Secondary Structure Prediction Algorithms Research Articles

LinearCoFold and LinearCoPartition: linear-time algorithms for secondary structure prediction of interacting RNA molecules.

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 26,190nt, 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.

Mono-valent salt corrections for RNA secondary structures in the ViennaRNA package

BackgroundRNA features a highly negatively charged phosphate backbone that attracts a cloud of counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled consistently by currently available secondary structure prediction algorithms. Combining Debye-Hückel potentials for line charges and Manning’s counter-ion condensation theory, Einert et al. (Biophys J 100: 2745-2753, 2011) modeled the energetic contributions of monovalent cations on loops and helices.ResultsThe model of Einert et al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in the ViennaRNA package in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments.ConclusionThe new feature in the ViennaRNA package makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner.AvailabilityCorrections for the concentration of monovalent cations are available in the ViennaRNA package starting from version 2.6.0.

C-RCPred: a multi-objective algorithm for interactive secondary structure prediction of RNA complexes integrating user knowledge and SHAPE data.

RNAs can interact with other molecules in their environment, such as ions, proteins or other RNAs, to form complexes with important biological roles. The prediction of the structure of these complexes is therefore an important issue and a difficult task. We are interested in RNA complexes composed of several (more than two) interacting RNAs. We show how available knowledge on the considered RNAs can help predict their secondary structure. We propose an interactive tool for the prediction of RNA complexes, called C-RCPRed, that considers user knowledge and probing data (which can be generated experimentally or artificially). C-RCPred is based on a multi-objective optimization algorithm. Through an extensive benchmarking procedure, which includes state-of-the-art methods, we show the efficiency of the multi-objective approach and the positive impact of considering user knowledge and probing data on the prediction results. C-RCPred is freely available as an open-source program and web server on the EvryRNA website (https://evryrna.ibisc.univ-evry.fr).

RNA independent fragment partition method based on deep learning for RNA secondary structure prediction

The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance (especially for long RNA sequences) and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at https://github.com/mianfei71/RNAPar.

Formula omitted]-HMM and optimal decoding higher-order structures on sequential data

Decoding higher-order structure on sequential data is an indispensable task in data science. It requires models to have the capability to characterize interdependencies among hidden events that have generated observable data. However, to be able to decode arbitrary structures, such models would need to cope with the intractability arising from computing context-sensitive relations, likely compromising the quality of answers. To address this important issue, the current paper introduces the arbitrary order hidden Markov model (α-HMM), an extension of the HMM that permits decoding of the optimal higher-order structure with an assurance of computational tractability. The advantage of the α-HMMis made possible by an identified principle on how random variables influence each other in a stochastic process. In particular, it is shown that decoding the optimal structure with an α-HMMcan be computed in O(n3)-time for any stochastic process of n random variables. As an application, it is demonstrated the decoding algorithm inspires a simple yet effective algorithm for RNA secondary structure prediction.

Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User's Guide to the LandscapeFold Algorithm.

Here we detail the LandscapeFold secondary structure prediction algorithm and how it is used. The algorithm was previously described and tested in (Kimchi O et al., Biophys J 117(3):520-532, 2019), though it was not named there. The algorithm directly enumerates all possible secondary structures into which up to two RNA or single-stranded DNA sequences can fold. It uses a polymer physics model to estimate the configurational entropy of structures including complex pseudoknots. We detail each of these steps and ways in which the user can adjust the algorithm as desired. The code is available on the GitHub repository https://github.com/ofer-kimchi/LandscapeFold .

ExpertRNA: A New Framework for RNA Secondary Structure Prediction

Ribonucleic acid (RNA) is a fundamental biological molecule that is essential to all living organisms, performing a versatile array of cellular tasks. The function of many RNA molecules is strongly related to the structure it adopts. As a result, great effort is being dedicated to the design of efficient algorithms that solve the “folding problem”—given a sequence of nucleotides, return a probable list of base pairs, referred to as the secondary structure prediction. Early algorithms largely rely on finding the structure with minimum free energy. However, the predictions rely on effective simplified free energy models that may not correctly identify the correct structure as the one with the lowest free energy. In light of this, new, data-driven approaches that not only consider free energy, but also use machine learning techniques to learn motifs are also investigated and recently been shown to outperform free energy–based algorithms on several experimental data sets. In this work, we introduce the new ExpertRNA algorithm that provides a modular framework that can easily incorporate an arbitrary number of rewards (free energy or nonparametric/data driven) and secondary structure prediction algorithms. We argue that this capability of ExpertRNA has the potential to balance out different strengths and weaknesses of state-of-the-art folding tools. We test ExpertRNA on several RNA sequence-structure data sets, and we compare the performance of ExpertRNA against a state-of-the-art folding algorithm. We find that ExpertRNA produces, on average, more accurate predictions of nonpseudoknotted secondary structures than the structure prediction algorithm used, thus validating the promise of the approach. Summary of Contribution: ExpertRNA is a new algorithm inspired by a biological problem. It is applied to solve the problem of secondary structure prediction for RNA molecules given an input sequence. The computational contribution is given by the design of a multibranch, multiexpert rollout algorithm that enables the use of several state-of-the-art approaches as base heuristics and allowing several experts to evaluate partial candidate solutions generated, thus avoiding assuming the reward being optimized by an RNA molecule when folding. Our implementation allows for the effective use of parallel computational resources as well as to control the size of the rollout tree as the algorithm progresses. The problem of RNA secondary structure prediction is of primary importance within the biology field because the molecule structure is strongly related to its functionality. Whereas the contribution of the paper is in the algorithm, the importance of the application makes ExpertRNA a showcase of the relevance of computationally efficient algorithms in supporting scientific discovery.

Efficient Generation of RNA Secondary Structure Prediction Algorithm Under PAR Framework.

Prediction of RNA secondary structure is an important part of bioinformatics genomics research. Mastering RNA secondary structure can help us to better analyze protein synthesis, cell differentiation, metabolism, and genetic processes and thus reveal the genetic laws of organisms. Comparative sequence analysis, support vector machine, centroid method, and other algorithms in RNA secondary structure prediction algorithms often use dynamic programming algorithm to predict RNA secondary structure because of their huge time and space consumption and complex data structure. In this article, the domain of RNA secondary structure prediction algorithm based on dynamic programming (DP-SSP) is analyzed in depth, and the domain features are modeled. According to the generative programming method, the DP-SSP algorithm components are interactively designed. With the support of PAR platform, the DP-SSP algorithm component library is formally realized. Finally, the concrete algorithm is generated through component assembly, which improves the efficiency and reliability of algorithm development.

UFold: fast and accurate RNA secondary structure prediction with deep learning.

For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs). We benchmark the performance of UFold on both within- and cross-family RNA datasets. It significantly outperforms previous methods on within-family datasets, while achieving a similar performance as the traditional methods when trained and tested on distinct RNA families. UFold is also able to predict pseudoknots accurately. Its prediction is fast with an inference time of about 160 ms per sequence up to 1500 bp in length. An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold.

Discovering the Ultimate Limits of Protein Secondary Structure Prediction.

Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81–86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4–5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84–87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.

A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.

The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.

ATTfold: RNA Secondary Structure Prediction With Pseudoknots Based on Attention Mechanism.

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.

An efficient three-Level parallel ABC algorithm for secondary structure prediction of complex RNA sequences

This paper introduces 3L-PABCfold, a parallel artificial bee colony algorithm with three-Level strategy for efficient secondary structure prediction of complex RNA sequences, by multi-processor parallel computing. Designed 3L-PABCfold is a combination of additionally proposed three sub-algorithms: three-Level parallel ABC algorithm (3L-PABC), set based ABC algorithm and discrete ABC (DABC) algorithm. Proposed 3L-PABCfold is an extension of author’s earlier developed TL-PSOfold Lalwani et al. (2016), a two-Level particle swarm optimization algorithm for RNA secondary structure prediction. 3L-PABCfold is furthermore enriched with complexity evaluation of highly obscure sequence families followed by makespan minimization and algorithm implementation in parallel computing environment.As the complex dataset, 1,00,000 RNA sequences are randomly generated with length range 139–6358 nucleotides. Level-I of the algorithm reduces the machines idle time by implementing an efficient schedule, whereas, Level-II maximizes the bonded pairs of RNA sequences and Level-III provides optimum secondary structure of RNA sequence. Moreover, three more parallel versions are developed i.e. parallel version of TL-PSOfold (TL-PPSOfold), three Level parallel version of TL-PSOfold (3L-PPSOfold) and two-Level parallel version of 3L-PABCfold (TL-PABCfold), so as to avail a fair comparison and to identify the competent strategy for the addressed problem. The performance evaluated at the criteria of sensitivity, specificity, F-measure along with the descriptive statistics and statistical significance, yields significantly better prediction accuracy for 3L-PABCfold. Further, the efficiency at the processing time criteria proves 3L-PABCfold as the most competent algorithm than TL-PPSOfold, TL-PABCfold and 3L-PPSOfold.

Collaborative Learning of Estimation of Distribution Algorithm for RNA secondary structure prediction

Estimation of distribution algorithms (EDAs) are successfully applied in the fields of bioinformatics for tasks such as gene structure analysis, protein structure prediction, and RNA secondary structure prediction. This paper proposes a new method, namely collaborative learning of estimation of distribution algorithms, or Co-EDAs, based on an estimation of distribution algorithm for RNA secondary structure prediction using a single RNA sequence as input. The proposed method consists of two EDAs with minimum free energy objective. The Co-EDAs use both good and poor solutions to improve the algorithm’s to search throughout the search space. Using information from poor solutions can indicate which area is unappealing to explore when searching with high-dimensional data. The Co-EDAs method was tested with 750 known RNA structures from RNA STRAND v2.0. That database includes data with more than 14 RNA types. The proposed method was compared to three prediction programs that are based on dynamic programming algorithms called Mfold, RNAfold, and RNAstructure. These programs are available as services on web servers. The results on average show that the Co-EDAs yields approximately 6% better accuracy than those competitors in all metrics.

The Order-Disorder Continuum: Linking Predictions of Protein Structure and Disorder through Molecular Simulation

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions within proteins (IDRs) serve an increasingly expansive list of biological functions, including regulation of transcription and translation, protein phosphorylation, cellular signal transduction, as well as mechanical roles. The strong link between protein function and disorder motivates a deeper fundamental characterization of IDPs and IDRs for discovering new functions and relevant mechanisms. We review recent advances in experimental techniques that have improved identification of disordered regions in proteins. Yet, experimentally curated disorder information still does not currently scale to the level of experimentally determined structural information in folded protein databases, and disorder predictors rely on several different binary definitions of disorder. To link secondary structure prediction algorithms developed for folded proteins and protein disorder predictors, we conduct molecular dynamics simulations on representative proteins from the Protein Data Bank, comparing secondary structure and disorder predictions with simulation results. We find that structure predictor performance from neural networks can be leveraged for the identification of highly dynamic regions within molecules, linked to disorder. Low accuracy structure predictions suggest a lack of static structure for regions that disorder predictors fail to identify. While disorder databases continue to expand, secondary structure predictors and molecular simulations can improve disorder predictor performance, which aids discovery of novel functions of IDPs and IDRs. These observations provide a platform for the development of new, integrated structural databases and fusion of prediction tools toward protein disorder characterization in health and disease.

Propensities of Amino Acid Pairings in Secondary Structure of Globular Proteins.

A class of secondary structure prediction algorithms use the information from the statistics of the residue pairs found in secondary structural elements. Because the protein folding process is dominated by backbone hydrogen bonding, an approach based on backbone hydrogen-bonded residue pairings would improve the predicting capabilities of these class algorithms. The reliability of the prediction algorithms depends on the quality of the statistics, therefore, of the data set. In this study, it was aimed to determine the propensities of the backbone hydrogen-bonded residue pairings for secondary structural elements of α-helix and β-sheet in globular proteins using a new and comprehensive data set created from the peptides deposited in Worldwide Protein Data Bank. A master data set including 4882 globular peptide chains with resolution better than 2.5 Å, sequence identity smaller than 25% and length of no shorter than 100 residues were created. Separate data sub sets also were created for helix and sheet structures from master set and each sub set includes 4594 and 4483 chains, respectively. Backbone hydrogen-bonded residue pairings in helices and sheets were detected and the propensities of them were represented as odds ratios (observed/[random or expected]) in matrices. Propensities assigned by this study to the residue pairings in secondary structural elements (as helix, overall strands, parallel strands and antiparallel strands) differ from the previous studies by 19 to 34%. These dissimilarities are important and they would cause further improvements in secondary structure prediction algorithms.

An efficient simulated annealing algorithm for the RNA secondary structure prediction with Pseudoknots

BackgroundRNA pseudoknot structures play an important role in biological processes. However, existing RNA secondary structure prediction algorithms cannot predict the pseudoknot structure efficiently. Although random matching can improve the number of base pairs, these non-consecutive base pairs cannot make contributions to reduce the free energy.ResultIn order to improve the efficiency of searching procedure, our algorithm take consecutive base pairs as the basic components. Firstly, our algorithm calculates and archive all the consecutive base pairs in triplet data structure, if the number of consecutive base pairs is greater than given minimum stem length. Secondly, the annealing schedule is adapted to select the optimal solution that has minimum free energy. Finally, the proposed algorithm is evaluated with the real instances in PseudoBase.ConclusionThe experimental results have been demonstrated to provide a competitive and oftentimes better performance when compared against some chosen state-of-the-art RNA structure prediction algorithms.

Myoglobin Modeling to Study Species-Specific Differences in the Distance Between Heme Iron and Proximal and Distal Histidines

ObjectivesSpecies-specific differences in amino acid sequence influence myoglobin redox properties. Previous studies reported that the number and location of histidine residues can influence myoglobin redox stability. However, limited knowledge is currently available on the species-specific differences in the distances between the proximal (His 93) and distal (His 64) histidines and the heme iron in myoglobin. The objective of the current research was to utilize homology-based modeling to determine the distances between the proximal and distal histidines and the heme iron in the myoglobins from beef, pork, goat, bison, sheep, water-buffalo, venison, and emu.Materials and MethodsThe homology-based modeling was conducted using the Iterative Threading Assembly Refinement server (I-TASSER), which identifies the homologous structure models of myoglobins (beef, pork, goat, bison, sheep, water-buffalo, venison, and emu) from Protein Data Bank (PDB) using an algorithm named Local Meta-Threading-Server. The secondary structure of the target protein was predicted based on sequence information from the Protein Secondary Structure PREDiction algorithm. The lowest free energy conformations of the proteins were determined by SPICKER (a clustering approach to identify near-native protein folds). Refinement of the low free energy conformations were done by using Fragment Guided Molecular Dynamics simulations and ModRefiner. Prediction of the ligand-binding site of the target proteins were made by COACH algorithm. The distances between histidines (His 64 and His 93) and the iron in the heme group in the predicted structure of eight different species were determined using PyMOL, a computer software used for molecular visualization.ResultsThe homology-based modeling has shown that despite having 80% sequence similarity and conserved histidine residues (His 64 and His 93), the distance between the distal histidine (His 64) and heme iron varied between 4.3–5.5 Angstrom. Pork myoglobin has the shortest distance, and beef myoglobin has the longest distance. The distance between the proximal histidine (His 93) to the heme varied between 1.9 to 3 Angstrom; sheep myoglobin had the shortest and bison had the longest.ConclusionThe results suggest that in addition to the inherent differences in muscle biochemistry, variations in myoglobin structure also contributes to species-specific differences in meat color.

RNAknot: A new algorithm for RNA secondary structure prediction based on genetic algorithm and GRASP method.

The prediction of the optimal secondary structure for a given RNA sequence represents a challenging computational problem in bioinformatics. This challenge becomes harder especially with the discovery of different pseudoknot classes, which is a complex topology that plays diverse roles in biological processes. Many recent studies have been proposed to predict RNA secondary structure with some pseudoknot classes, but only a few of them have reached satisfying results in terms of both complexity and accuracy. Here we present RNAknot, a new method for predicting RNA secondary structure that contains the following components: stems, hairpin loops, multi-branched loops or multi-loops, bulge loops, and internal loops, in addition to two types of pseudoknots, H-type pseudoknot and Hairpin kissing. RNAknot is based on a genetic algorithm and Greedy Randomized Adaptive Search Procedure (GRASP), and it uses the free energy as fitness function to evaluate the obtained structures. In order to validate the performance of the presented method 131 tests have been performed using two datasets of 26 and 105 RNA sequences, which have been taken from the two data bases RNAstrand and Pseudobase respectively. The obtained results are compared with those of some RNA secondary structure prediction programs such as Vs_subopt, CyloFold, IPknot, Kinefold, RNAstructure, and Sfold. The results of this comparative study show that the prediction accuracy of our proposed approach is significantly improved compared to those obtained by the other programs. For the first dataset, RNAknot has the highest specificity (SP) (71.23%) and sensitivity (SN) (72.15%) averages compared to the other programs. Concerning the second dataset, the RNA secondary structure predictions obtained by the RNAknot correspond to the highest averages of SP (85.49%) and F-measure (79.97%) compared to the other programs. The program is available as a jar file in the link: www.bachmek.umi.ac.ma/wp-content/uploads/RNAknot.0.0.2.rar.