Fitness functions for RNA structure design

Ward, Max; Courtney, Eliot; Rivas, Elena

doi:10.1093/nar/gkad097

Cited by 8 publications

(7 citation statements)

References 60 publications

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“…Eterna designs were originally evaluated computationally as to whether the sequence is predicted to fold with the lowest free energy to the target structure. This can be achieved whether or not the NED is relatively low. ,, Therefore, we expected that many of the designs would not provide low NED, despite performing well by minimum free energy folding. We compared the lowest NED for our solutions using P–Z to the lowest NED using Eterna player RNA sequences (Figure S4).…”

Section: Resultsmentioning

confidence: 99%

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DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters

Pham,

Miffin,

Sun

et al. 2023

ACS Synth. Biol.

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We show that in silico design of DNA secondary structures is improved by extending the base pairing alphabet beyond A−T and G−C to include the pair between 2-amino-8-(1′-β-D-2′deoxyribofuranosyl)-imidazo-[1,2-a]-1,3,5-triazin-(8H)-4-one and 6amino-3-(1′-β-D-2′-deoxyribofuranosyl)-5-nitro-(1H)-pyridin-2-one, abbreviated as P and Z. To obtain the thermodynamic parameters needed to include P−Z pairs in the designs, we performed 47 optical melting experiments and combined the results with previous work to fit free energy and enthalpy nearest neighbor folding parameters for P−Z pairs and G−Z wobble pairs. We find G−Z pairs have stability comparable to that of A−T pairs and should therefore be included as base pairs in structure prediction and design algorithms. Additionally, we extrapolated the set of loop, terminal mismatch, and dangling end parameters to include the P and Z nucleotides. These parameters were incorporated into the RNAstructure software package for secondary structure prediction and analysis. Using the RNAstructure Design program, we solved 99 of the 100 design problems posed by Eterna using the ACGT alphabet or supplementing it with P−Z pairs. Extending the alphabet reduced the propensity of sequences to fold into off-target structures, as evaluated by the normalized ensemble defect (NED). The NED values were improved relative to those from the Eterna example solutions in 91 of 99 cases in which Eterna-player solutions were provided. P−Z-containing designs had average NED values of 0.040, significantly below the 0.074 of standard-DNA-only designs, and inclusion of the P−Z pairs decreased the time needed to converge on a design. This work provides a sample pipeline for inclusion of any expanded alphabet nucleotides into prediction and design workflows.

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Section: Resultsmentioning

confidence: 99%

“…This can be achieved whether or not the NED is relatively low. 13 , 44 , 45 Therefore, we expected that many of the designs would not provide low NED, despite performing well by minimum free energy folding. We compared the lowest NED for our solutions using P–Z to the lowest NED using Eterna player RNA sequences ( Figure S4 ).…”

Section: Resultsmentioning

confidence: 99%

DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters

Pham,

Miffin,

Sun

et al. 2023

ACS Synth. Biol.

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“…We also require that its native (target) secondary structure is complete, i.e., that no part of the motif sequence forms base pairs outside of it. To analyze how the addition of different flanking sequences around a motif change its structure, we adopt several different measures that operate on its secondary structure representation [64, 65]: (i) structure probability 𝒫 (ℳ), (ii) ensemble defect ED(ℳ), and (iii) Shannon entropy SE(ℳ).…”

Section: Methodsmentioning

confidence: 99%

“…Structure probability. A very simple yet powerful measure of motif stability is simply the likelihood of its exact structure ocurring at equilibrium, P(M) [65]. The value of structure probability is obtained from the thermal ensemble of RNA structures, counting the number of occurrences of the exact (target) motif and dividing it with the number of all structures in the ensemble.…”

Section: Measures Of Motif Structural Stabilitymentioning

confidence: 99%

Context-dependent structure formation of RNA hairpin motifs

Bukina,

Božič

2024

Preprint

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Many functions of ribonucleic acid (RNA) rely on its ability to assume specific sequence-structure motifs. Packaging signals of some RNA viruses - hairpin motifs that interact with capsid proteins and drive self-assembly - are a prominent example. While interaction specificity demands the formation of stable motifs, they remain a small part of a much larger genomic RNA. An underexplored question is how the presence and composition of a flanking sequence around a motif interacts and interferes with its structure. Combining secondary and tertiary structure prediction, we study structural stability of 14 hairpin motifs from the RNA genome of MS2 bacteriophage and show that while some motif structures can be stable in any context, others require specific context provided by the genome. Our results demonstrate how important it is to consider RNA structure in its context and that changes in the flanking sequence of an RNA motif sometimes lead to drastic changes in its structure.

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“…The partition function can be used to calculate the probability of a structure. The probability of a structure is known to be a good guide for inverse folding [21]. Gradients with respect to the sequence distribution can be computed, which lets us optimize the probability by gradient descent.…”

Section: Introductionmentioning

confidence: 99%

Differentiable Partition Function Calculation for RNA

Matthies

Krueger

Torda

et al. 2023

Preprint

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Ribonucleic acid (RNA) is an essential molecule in a wide range of biological functions. In 1990, McCaskill introduced a dynamic programming algorithm for computing the partition function of an RNA sequence. This forward model is widely used for understanding the thermodynamic properties of a given RNA. In this work, we introduce a generalization of McCaskill's algorithm that is well-defined over continuous inputs and is differentiable. This allows us to tackle the inverse folding problem---designing a sequence with desired equilibrium thermodynamic properties---directly using gradient optimization. This has applications to creating RNA-based drugs such as mRNA vaccines. Furthermore, it allows McCaskill's foundational algorithm to be incorporated into machine learning pipelines directly since we have made it end-to-end differentiable. This work highlights how principles from differentiable programming can be translated to existing physical models to develop powerful tools for machine learning. We provide a concrete example by implementing an effective and interpretable RNA design algorithm.

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Fitness functions for RNA structure design

Cited by 8 publications

References 60 publications

DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters

DNA Structure Design Is Improved Using an Artificially Expanded Alphabet of Base Pairs Including Loop and Mismatch Thermodynamic Parameters

Context-dependent structure formation of RNA hairpin motifs

Differentiable Partition Function Calculation for RNA

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