The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

Proteins are macromolecules essential for living organisms. However, to perform their function, proteins need to achieve their Native Structure (NS). The NS is reached fast in nature. By contrast, in silico, it is obtained by solving the Protein Folding problem (PFP) which currently has a long execution time. PFP is computationally an NP-hard problem and is considered one of the biggest current challenges. There are several methods following different strategies for solving PFP. The most successful combine computational methods and biological information: I-TASSER, Rosetta (Robetta server), AlphaFold2 (CASP14 Champion), QUARK, PEP-FOLD3, TopModel, and GRSA2-SSP. The first three named methods obtained the highest quality at CASP events, and all apply the Simulated Annealing or Monte Carlo method, Neural Network, and fragments assembly methodologies. In the present work, we propose the GRSA2-FCNN methodology, which assembles fragments applied to peptides and is based on the GRSA2 and Convolutional Neural Networks (CNN). We compare GRSA2-FCNN with the best state-of-the-art algorithms for PFP, such as I-TASSER, Rosetta, AlphaFold2, QUARK, PEP-FOLD3, TopModel, and GRSA2-SSP. Our methodology is applied to a dataset of 60 peptides and achieves the best performance of all methods tested based on the common metrics TM-score, RMSD, and GDT-TS of the area.

Objective: To know the expectations of teachers about the importance of statistical literacy, whether they teach the subject of statistics or those that are directly related to it, in the degree courses offered by the institution, using software with a user-friendly statistical application that contributes to strengthening teaching skills, the curriculum and student learning. Theoretical Framework: The conceptual basis includes some research that points out what has been done to generate a theoretical perspective as a foundation. In this way, reference is made to the central theme of mathematics, specifically statistics, as it improves company quality and productivity. However, some approaches assert a natural resistance of students towards learning it due to the tedium it implies or because the instructor needs more resources for teaching. For this reason, the national reforms sought to remedy this situation, together with the use of technology that favours the meaningful learning of this subject. Method: The research was framed under the quantitative approach, reinforced by a four-point Likert scale questionnaire through the Web application Google Forms to determine teachers' perceptions of the importance of literacy in statistics using the MINITAB software. After the data collection, graphical representations were made to facilitate the interpretation of these data. The population sample was non-probabilistic and convenient for reasons of economy, time, and availability of teachers to answer the survey. Results and Discussion: The results emphasise that the majority of the respondents have taught mathematics. Almost all of these were assigned statistics. They also expressed the importance of statistics as fundamental for them during vocational training and that it is relevant to many areas of knowledge and its application in various industrial processes. On the other hand, most respondents stated that using alternative means for teaching statistics is essential and that by implementing technology, they emphasise statistical literacy. Regarding the use of MINITAB as a learning tool, a large percentage of the surveyed population would like to be trained, expressing interest in using it in their teaching. On the other hand, statistics education today is considered a discipline that has been reborn and remains in continuous consolidation and expansion. Its implementation has increased worldwide at all levels of education and by most higher education institutions, thus developing a statistical culture. Others claim it is a complex, multifaceted and intertwined reality, as modern statistics tends towards reductionism. However, the commitment to using technology brings great benefits and produces better academic results. Research Implications: Statistics literacy is a fundamental area of opportunity for higher education teachers and students, opening up a range of possibilities. It is a branch of mathematics whose application is diversified in the careers offered by the institution and the labour field. Once teachers have been trained in MINITAB, they will have the digital tool to reproduce what they have learned, applying it in the classroom and in academic research. Originality/Value: The implementation of MINITAB as a didactic tool in the teaching of statistics adds advantages for students, as they will acquire instrumental competencies for data examination, trend discovery, and pattern anticipation, among other opportunities and challenges. Nowadays, the use of technology in the classroom is always vital, as it allows teachers to design teaching units that adapt to student needs, change the teaching style, capture the students' interest, discover the advantage of optimising statistical calculations, and stimulate curiosity and foresight, among other things.

Recent advancements have been made in the precise prediction of protein structures within the Protein Folding Problem (PFP), particularly in relation to minimizing the energy function to achieve stable and biologically relevant protein structures. This problem is classified as NP-hard within computational theory, necessitating the development of various techniques and algorithms. Bio-inspired algorithms have proven effective in addressing NP-hard challenges in practical applications. This study introduces a novel hybrid algorithm, termed GRSABio, which integrates the strategies of Jumping Spider Algorithm (JSOA) with the Golden Ratio Simulated Annealing (GRSA) for peptide prediction. Furthermore, the GRSABio algorithm incorporates a Convolutional Neural Network for fragment prediction (FCNN), forms an enhanced methodology called GRSABio-FCNN. This integrated framework achieves improved structure refinement based on energy for protein prediction. The proposed enhanced GRSABio-FCNN approach was applied to a dataset of 60 peptides. The Wilcoxon and Friedman statistics test were employed to compare the GRSABio-FCNN results against recent state-of-the-art-approaches. The results of these tests indicate that the GRSABio-FCNN approach is competitive with state-of-the-art methods for peptides up to 50 amino acids in length and surpasses leading PFP algorithms for peptides with up to 30 amino acids.