Comparison Of HSRNAFold and RNAFold Algorithms for RNA Secondary Structure Prediction.

Ribonucleic Acid (RNA) has important structural and functional roles in the cell and plays roles in many stages of protein synthesis. The structure of RNA largely determines its function

Adaptive And Cooperative Harmony Search Models For Rna Secondary Structure Prediction

Penentuan fungsi molekul RNA amat bergantung kepada struktur sekunderya. Kaedah fizikal yang sedia ada untuk penentuan struktur sekunder adalah mahal dan memakan masa. Determining the function of RNA molecules relies heavily on its secondary structure

Adaptive and cooperative harmony search models for RNA secondary structure prediction

Penentuan fungsi molekul RNA amat bergantung kepada struktur sekundernya. Kaedah fizikal yang sedia ada untuk penentuan struktur sekunder adalah mahal dan memakan masa. Beberapa algoritma telah dicadangkan untuk peramalan struktur sekunder RNA, termasuk pengaturcaraan dinamik dan algoritma metaheuristik. Determining the function of RNA molecules relies heavily on its secondary structure. The current physical methods for secondary structure determination are expensive and time consuming. Several algorithms have been proposed for the RNA secondary structure prediction, including dynamic programming and metaheuristic algorithms

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Harmony search algorithms for ab initio protein tertiary structure prediction.

Meramal struktur tertier protein daripada jujukan linear struktur-struktur tersebut adalah suatu cabaran besar dalam bidang biologi. Tesis ini berkisar tentang ramalan struktur tertier protein ab initio. Algoritma Gelintar Harmoni (HSA) disesuaikan untuk ramalan struktur tertier protein di mana keseluruhan proses dimodelkan sebagai pengoptimunan permasalahan. HAS telah pun memperolehi penyelesaian-penyelesaian yang layak tetapi tidak sehebat yang dilaporkan di dalam penulisan. Predicting the tertiary structure of proteins from their linear sequence is really a big challenge in biology. This thesis considers the ab initio protein tertiary structure prediction. The Harmony Search Algorithm (HSA) has been adapted for the protein structure prediction by modeling the problem as an optimization problem. HSA has obtained feasible solutions but not as magnificent as those reported in the literature

Using evolutionary algorithms and machine learning to explore sequence space for the discovery of antimicrobial peptides

We present a proof-of-concept methodology for efficiently optimizing a chemical trait by using an artificial evolutionary workflow. We demonstrate this by optimizing the efficacy of antimicrobial peptides (AMPs). In particular, we used a closed-loop approach that combines a genetic algorithm, machine learning, and in vitro evaluation to improve the antimicrobial activity of peptides against Escherichia coli. Starting with a 13-mer natural AMP, we identified 44 highly potent peptides, achieving up to a ca. 160-fold increase in antimicrobial activity within just three rounds of experiments. During these experiments, the conformation of the peptides selected was changed from a random coil to an α-helical form. This strategy not only establishes the potential of in vitro molecule evolution using an algorithmic genetic system but also accelerates the discovery of antimicrobial peptides and other functional molecules within a relatively small number of experiments, allowing the exploration of broad sequence and structural space

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

BACKGROUND: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. RESULTS: In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications. CONCLUSION: The iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1561-8) contains supplementary material, which is available to authorized users

Adapting And Enhancing Hybrid Computational Methods For RNA Secondary Structure Prediction

The secondary structure of RNA with pseudoknots is widely utilized for tracing the RNA tertiary structure, which is a key to understanding the functions of the RNAs and their useful roles in developing drugs for viral diseases. Experimental methods for determining RNA tertiary structure are time consuming and tedious. Therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. This thesis presents a hybrid method to predict the RNA pseudoknot secondary structures by combining detection methods with dynamic programming algorithms

Structure and dynamics of a predicted ferredoxin-like selenoprotein in Japanese encephalitis virus

Homologues of the selenoprotein glutathione peroxidase (GPx) have been previously identified in poxviruses and in RNA viruses including HIV-1 and hepatitis C virus (HCV). Sequence analysis of the NS4 region of Japanese encephalitis virus (JEV) suggests it may encode a structurally related but functionally distinct selenoprotein gene, more closely related to the iron-binding protein ferredoxin than to GPx, with three highly conserved UGA codons that align with essential Cys residues of ferredoxin. Comparison of the probe JEV sequence to an aligned family of ferredoxin sequences gave an overall 30.3% identity and 45.8% similarity, and was statistically significant at 4.9 S.D. (P < 10-6) above the average score computed for randomly shuffled sequences. A 3-dimensional model of the hypothetical JEV protein (JEV model) was constructed by homology modeling using SYBYL, based upon a high resolution X-ray structure of ferredoxin (PDB code: 1awd). The JEV model and the model from 1awd were subsequently subjected to molecular dynamics simulations in aqueous medium using AMBER 6. The solution structure of the JEV model indicates that it could fold into a tertiary structure globally similar to ferredoxin 1awd, with RMSD between the averaged structures of 1.8 Å for the aligned regions. The modeling and MD simulations data also indicate that this structure for the JEV protein is energetically favorable, and that it could be quite stable at room temperature. This protein might play a role in JEV infection and replication via TNF and other cellular stimuli mediated via redox mechanisms

14th Conference on DATA ANALYSIS METHODS for Software Systems

DAMSS-2023 is the 14th International Conference on Data Analysis Methods for Software Systems, held in Druskininkai, Lithuania. Every year at the same venue and time. The exception was in 2020, when the world was gripped by the Covid-19 pandemic and the movement of people was severely restricted. After a year’s break, the conference was back on track, and the next conference was successful in achieving its primary goal of lively scientific communication. The conference focuses on live interaction among participants. For better efficiency of communication among participants, most of the presentations are poster presentations. This format has proven to be highly effective. However, we have several oral sections, too. The history of the conference dates back to 2009 when 16 papers were presented. It began as a workshop and has evolved into a well-known conference. The idea of such a workshop originated at the Institute of Mathematics and Informatics, now the Institute of Data Science and Digital Technologies of Vilnius University. The Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea, which gained enthusiastic acceptance from both the Lithuanian and international scientific communities. This year’s conference features 84 presentations, with 137 registered participants from 11 countries. The conference serves as a gathering point for researchers from six Lithuanian universities, making it the main annual meeting for Lithuanian computer scientists. The primary aim of the conference is to showcase research conducted at Lithuanian and foreign universities in the fields of data science and software engineering. The annual organization of the conference facilitates the rapid exchange of new ideas within the scientific community. Seven IT companies supported the conference this year, indicating the relevance of the conference topics to the business sector. In addition, the conference is supported by the Lithuanian Research Council and the National Science and Technology Council (Taiwan, R. O. C.). The conference covers a wide range of topics, including Applied Mathematics, Artificial Intelligence, Big Data, Bioinformatics, Blockchain Technologies, Business Rules, Software Engineering, Cybersecurity, Data Science, Deep Learning, High-Performance Computing, Data Visualization, Machine Learning, Medical Informatics, Modelling Educational Data, Ontological Engineering, Optimization, Quantum Computing, Signal Processing. This book provides an overview of all presentations from the DAMSS-2023 conference