###### Abstract  
The observation and analysis of RNA molecules have proved crucial for the understanding of various processes in nature. Scientists have mined knowledge and drawn conclusions using experimental methods for decades. Leveraging advanced computational methods in recent years has led to fast and more accurate results in all areas of interest. One highly challenging task, in terms of RNA analysis, is the prediction of its structure, which provides valuable information about how it transforms and operates numerous significant tasks in organisms. In this paper, we focus on the prediction of the 2-D or secondary structure of RNA, specifically, on a rare but yet complex type of pseudoknot, the L-type pseudoknot, extending our previous framework specialized for H-type pseudoknots. We propose a grammar-based framework that predicts all possible L-type pseudoknots of a sequence in a reasonable response time, leveraging also the advantages of core biological principles, such as maximum base pairs and minimum free energy. In order to evaluate the effectiveness of our methodology, we assessed four performance metrics: precision; recall; Matthews correlation coefficient (MCC); and F1-score, which is the harmonic mean of precision and recall. Our methodology outperformed the other three well known methods in terms of Precision, with a score of 0.844, while other methodologies scored 0.500, 0.333, and 0.308. Regarding the F1-score, our platform scored 0.671, while other methodologies scored 0.661, 0.449, and 0.449. The proposed methodology surpassed all methods in terms of the MCC metric, achieving a score of 0.521. The proposed method was added to our RNA toolset, which aims to enhance the capabilities of biologists in the prediction of RNA motifs, including pseudoknots, and holds the potential to be applied in a multitude of biological domains, including gene therapy, drug design, and comprehending RNA functionality. Furthermore, the suggested approach can be employed in conjunction with other methodologies to enhance the precision of RNA structure prediction. s 2023 13 5168 10 March 2023 18 April 2023 19 April 2023 21 April 2023 21 April 2022-D, making them available to scientists for initial conclusions and rapid decision making. Thereafter, it is easier to map this secondary structure to the 3-D space for further analysis and experimentation. Various motifs, such as stems, hairpins, bulges, interior loops, and multi-branch loops, can be found in the RNA secondary structure, and there are several methodologies that can accurately predict these motifs using dynamic programming, thermodynamic models, stochastic methods, artificial intelligence, syntactic pattern recognition techniques, or a combination of these. Section 3 provides a detailed overview of the relevant research, including the state-of-the-art works. However, predicting a pseudoknot is a more complex task, and only a few existing algorithms are designed to handle the interconnection of pseudoknots. In that context, we introduced, in our previous research, two robust frameworks that are capable of predicting H-type pseudoknots, the most common type of this motif. Firstly, we presented Knotify [1] and an optimized version of this framework in terms of execution time, which predict H-type pseudoknots using syntactic pattern recognition, minimum free energy, and a parallel architecture to achieve high performance. These approaches show a high accuracy level in terms of core stems prediction and total base pair prediction, better than or comparable with state-of-the-art frameworks, depending on the metrics evaluated. Secondly, we enhanced the previous implementation by creating Knotify+ [2], which is capable of predicting more complex motifs of H-type pseudoknots that include bulges. Knotify+ maintains execution speed at a low level, contrary to the most well-known platforms, and has, on average, a level of accuracy equal to that of Knotify.