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Many RNAs function through RNA–RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA–RNA interaction is useful, however, existing tools are either too simplistic or too slow. To address this issue, we present LinearCoFold, which approximates the complete minimum free energy structure of two strands in linear time, and LinearCoPartition, which approximates the cofolding partition function and base pairing probabilities in linear time. LinearCoFold and LinearCoPartition are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8× faster than RNAcofold MFE mode, and LinearCoPartition is 642.3× faster than RNAcofold partition function mode. Surprisingly, LinearCoFold and LinearCoPartition’s predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA–RNA interaction between SARS-CoV-2 genomic RNA (gRNA) and human U4 small nuclear RNA (snRNA), which has been experimentally studied, and observe that LinearCoFold’s prediction correlates better with the wet lab results than RNAcofold’s.

 

RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases.

We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs.

Our source code and data used in this article is available at https://github.com/shanry/SAMFEO.

 

This study assessed the disinfection byproduct (DBP) risks of algal impacted surface waters and the effects of peracetic acid (PAA) pre-oxidation on DBP risks. Authentic samples from three eutrophic lakes were collected over a 13-week period during the algal bloom season. The formation of 11 DBPs (four trihalomethanes, four haloacetonitriles, two haloketones, and trichloronitromethane) in these samples was assessed under uniform formation conditions (UFC) approximating drinking water disinfection. Trihalomethanes formed in the greatest abundance (90–370 μg L −1 ), followed by haloacetonitriles (6.5–87 μg L −1 ), haloketones (0.4–11.4 μg L −1 ), and trichloronitromethane (0.3–9.7 μg L −1 ). Total chlorophyll, a common indicator of algal activity, was not found to correlate with DBP yields. On the other hand, the yields of trichloronitromethane and haloacetonitriles correlated with nitrite/nitrate concentrations and DON concentrations in the samples, respectively. PAA pre-oxidation reduced the formation of trihalomethanes in the subsequent UFC tests in 80% of the samples, but promoted the formation of haloacetonitriles and trichloronitromethane in 70% and 50% of the samples, respectively. Analyses of DOC, DON, SUVA, and fluorescence excitation–emission matrices suggest that PAA pre-oxidation can alter the DBP precursors of a sample through the release of high haloacetonitrile/trichloronitromethane-yielding organic matter from algal cells and the oxidative transformation of existing and newly released dissolved organic matter. The results of this study, obtained from authentic surface water samples, suggest that mixed organic matter dynamics is an important consideration for the DBP risks of algal-impacted waters. 

Peracetic acid (PAA) is being considered as a disinfectant in membrane-based wastewater reuse systems, but its compatibility with polyamide membranes has not been thoroughly investigated. In this work, we showed that PAA induced much less change in the performance and material characteristics of NF90 membranes than the traditional disinfectant free chlorine (NaOCl). The change in membrane water flux and the rejection of salt and neutral organic compounds after PAA exposure (1–180 g h L −1 ) is significantly less than that resulting from NaOCl exposure at levels as low as 1 g h L −1 . The presence of two wastewater constituents, chloride or Fe( ii ), did not significantly impact membrane performance upon exposure to PAA. Surface characterization showed that oxygen was incorporated into polyamide by PAA, some of which was attributed to the formation of carboxylic acid groups. Experiments using a model aromatic amide, benzanilide, indicated an unexpected role of PAA in protecting the membrane from radicals formed by Fe( ii ) and the H 2 O 2 present in commercial PAA formulations. Furthermore, product identification suggests that both amide bond breakage and ring oxidation are possible reaction mechanisms for PAA. Our findings support that PAA is a viable disinfectant candidate for wastewater reuse and warrants further evaluation.