IndelsRNAmute: predicting deleterious multiple point substitutions and indels mutations

Background

RNA deleterious point mutation prediction was previously addressed with programs such as RNAmute and MultiRNAmute. The purpose of these programs is to predict a global conformational rearrangement of the secondary structure of a functional RNA molecule, thereby disrupting its function. RNAmute was designed to deal with only single point mutations in a brute force manner, while in MultiRNAmute an efficient approach to deal with multiple point mutations was developed. The approach used in MultiRNAmute is based on the stabilization of the suboptimal RNA folding prediction solutions and/or destabilization of the optimal folding prediction solution of the wild type RNA molecule. The MultiRNAmute algorithm is significantly more efficient than the brute force approach in RNAmute, but in the case of long sequences and large m-point mutation sets the MultiRNAmute becomes exponential in examining all possible stabilizing and destabilizing mutations.

Results

An inherent limitation in the RNAmute and MultiRNAmute programs is their ability to predict only substitution mutations, as these programs were not designed to work with deletion or insertion mutations. To address this limitation we herein develop a very fast algorithm, based on suboptimal folding solutions, to predict a predefined number of multiple point deleterious mutations as specified by the user. Depending on the user’s choice, each such set of mutations may contain combinations of deletions, insertions and substitution mutations. Additionally, we prove the hardness of predicting the most deleterious set of point mutations in structural RNAs.

Conclusions

We developed a method that extends our previous MultiRNAmute method to predict insertion and deletion mutations in addition to substitutions. The additional advantage of the new method is its efficiency to find a predefined number of deleterious mutations. Our new method may be exploited by biologists and virologists prior to site-directed mutagenesis experiments, which involve indel mutations along with substitutions. For example, our method may help to investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure.

Modeling the Interplay between HDV and HBV in Chronic HDV/HBV Patients

Hepatitis D virus is an infectious subviral agent that can only propagate in people infected with hepatitis B virus. In this study, we modified and further developed a recent model for early hepatitis D virus and hepatitis B virus kinetics to better reproduce hepatitis D virus and hepatitis B virus kinetics measured in infected patients during anti-hepatitis D virus treatment. The analytical solutions were provided to highlight the new features of the modified model. The improved model offered significantly better prospects for modeling hepatitis D virus and hepatitis B virus interactions.

Machine learning for mathematical models of HCV kinetics during antiviral therapy

Mathematical models for hepatitis C virus (HCV) dynamics have provided a means for evaluating the antiviral effectiveness of therapy and estimating treatment outcomes such as the time to cure. Recently, a mathematical modeling approach was used in the first proof-of-concept clinical trial assessing in real-time the utility of response-guided therapy with direct-acting antivirals (DAAs) in chronic HCV-infected patients. Several retrospective studies have shown that mathematical modeling of viral kinetics predicts time to cure of less than 12 weeks in the majority of individuals treated with sofosbuvir-based as well as other DAA regimens. A database of these studies was built, and machine learning methods were evaluated for their ability to estimate the time to cure for each patient to facilitate real-time modeling studies. Data from these studies exploring mathematical modeling of HCV kinetics under DAAs in 266 chronic HCV-infected patients were gathered. Different learning methods were applied and trained on part of the dataset ('train' set), to predict time to cure on the untrained part ('test' set). Our results show that this machine learning approach provides a means for establishing an accurate time to cure prediction that will support the implementation of individualized treatment.

A Mathematical Model for early HBV and -HDV Kinetics during Anti-HDV Treatment

Hepatitis delta virus (HDV) is an infectious subviral agent that can only propagate in people infected with hepatitis B virus (HBV). HDV/HBV infection is considered to be the most severe form of chronic viral hepatitis. In this contribution, a mathematical model for the interplay between HDV and HBV under anti-HDV treatment is presented. Previous models were not designed to account for the observation that HBV rises when HDV declines with HDV-specific therapy. In the simple model presented here, HDV and HBV kinetics are coupled, giving rise to an improved viral kinetic model that simulates the early interplay of HDV and HBV during anti-HDV therapy.

A Mathematical Analysis of HDV Genotypes: From Molecules to Cells

Hepatitis D virus (HDV) is classified according to eight genotypes. The various genotypes are included in the HDVdb database, where each HDV sequence is specified by its genotype. In this contribution, a mathematical analysis is performed on RNA sequences in HDVdb. The RNA folding predicted structures of the Genbank HDV genome sequences in HDVdb are classified according to their coarse-grain tree-graph representation. The analysis allows discarding in a simple and efficient way the vast majority of the sequences that exhibit a rod-like structure, which is important for the virus replication, to attempt to discover other biological functions by structure consideration. After the filtering, there remain only a small number of sequences that can be checked for their additional stem-loops besides the main one that is known to be responsible for virus replication. It is found that a few sequences contain an additional stem-loop that is responsible for RNA editing or other possible functions. These few sequences are grouped into two main classes, one that is well-known experimentally belonging to genotype 3 for patients from South America associated with RNA editing, and the other that is not known at present belonging to genotype 7 for patients from Cameroon. The possibility that another function besides virus replication reminiscent of the editing mechanism in HDV genotype 3 exists in HDV genotype 7 has not been explored before and is predicted by eigenvalue analysis. Finally, when comparing native and shuffled sequences, it is shown that HDV sequences belonging to all genotypes are accentuated in their mutational robustness and thermodynamic stability as compared to other viruses that were subjected to such an analysis.

Modeling-Based Response-Guided Glecaprevir-Pibrentasvir Therapy for Chronic Hepatitis C to Identify Patients for Ultrashort Treatment Duration

We recently showed in a proof-of-concept study that real-time modeling-based response-guided therapy can shorten hepatitis C virus treatment duration with sofosbuvir-velpatasvir, elbasvir-grazoprevir, and sofosbuvir-ledipasvir without compromising efficacy, confirming our retrospective modeling reports in >200 patients. However, retrospective modeling of pibrentasvir-glecaprevir (P/G) treatment has yet to be evaluated. In the current study, modeling hepatitis C virus kinetics in 44 cirrhotic and noncirrhotic patients predicts that P/G treatment might have been reduced to 4, 6, and 7 weeks in 16%, 34%, and 14% of patients, respectively. These results support the further evaluation of a modeling-based response-guided therapy approach using P/G.

HCVMultiscaleFit: A Simulator For Parameter Estimation in Multiscale Models Of Hepatitis C Virus Dynamics

Callibration in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation or fitting (callibration) that solves all cases of data points available presents a formidable challenge, as efficiency considerations need to be employed in order for the method to become practical. In the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derviative approximations. These steps that were successful in significantly speeding up a highly non-efficient approach, rendering it practical, can also be adapted to multiscale models of other viruses and other sophisticated differential equation models. The newly efficient methods that were developed as a result of the above approach are described. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for multiscale models. We provide a simulator called HCVMultiscaleFit with a Graphical User Interface that applies these methods and is useful to perform parameter estimation for simulating viral dynamics during antiviral treatment.

incaRNAfbinv 2.0: a webserver and software with motif control for fragment-based design of RNAs

SUMMARY

RNA design has conceptually evolved from the inverse RNA folding problem. In the classical inverse RNA problem, the user inputs an RNA secondary structure and receives an output RNA sequence that folds into it. Although modern RNA design methods are based on the same principle, a finer control over the resulting sequences is sought. As an important example, a substantial number of non-coding RNA families show high preservation in specific regions, while being more flexible in others and this information should be utilized in the design. By using the additional information, RNA design tools can help solve problems of practical interest in the growing fields of synthetic biology and nanotechnology. incaRNAfbinv 2.0 utilizes a fragment-based approach, enabling a control of specific RNA secondary structure motifs. The new version allows significantly more control over the general RNA shape, and also allows to express specific restrictions over each motif separately, in addition to other advanced features.

AVAILABILITY AND IMPLEMENTATION

incaRNAfbinv 2.0 is available through a standalone package and a web-server at https://www.cs.bgu.ac.il/incaRNAfbinv. Source code, command-line and GUI wrappers can be found at https://github.com/matandro/RNAsfbinv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Efficient Methods for Parameter Estimation of Ordinary and Partial Differential Equation Models of Viral Hepatitis Kinetics

Parameter estimation in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation that addresses all cases of data points available presents a formidable challenge and efficiency considerations need to be employed in order for the method to become practical. In the case of age-structured models of viral hepatitis dynamics under antiviral treatment that deal with partial differential equations, a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derivative approximations. The newly efficient methods that were developed as a result of the above approach are described for hepatitis C virus kinetic models during antiviral therapy. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for both the ordinary and partial differential equation models.

A Parameter Estimation Method for Multiscale Models of Hepatitis C Virus Dynamics

Mathematical models that are based on differential equations require detailed knowledge about the parameters that are included in the equations. Some of the parameters can be measured experimentally while others need to be estimated. When the models become more sophisticated, such as in the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), several strategies can be tried. It is possible to use parameter estimation on an analytical approximation of the solution to the multiscale model equations, namely the long-term approximation, but this limits the scope of the parameter estimation method used and a long-term approximation needs to be derived for each model. It is possible to transform the PDE multiscale model to a system of ODEs, but this has an effect on the model parameters themselves and the transformation can become problematic for some models. Finally, it is possible to use numerical solutions for the multiscale model and then use canned methods for the parameter estimation, but the latter is making the user dependent on a black box without having full control over the method. The strategy developed here is to start by working directly on the multiscale model equations for preparing them toward the parameter estimation method that is fully coded and controlled by the user. It can also be adapted to multiscale models of other viruses. The new method is described, and illustrations are provided using a user-friendly simulator that incorporates the method.

Design of RNAs: comparing programs for inverse RNA folding

Computational programs for predicting RNA sequences with desired folding properties have been extensively developed and expanded in the past several years. Given a secondary structure, these programs aim to predict sequences that fold into a target minimum free energy secondary structure, while considering various constraints. This procedure is called inverse RNA folding. Inverse RNA folding has been traditionally used to design optimized RNAs with favorable properties, an application that is expected to grow considerably in the future in light of advances in the expanding new fields of synthetic biology and RNA nanostructures. Moreover, it was recently demonstrated that inverse RNA folding can successfully be used as a valuable preprocessing step in computational detection of novel noncoding RNAs. This review describes the most popular freeware programs that have been developed for such purposes, starting from RNAinverse that was devised when formulating the inverse RNA folding problem. The most recently published ones that consider RNA secondary structure as input are antaRNA, RNAiFold and incaRNAfbinv, each having different features that could be beneficial to specific biological problems in practice. The various programs also use distinct approaches, ranging from ant colony optimization to constraint programming, in addition to adaptive walk, simulated annealing and Boltzmann sampling. This review compares between the various programs and provides a simple description of the various possibilities that would benefit practitioners in selecting the most suitable program. It is geared for specific tasks requiring RNA design based on input secondary structure, with an outlook toward the future of RNA design programs.

A Robust and Efficient Numerical Method for RNA-Mediated Viral Dynamics

The multiscale model of hepatitis C virus (HCV) dynamics, which includes intracellular viral RNA (vRNA) replication, has been formulated in recent years in order to provide a new conceptual framework for understanding the mechanism of action of a variety of agents for the treatment of HCV. We present a robust and efficient numerical method that belongs to the family of adaptive stepsize methods and is implicit, a Rosenbrock type method that is highly suited to solve this problem. We provide a Graphical User Interface that applies this method and is useful for simulating viral dynamics during treatment with anti-HCV agents that act against HCV on the molecular level.

Free energy minimization to predict RNA secondary structures and computational RNA design

Determining the RNA secondary structure from sequence data by computational predictions is a long-standing problem. Its solution has been approached in two distinctive ways. If a multiple sequence alignment of a collection of homologous sequences is available, the comparative method uses phylogeny to determine conserved base pairs that are more likely to form as a result of billions of years of evolution than by chance. In the case of single sequences, recursive algorithms that compute free energy structures by using empirically derived energy parameters have been developed. This latter approach of RNA folding prediction by energy minimization is widely used to predict RNA secondary structure from sequence. For a significant number of RNA molecules, the secondary structure of the RNA molecule is indicative of its function and its computational prediction by minimizing its free energy is important for its functional analysis. A general method for free energy minimization to predict RNA secondary structures is dynamic programming, although other optimization methods have been developed as well along with empirically derived energy parameters. In this chapter, we introduce and illustrate by examples the approach of free energy minimization to predict RNA secondary structures.

Acoustic interactions between inversion symmetric and asymmetric two-level systems

Amorphous solids, as well as many disordered lattices, display remarkable universality in their low temperature acoustic properties. This universality is attributed to the attenuation of phonons by tunneling two-level systems (TLSs), facilitated by the interaction of the TLSs with the phonon field. TLS-phonon interaction also mediates effective TLS-TLS interactions, which dictates the existence of a glassy phase and its low energy properties. Here we consider KBr:CN, the archetypal disordered lattice showing universality. We calculate numerically, using conjugate gradients method, the effective TLS-TLS interactions for inversion symmetric (CN flips) and asymmetric (CN rotations) TLSs, in the absence and presence of disorder, in two and three dimensions. The observed dependence of the magnitude and spatial power law of the interaction on TLS symmetry, and its change with disorder, characterizes TLS-TLS interactions in disordered lattices in both extreme and moderate dilutions. Our results are in good agreement with the two-TLS model, recently introduced to explain long-standing questions regarding the quantitative universality of phonon attenuation and the energy scale of ≈ 1-3 K below which universality is observed.

RNAthermsw: direct temperature simulations for predicting the location of RNA thermometers

The mechanism of RNA thermometers is a subject of growing interest. Also known as RNA thermosensors, these temperature-sensitive segments of the mRNA regulate gene expression by changing their secondary structure in response to temperature fluctuations. The detection of RNA thermometers in various genes of interest is valuable as it could lead to the discovery of new thermometers participating in fundamental processes such as preferential translation during heat-shock. RNAthermsw is a user-friendly webserver for predicting the location of RNA thermometers using direct temperature simulations. It operates by analyzing dotted figures generated as a result of a moving window that performs successive energy minimization folding predictions. Inputs include the RNA sequence, window size, and desired temperature change. RNAthermsw can be freely accessed at http://www.cs.bgu.ac.il/~rnathemsw/RNAthemsw/ (with the slash sign at the end). The website contains a help page with explanations regarding the exact usage.

RNA dot plots: an image representation for RNA secondary structure analysis and manipulations

Dot plots were originally introduced in bioinformatics as dot-containing images used to compare biological sequences and identify regions of close similarity between them. In addition to similarity, dot plots were extended to possibly represent interactions between building blocks of biological sequences, where the dots can vary in size or color according to desired features. In this survey, we first review their use in representing an RNA secondary structure, which has mostly been applied for displaying the output secondary structures as a result of running RNA folding prediction algorithms. Such a result may often contain suboptimal solutions in addition to the optimal one, which can be easily incorporated in the dot plot. We then proceed from their passive use of providing RNA secondary structure snapshots to their active use of illustrating RNA secondary structure manipulations in beneficial ways. While comparison between RNA secondary structures can mostly be done efficiently using a string representation, there are notable advantages in using dot plots for analyzing the suboptimal solutions that convey important information about the structure of the RNA molecule. In addition, structure-based alignment of dot plots has been advanced considerably and the filtering of dot plots that considers chemical and enzymatic data from structure determination experiments has been suggested. We discuss these procedures and how they can be enhanced in the future by using an image representation to analyze RNA secondary structures and examine their manipulations.

On topological indices for small RNA graphs

The secondary structure of RNAs can be represented by graphs at various resolutions. While it was shown that RNA secondary structures can be represented by coarse grain tree-graphs and meaningful topological indices can be used to distinguish between various structures, small RNAs are needed to be represented by full graphs. No meaningful topological index has yet been suggested for the analysis of such type of RNA graphs. Recalling that the second eigenvalue of the Laplacian matrix can be used to track topological changes in the case of coarse grain tree-graphs, it is plausible to assume that a topological index such as the Wiener index that represents all Laplacian eigenvalues may provide a similar guide for full graphs. However, by its original definition, the Wiener index was defined for acyclic graphs. Nevertheless, similarly to cyclic chemical graphs, small RNA graphs can be analyzed using elementary cuts, which enables the calculation of topological indices for small RNAs in an intuitive way. We show how to calculate a structural descriptor that is suitable for cyclic graphs, the Szeged index, for small RNA graphs by elementary cuts. We discuss potential uses of such a procedure that considers all eigenvalues of the associated Laplacian matrices to quantify the topology of small RNA graphs.

RNAexinv: An extended inverse RNA folding from shape and physical attributes to sequences

Background

RNAexinv is an interactive java application that performs RNA sequence design, constrained to yield a specific RNA shape and physical attributes. It is an extended inverse RNA folding program with the rationale behind that the generated sequences should not only fold into a desired structure, but they should also exhibit favorable attributes such as thermodynamic stability and mutational robustness. RNAexinv considers not only the secondary structure in order to design sequences, but also the mutational robustness and the minimum free energy. The sequences that are generated may not fully conform with the given RNA secondary structure, but they will strictly conform with the RNA shape of the given secondary structure and thereby take into consideration the recommended values of thermodynamic stability and mutational robustness that are provided.

Results

The output consists of designed sequences that are generated by the proposed method. Selecting a sequence displays the secondary structure drawings of the target and the predicted fold of the sequence, including some basic information about the desired and achieved thermodynamic stability and mutational robustness. RNAexinv can be used successfully without prior experience, simply specifying an initial RNA secondary structure in dot-bracket notation and numerical values for the desired neutrality and minimum free energy. The package runs under LINUX operating system. Secondary structure predictions are performed using the Vienna RNA package.

Conclusions

RNAexinv is a user friendly tool that can be used for RNA sequence design. It is especially useful in cases where a functional stem-loop structure of a natural sequence should be strictly kept in the designed sequences but a distant motif in the rest of the structure may contain one more or less nucleotide at the expense of another, as long as the global shape is preserved. This allows the insertion of physical observables as constraints. RNAexinv is available at http://www.cs.bgu.ac.il/~RNAexinv.

The RNAmute web server for the mutational analysis of RNA secondary structures

RNA mutational analysis at the secondary-structure level can be useful to a wide-range of biological applications. It can be used to predict an optimal site for performing a nucleotide mutation at the single molecular level, as well as to analyze basic phenomena at the systems level. For the former, as more sequence modification experiments are performed that include site-directed mutagenesis to find and explore functional motifs in RNAs, a pre-processing step that helps guide in planning the experiment becomes vital. For the latter, mutations are generally accepted as a central mechanism by which evolution occurs, and mutational analysis relating to structure should gain a better understanding of system functionality and evolution. In the past several years, the program RNAmute that is structure based and relies on RNA secondary-structure prediction has been developed for assisting in RNA mutational analysis. It has been extended from single-point mutations to treat multiple-point mutations efficiently by initially calculating all suboptimal solutions, after which only the mutations that stabilize the suboptimal solutions and destabilize the optimal one are considered as candidates for being deleterious. The RNAmute web server for mutational analysis is available at http://www.cs.bgu.ac.il/~xrnamute/XRNAmute.

Bioinformatic analysis of the neutrality of RNA secondary structure elements across genotypes reveals evidence for direct evolution of genetic robustness in HCV

The properties and origin of genetic robustness have recently been investigated in several works that examined microRNA stem-loop structures, and a variety of conclusions have been reached without agreement. Considering that this is a universal phenomenon that is not restricted to miRNAs, we recall the original work on this topic that began from looking at viral RNAs of several types. We provide a link to this work by examining the neutrality of HCV structural elements, performing a detailed bioinformatic analysis using RNA secondary structure predictions across genotypes. This study provides supporting evidence for direct evolution of genetic robustness that is not limited to noncoding RNAs participating in gene regulation, but includes functionally important structural elements of the hepatitis C virus (HCV) that show excess of robustness beyond the intrinsic robustness of their stem-loop structure. These findings further support the adaptive behavior of genetic robustness in functional RNAs of various types that seem to have evolved with selection pressure towards increased robustness.

An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions

BACKGROUND

RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm) for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure.

RESULTS

Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3), for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations.

CONCLUSION

A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure. A complete explanation of the application, called MultiRNAmute, is available at [1].

RNAmute: RNA secondary structure mutation analysis tool

Background

RNAMute is an interactive Java application that calculates the secondary structure of all single point mutations, given an RNA sequence, and organizes them into categories according to their similarity with respect to the wild type predicted structure. The secondary structure predictions are performed using the Vienna RNA package. Several alternatives are used for the categorization of single point mutations: Vienna's RNAdistance based on dot-bracket representation, as well as tree edit distance and second eigenvalue of the Laplacian matrix based on Shapiro's coarse grain tree graph representation.

Results

Selecting a category in each one of the processed tables lists all single point mutations belonging to that category. Selecting a mutation displays a graphical drawing of the single point mutation and the wild type, and includes basic information such as associated energies, representations and distances. RNAMute can be used successfully with very little previous experience and without choosing any parameter value alongside the initial RNA sequence. The package runs under LINUX operating system.

Conclusion

RNAMute is a user friendly tool that can be used to predict single point mutations leading to conformational rearrangements in the secondary structure of RNAs. In several cases of substantial interest, notably in virology, a point mutation may lead to a loss of important functionality such as the RNA virus replication and translation initiation because of a conformational rearrangement in the secondary structure.