical basis for stabilizing messenger RNA through secondary structure design

Wayment-Steele

Kim

Choe

et al. 2020

Preprint

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“…( C) Upper triangle shows the estimated base-pairing probability matrix for this RNA using Vienna RNAfold, where darker red squares represent higher probability base pairs; the lower triangle shows the two different structures; ( D ) Comparison between classical, local, and left-to-right algorithms for MFE and partition function calculation.

( Zuker and Stiegler, 1981 ), °( McCaskill, 1990 ), *( Lange et al , 2012 ),

( Bernhart et al , 2006a ),

( Kiryu et al , 2008 ) and

( Huang et al , 2019 ). LinearFold and LinearPartition enjoy linear runtime because of a left-to-right order that enables heuristic beam pruning, and both become exact

algorithms without pruning.…”

Section: Introductionmentioning

confidence: 99%

“…To address this

-time bottleneck, we present LinearPartition, which is inspired by our recently proposed LinearFold algorithm ( Huang et al , 2019 ) that approximates the MFE structure in linear time. Using the same idea, LinearPartition can approximate the partition function and base-pairing probability matrix in linear time.…”

Section: Introductionmentioning

confidence: 99%

LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities

Zhang

Mathews

et al. 2020

Bioinformatics

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Motivation RNA secondary structure prediction is widely used to understand RNA function. Recently, there has been a shift away from the classical minimum free energy methods to partition function-based methods that account for folding ensembles and can therefore estimate structure and base pair probabilities. However, the classical partition function algorithm scales cubically with sequence length, and is therefore prohibitively slow for long sequences. This slowness is even more severe than cubic-time free energy minimization due to a substantially larger constant factor in runtime. Results Inspired by the success of our recent LinearFold algorithm that predicts the approximate minimum free energy structure in linear time, we design a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base-pairing probabilities, which is shown to be orders of magnitude faster than Vienna RNAfold and CONTRAfold (e.g. 2.5 days versus 1.3 min on a sequence with length 32 753 nt). More interestingly, the resulting base-pairing probabilities are even better correlated with the ground-truth structures. LinearPartition also leads to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNAs), as well as a substantial improvement on long-distance base pairs (500+ nt apart). Availability and implementation Code: http://github.com/LinearFold/LinearPartition; Server: http://linearfold.org/partition. Supplementary information Supplementary data are available at Bioinformatics online.

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“…Though it is faster than CONTRAfold training, it borrows the inference implementation from CONTRAfold, which runs in cubic time and results in a costly inference process, especially when the training set includes long sequences. In [10] for inference. We use structured SVM algorithm for training.…”

Section: Introductionmentioning

confidence: 99%

Learning to Fold RNAs in Linear Time

Zhang

Huang

2019

Preprint

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RNA secondary structure is helpful for understanding RNA's functionality, thus accurate prediction systems are desired. Both thermodynamics-based models and machine learning-based models have been used in different prediction systems to solve this problem. Compared to thermodynamicsbased models, machine learning-based models can address the inaccurate measurement of thermodynamic parameters due to experimental limitation. However, the existing methods for training machine learning-based models are still expensive because of their cubic-time inference cost. To overcome this, we present a linear-time machine learning-based folding system, using recently proposed approximate folding tool LinearFold as inference engine, and structured SVM (sSVM) as training algorithm. Furthermore, to remedy non-convergence of naive sSVM with inexact search inference, we introduce a max violation update strategy. The training speed of our system is 41× faster than CONTRAfold on a diverse dataset for one epoch, and 14× faster than MXfold on a dataset with longer sequences. With the learned parameters, our system improves the accuracy of LinearFold, and is also the most accurate system among selected folding tools, including CONTRAfold, Vienna RNAfold and MXfold.

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LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

Cited by 128 publications

References 62 publications

Theoretical basis for stabilizing messenger RNA through secondary structure design

Theoretical basis for stabilizing messenger RNA through secondary structure design

LinearPartition: linear-time approximation of RNA folding partition function and base-pairing probabilities

Learning to Fold RNAs in Linear Time

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