Current Bioinformatics - Volume 15, Issue 2, 2020

Background: Over the last few decades, a search for the theory of protein folding has grown into a full-fledged research field at the intersection of biology, chemistry and informatics. Despite enormous effort, there are still open questions and challenges, like understanding the rules by which amino acid sequence determines protein secondary structure. Objective: In this review, we depict the progress of the prediction methods over the years and identify sources of improvement. Methods: The protein secondary structure prediction problem is described followed by the discussion on theoretical limitations, description of the commonly used data sets, features and a review of three generations of methods with the focus on the most recent advances. Additionally, methods with available online servers are assessed on the independent data set. Results: The state-of-the-art methods are currently reaching almost 88% for 3-class prediction and 76.5% for an 8-class prediction. Conclusion: This review summarizes recent advances and outlines further research directions.

Background: microRNAs are small non-coding RNAs which inhibit translational and post-transcriptional processes whereas long non-coding RNAs are found to regulate both transcriptional and post-transcriptional gene expression. Medicago truncatula is a well-known model plant for studying legume biology and is also used as a forage crop. In spite of its importance in nitrogen fixation and soil fertility improvement, little information is available about Medicago non-coding RNAs that play important role in symbiosis. Objective: In this study we have tried to understand the role of Medicago ncRNAs in symbiosis and regulation of transcription factors. Methods: We have identified novel miRNAs by computational methods considering various parameters like length, MFEI, AU content, SSR signatures and tried to establish an interaction model with their targets obtained through psRNATarget server. Results: 149 novel miRNAs are predicted along with their 770 target proteins. We have also shown that 51 of these novel miRNAs are targeting 282 lncRNAs. Conclusion: In this study role of Medicago miRNAs in the regulation of various transcription factors are elucidated. Knowledge gained from this study will have a positive impact on the nitrogen fixing ability of this important model plant, which in turn will improve the soil fertility.

Background: The host tropism determinants of influenza virus, which cause changes in the host range and increase the likelihood of interaction with specific hosts, are critical for understanding the infection and propagation of the virus in diverse host species. Methods: Six types of protein sequences of influenza viral strains isolated from three classes of hosts (avian, human, and swine) were obtained. Random forest, naïve Bayes classification, and knearest neighbor algorithms were used for host classification. The Java language was used for sequence analysis programming and identifying host-specific position markers. Results: A machine learning technique was explored to derive the physicochemical properties of amino acids used in host classification and prediction. HA protein was found to play the most important role in determining host tropism of the influenza virus, and the random forest method yielded the highest accuracy in host prediction. Conserved amino acids that exhibited host-specific differences were also selected and verified, and they were found to be useful position markers for host classification. Finally, ANOVA analysis and post-hoc testing revealed that the physicochemical properties of amino acids, comprising protein sequences combined with position markers, differed significantly among hosts. Conclusion: The host tropism determinants and position markers described in this study can be used in related research to classify, identify, and predict the hosts of influenza viruses that are currently susceptible or likely to be infected in the future.

Background: The prediction of RNA secondary structure using optimization algorithms is key to understand the real structure of an RNA. Evolutionary algorithms (EAs) are popular strategies for RNA secondary structure prediction. However, compared to most state-of-the-art software based on DPAs, the performances of EAs are a bit far from satisfactory. Objective: Therefore, a more powerful strategy is required to improve the performances of EAs when applied to the prediciton of RNA secondary structures. Methods: The idea of quantum computing is introduced here yielding a new strategy to find all possible legal paired-bases with the constraint of minimum free energy. The sate of a stem pool with size N is encoded as a population of QGA, which is represented by N quantum bits but not classical bits. The updating of populations is accomplished by so-called quantum crossover operations, quantum mutation operations and quantum rotation operations. Results: The numerical results show that the performances of traditional EAs are significantly improved by using QGA with regard to not only prediction accuracy and sensitivity but also complexity. Moreover, for RNA sequences with middle-short length, QGA even improves the state-of-art software based on DPAs in terms of both prediction accuracy and sensitivity. Conclusion: This work sheds an interesting light on the applications of quantum computing on RNA structure prediction.

Background: MicroRNAs (miRNAs) play an important role in the pathogenesis of various renal diseases, including Chronic Kidney Diseases (CKD). CKD refers to the gradual loss of kidney function with the declining Glomerular Functional Rate (GFR). Objective: This study focused on the regulatory mechanism of miRNA to control gene expression in CKD. Methods: In this context, two lists of Differentially Expressed Genes (DEGs) were obtained; one from the three selected experiments by setting a cutoff p-value of <0.05 (List A), and one from a list of target genes of miRNAs (List B). Both lists were then compared to get a common dataset of 33 miRNAs, each had a set of DEGs i.e. both up-regulated and down-regulated genes (List C). These data were subjected to functional enrichment analysis, network illustration, and gene homology studies. Results: This study confirmed the active participation of various miRNAs i.e. hsa -miR-15a-5p, hsa-miR-195-5p, hsa-miR-365-3p, hsa-miR-30a-5p, hsa-miR-124-3p, hsa-miR-200b-3p, and hsamiR- 429 in the dysregulation of genes involved in kidney development and function. Integrated analyses depicted that miRNAs modulated renal development, homeostasis, various metabolic processes, immune responses, and ion transport activities. Furthermore, homology studies of miRNA-mRNA hybrid highlighted the effect of partial complementary binding pattern on the regulation of genes by miRNA. Conclusion: The study highlighted the great values of miRNAs as biomarkers in kidney diseases. In addition, the need for further investigations on miRNA-based studies is also commended in the development of diagnostic, prognostic, and therapeutic tools for renal diseases.

Background: The lungs are directly exposed to pollutants, pathogens, allergens, and chemicals, which might lead to physiological disorders. During the Bhopal gas disaster, the lungs of the victims were exposed to various chemicals. Here, using molecular modelling studies, we describe the effects of these chemicals (Dimethyl urea, Trimethyl urea, Trimethyl isocyanurate, Alphanaphthol, Butylated hydroxytoluene and Carbaryl) on pulmonary immune proteins. Objectives: In the current study, we performed molecular modelling methods like molecular docking and molecular dynamics simulation studies to identify the effects of hydrolytic products of MIC and dumped residues on the pulmonary immune proteins. Methods: Molecular docking studies of (Dimethyl urea, Trimethyl urea, Trimethyl isocyanurate, Alphanaphthol, Butylated hydroxytoluene and Carbaryl) on pulmonary immune proteins was performed using the Autodock 4.0 tool, and gromacs was used for the molecular dynamics simulation studies to get an insight into the possible mode of protein-ligand interactions. Further, in silico ADMET studies was performed using the TOPKAT protocol of discovery studio. Results: From docking studies, we found that surfactant protein-D is inhibited most by the chemicals alphanaphthol (dock score, -5.41Kcal/mole), butylated hydroxytoluene (dock score,-6.86 Kcal/mole), and carbaryl (dock score,-6.1 Kcal/mole). To test their stability, the obtained dock poses were placed in a lipid bilayer model system mimicking the pulmonary surface. Molecular dynamics simulations suggest a stable interaction between surfactant protein-D and carbaryl. Conclusion: This, study concludes that functioning of surfactant protein-D is directly or indirectly affected by the carbaryl chemical, which might account for the increased susceptibility of Bhopal gas disaster survivors to pulmonary tuberculosis.

Background: The standard approach for transcriptomic profiling involves high throughput short-read sequencing technology, mainly dominated by Illumina. However, the short reads have limitations in transcriptome assembly and in obtaining full-length transcripts due to the complex nature of transcriptomes with variable length and multiple alternative spliced isoforms. Recent advances in long read sequencing by the Oxford Nanopore Technologies (ONT) offered both cDNA as well as direct RNA sequencing and has brought a paradigm change in the sequencing technology to greatly improve the assembly and expression estimates. ONT enables molecules to be sequenced without fragmentation resulting in ultra-long read length enabling the entire genes and transcripts to be fully characterized. The direct RNA sequencing method, in addition, circumvents the reverse transcription and amplification steps. Objective: In this study, RNA sequencing methods were assessed by comparing data from Illumina (ILM), ONT cDNA (OCD) and ONT direct RNA (ODR). Methods: The sensitivity & specificity of the isoform detection was determined from the data generated by Illumina, ONT cDNA and ONT direct RNA sequencing technologies using Saccharomyces cerevisiae as model. Comparative studies were conducted with two pipelines to detect the isoforms, novel genes and variable gene length. Results: Mapping metrics and qualitative profiles for different pipelines are presented to understand these disruptive technologies. The variability in sequencing technology and the analysis pipeline were studied.