Current Bioinformatics - Volume 13, Issue 2, 2018

Background: Biological networks are used to represent interactions involving genes, DNA, RNA and proteins that are able to manipulate many cellular processes. Objective: The aim of this study is to evaluate whether prior knowledge can improve the quality of biological networks, in particular protein-protein interaction networks and gene regulatory networks. Method: Gene Ontology (GO) as well as three different types of semantic similarity measures were used to assess the interaction between biological networks so as to build the corresponding filtered networks. Both the original and the filtered networks were statistically compared against a reference network. Results and Conclusion: The results confirm the effectiveness of the GO-based measure HRSS as it improves the quality of the original network by removing many false interactions while maintaining the true interactions. In general, the inclusion of external sources of biological information to improve the quality of inferred knowledge (networks or any other model) is a fundamental step before the fusion of filtered -statistically validated- intermediate results.

Background: Peptides are chemicals built of 20 types of amino acids linked into long chain called sequence. Determination of amino acid sequence (order) in peptide is a very important issue of modern molecular biology. There is no direct chemical method that allows determining long peptide sequences. The approach to do so is to cut long sequence into short pieces, sequence them and then to figure out what was their order in the original long sequence (proper order determines long sequence). This process is called assembly and requires applying computational methods. In this paper, we present dedicated heuristic for strongly NP-hard variant of peptide assembly problem. Objective: The objective of the research was to propose a new method for peptide assembly problem. There are a few metaheuristic methods available in the literature, however their results (similarity of computed sequence compare to original one) are not good enough to be considered for practical use. The main goal was to design dedicated heuristic that takes into account problem properties and finally leads to better results. Method: The considered variant of the peptide assembly problem was formulated using graph theory. For metaheuristic methods available in the literature, a special variant of Hamiltonian problem had to be solved to find the final peptide sequence. For dedicated heuristic, instead of looking for the special variant of Hamiltonian path in this graph, the family of its sub-graphs (called adjoints) is searched for the Hamiltonian path. The space of acceptable solutions that need to be searched to find the optimal solution was strongly decreased in comparison to metaheuristic methods. Results: The computational experiment has been performed to test dedicated heuristic and the obtained results strongly exceed results of methods (solving this problem) which are available in the literature. The average result for the new dedicated heuristic is 96.07% compared to 59,7% for Tabu Search, 72,32% for GRASP and 88,74% for evolutionary algorithm. Conclusion: The obtained results clearly show that the new dedicated heuristic is much more useful in the process of peptide sequence determination.

Background: The ovarian follicle is an essential component of the reproductive process. It plays an important role in controlling the estrous cycle, determining estrous behaviour, ensuring oocyte competency and subsequent embryo survival rate, and determining both post ovulation corpus luteum function and progesterone synthesis. Traditionally, gene expression studies in the field of follicular development focus on the study of expression of candidate genes of interest. With the development of next-generation sequencing technologies, transcriptome profiling has become a powerful approach for identification of genes globally expressed in various tissues including ovarian follicles. Objective: To investigate potential differentially expressed genes associated with follicular development and gene expression profiles of bovine ovarian follicles at onset of deviation stages of a follicular wave. Method: Ovaries were removed, greatest (ODF1) and second greatest (ODF2) diameter follicles were isolated, and GCs were isolated from the two types of follicles (n=4). RNA samples of follicles were pooled within ODF1 and ODF2. The two cDNA libraries were sequenced, and mapped to the bovine RefSeq database. Gene ontology (GO) functional enrichment analysis was subjected and differentially expressed genes were analysed using visualization plug BiNGO of Cytoscape 3.0.0 software. KEGG pathways were performed and all the pathways and protein equivalent convert information of UniProt database of cattle. Twelve genes were selected randomly to verify their expression by QRT-PCR technology. Results: A total of 43,708,132 and 43,826,914 clean reads were obtained for ODF1 and ODF2, respectively. After being mapped to the bovine RefSeq database, a total of 15,519 genes were expressed (cut-off RPKM>0.5), of which 761 were highly expressed (cut-off RPKM>100) in both types of follicles. GO functional classification of these highly expressed genes revealed that many of them are involved in metabolic processes, multicellular organismal processes, and binding. Furthermore, 831 genes were identified as differentially expressed in ODF1 versus ODF2, in which 384 genes were up-regulated, and 447 genes were down-regulated. KEGG pathway analysis revealed eight differentially expressed genes in ODF1 vs. ODF2, QRT-PCR results showed that 9 of the 12 selected genes mRNA amounts were significantly greater in ODF1 than that ODF2 (P<0.05 or 0.01), which was consistent with the deep sequencing data. Conclusion: This study represents the first transcriptome investigation of bovine follicles at onset of deviation of the largest vs. second largest follicles. It provides a foundation for future investigation of the regulatory mechanisms involved in follicular development in cattle.

Background: Accumulating studies have indicated that the human microbiome plays critical roles in human health and disease. It is significantly important to analyze the relationships between microbe and disease, which is helpful for the understanding of disease mechanism, diagnosis and therapy. Predicting potential microbe-disease associations would not only boost understanding of the disease mechanism but also it will help to find biomarkers for disease diagnosis and prognosis. However, systems understanding of microbe-disease relationships is mostly undefined. Objective: It is important to analyze the relationships between microbe and disease, for this, we proposed a new method to predict potential microbe-disease associations. Method: In this article, we constructed a gene-disease bipartite network and a microbe-disease bipartite network based on known human gene-disease associations and microbe-disease interactions, respectively. Then, a model for predicting potential microbe-disease associations was designed based on the two bipartite networks. In order to evaluate the prediction performance of our proposed method, Leave-One-Out Cross Validation (LOOCV) and K-fold Cross Validation procedures were implemented. Results: Simulation results show that our method can achieve a reliable performance with AUC of 0.8993 based on microbe-disease associations downloaded from HMDAD database and human genedisease relationships downloaded from DisGeNET database. Furthermore, it also can reach to a sound performance with AUC of 0.8640 only based on the known microbe-disease associations in the framework of LOOCV, which is clearly better than the performance of state of the art KATZHMDA model with AUC of 0.8382. Conclusion: It is anticipated that our proposed model could be used to predict more potential microbedisease associations, which is helpful for the understanding of disease mechanism, diagnosis and prognosis.

Background: Increasing numbers of researchers apply linear B-cell epitope prediction in their research, as in the case of peptide-based vaccine design, diagnostic tests, disease prevention and antibody production. Online software offers major ways of epitope prediction. Objective: With the advent of more and more newly built software, a standard assessment for various prediction tools is urgently needed. Methods: From the users' perspective, we compared and evaluated the ability to correctly predict true epitopes of six different B-cell epitope prediction softwares: Bepipred, ABCpred, AApred, LBtope, BEST and SVMTrip, together with one Random group and five consensus groups. Fourteen experimentally confirmed proteins (including 60 linear epitopes) were collected into an epitope database. Positive value (PV) and minor predicted value (MPV) were used to quantify the method's prediction ability. Results: Bepipred, AApred, BEST and LBtope performed significantly better than the Random group in terms of PV and MPV. Based upon the average value of the two parameters, BEST was the most efficient tool. Conclusion: Bepipred, AApred and BEST are the most efficient tools for users to predict linear B-cell epitopes. In addition, consensus groups have the effect of gathering true predicted results, but they fail to greatly improve prediction performance.

Background: Inhibition of DPP-IV enzyme is an effective strategy for the treatment of type-2 diabetes mellitus, which involves the degradation of incretin hormones, glucagon like peptide (GLP-1) and gastric inhibitory polypeptide (GIP), being valuable in glucose tolerance and insulin secretion. A series of indole alkaloids from the medicinal plant Rauvolfia serpentina were identified as novel DPP-IV inhibitors using insilico prediction. Objective: Our study is aimed to identify the novel DPP-IV inhibitors for the treatment of diabetes through system level investigation. Method: Computational simulation techniques were used to analyze the molecular interaction between DPP-IV and a series of indole alkaloids. 3D structures of the indole alkaloids were retieved from Pubchem and PRIME KNApSAck databases. Online tools such as Molinspiration, ADMET and drug likeness properties of the indole alkaloids were investigated. Based on the minimum binding energy, stability of the docked complex was evaluated by molecular dynamic simulation. Results: Among the 20 indole alkaloids investigated, molecular docking analysis revealed that yohimbine has higher binding energy compared to other indole alkaloids. It exhibits three π - π stacking interactions with amino acids in hydrophobic S1 and S2 pocket of DPP-IV receptor. Different parameters like binding energy, intermolecular energy, inhibition constant and H-bonding between the ligands & the target were used to determine the extent of inhibition. Interestingly, 5 intermolecular hydrogen bonds were formed between receptor-inhibitor complex to facilitate inhibition. The stability of the docked complex was confirmed by molecular dynamics simulation. Conclusion: DPP-IV enzyme has a significant role in the regulation of glucose metabolism. Among the 20 indole alkaloids, yohimbine has a good binding energy towards DPP-IV and inhibits its function. Thus, yohimbine compound acts as a novel DPP-IV inhibitor for the treatment of type-2 diabetes.

Background: The diagnosis of diseases correctly became a challenge, and any error can cost patients life, especially when there is a lack of knowledge or expertise related to a disease, it often results in patient’s death or takes the form of an epidemic, as we have seen in the case of Ebola. Objective: The automation and the development of reliable diagnostic systems became a necessity. Through the use of technology, we can automatically share the knowledge without formal interaction as well as we can identify areas where the disease is spreading while it is not known by the doctors there. Methods: We have presented a complete system that utilizes a combination of one of the best techniques in the field of parallelism, classification, and knowledge sharing. We have used two data sets (DDSM and Belarus Tuberculosis data) to test the applicability of the idea. After retrieving the data, the images are preprocessed, and then Gray level co-occurrence matrix features have been extracted and finally passed to training using three versions of support vector machines. Results: GPU-Accelerated SVM outperformed both parallelized SVM and sequential SVM using breast cancer data, but with lung CT images, GPU-accelerated LIBSVM have not given a remarkable speed-up because the data is small and the gain is lost due to the gpu-cpu memory and cpu-gpu transfer time. The accuracy performances given by three SVMs were identical. Conclusion: Automation through knowledge sharing and parallel computing can help to deal across the world with diseases and it will be easy for doctors to draw the inference.

Background: EphrinB2 ligand and its associated receptor have been responsible for mediating signal transduction pathways related to developmental, cell segmentation processes, cell proliferation, etc. Aberrations in ephb2 gene result in altered EPHB2 receptor protein, which in turn, affect the related biological processes. Objective: The objective of this study is to determine the most lethal, non-synonymous SNPs associated with ephb2 gene and administer the effects of the mutation in the ligand-binding domain of EPHB2 protein. Understanding the molecular consequences of the detrimental SNPs and the mechanism, via which it deters the biological functions of the protein, will aid in predicting its impact on biological pathways. Methods: We shortlisted the non-synonymous single nucleotide variants and observed the impact on phenotypic properties based on molecular dynamics simulations. Results: Results suggest the introduction of detrimental nsSNP causes reduction in its stability, binding affinity for the associated ligand, namely Ephrin, and increases the enthalpy of the entire system. Conclusion: These observations further enhance our understanding at the molecular level about the effects of genomic variants occurring in populations, and the mechanism by which, it leads to changes in susceptibility towards certain disease symptoms on the individual basis.

Background: Protein folding depends on the nature of the amino acid sequence. Once folding process of the amino acid sequence is successful, the protein becomes functional. Recently, a two-dimensional hydrophobic-polar (2D HP) model algorithm has been developed for the effective prediction of protein folding. However, the particular 2D HP models still lack an algorithm for protein folding prediction. Objective: Some developed algorithms still require further improvement in terms of accuracy and search stability. Method: In order to evaluate its improvement for protein folding of the 2D HP model in this study, we propose the hybrid high exploration particle swarm optimization (HHEPSO) method, which employs the HEPSO algorithm for optimization which combines both hill climbing and greedy algorithms for local search. Results: Several algorithms for protein structure prediction on the 2D square and triangular lattice models are compared with HHEPSO. In terms of accuracy and stability, our proposed HHEPSO revealed better performance than most of the test algorithms. HHEPSO also successfully deals with protein structure prediction problems for the longer amino acid sequences. Conclusion: Our proposed HHEPSO algorithm is accurate and effective for protein structure prediction for a 2D triangular lattice model.

Background: Proteomics was built around two-dimensional (2D) gel electrophoresis. Accurately analyzing the images generated from 2D gel electrophoresis for spot detection is a timeconsuming process especially for high-resolution and large images. Objective: In this paper, we present an accurate GPU-accelerated software tool for the detection and quantification of protein spots in 2D gel electrophoresis images. Method: We adopt pixel-based approach that employs wavelet relational fuzzy C-means clustering and distance transform to detect and quantify the protein spots. This pixel-based spot detection approach is more accurate than the contour-based approaches; however it is compute-intensive. So, along with algorithmic optimizations, we present the mapping and optimization of the pixel-based spot detection algorithm onto graphics processing units (GPUs); including NVIDIA and AMD GPUs. Results: This approach is proved to exhibit better spot detection in quantitative comparisons with the commercial software tools. Specifically, it achieves a degree of improvement in F-measure of 21.237% and 11.716% on the average compared to Delta2D and Melanie, respectively. We carry out experiments on images of large size and high resolution for healthy and diseased samples. Our implementation has accomplished up to five orders of magnitude speedup compared to the single-threaded MATLAB implementation. Conclusion: We proposed an accurate and efficient tool for detecting and quantifying the protein spots in 2D Gel Electrophoresis images. Our tool outperforms commercial software tools in accuracy of detecting protein spots while achieving significantly better performance than single-threaded MATLAB implementation by utilizing the GPU accelerators.

Background: Magnolia Bark Extract (MBE), has been utilized in Asia as herbal medicine and a broad range of its potential efficacy was considered such as anti-inflammatory, anti-oxidant, and anti-bacterial. Much interest has been focused on pharmacological actions of two primary active phenolic MBE constituents (magnolol and honokiol). Objective: Our aim is computational studies of the interactions between the bioactive components of MBE and Human Serum Albumin (HSA) that is necessary to provide more information about the binding process at molecular level. This may supply the better understanding of the HSA properties as carrier protein and useful information for future studies about the transport of drugs. Method: In this work, the interactions between MBE with HSA were investigated using molecular docking. The binding modes of MBE bioactive constituents were compared. In addition, magnolol and honokiol as two primary active constituents of MBE were subjected to a 14 ns Molecular Dynamics (MD) simulation to further validate the docking results. Results: Relatively suitable binding energies were observed during docking results in the range of - 28.40 to -36.43 kJ. mol-1 and 4-methoxy honokiol showed most negative binding energy equal to -36.43 kJ. mol-1. In addition, analysis of MD simulations trajectories show that root mean square deviation profiles of magnolol and honokiol were fairly stable during the whole simulation time that indicated the orientations were produced by the docking studies are reliable. Conclusion: In conclusion, all the molecular modeling results revealed that these components were strongly bound to HSA. In addition, a small change of HSA tertiary structure was observed upon interaction with magnolol and honokiol.

Background: Genomes of organisms contains a variety of repeated structures of various lengths and type, interspersed or tandem. Tandem repeats play important role in molecular biology as they are related to genetic backgrounds of inherited diseases, and also they can serve as markers for DNA mapping and DNA fingerprinting. Improving the efficiency of algorithms for searching the tandem repeats in DNA sequences can lead to many useful applications in the area of genomics. Objective: We introduce an efficient algorithm of O(n) for searching the maximum length exact tandem repeats in genomes. Method: Algorithm is based on the use of the Enhanced Suffix Array (ESA). ESA consists of Suffix Array (SA) and Longest Common Prefix (LCP) array. SA is an array of all sorted suffixes of a string and LCP array stores the lengths of the longest common prefixes between all pairs of consecutive suffixes in a sorted suffix array. Results: We compare the results of our computation with other existing application: Burrows Wheeler Tandem Repeat Searcher (BWtrs) for searching the exact tandem repeats. We provided an open source standalone application called TR-ESA (available at: www.algorithms-akgec-shivika.in/tandem), which implements searching of exact maximum length tandem repeat. Conclusion: Tool is remarkably efficient and powerful which allows the analysis of complete genomes having exact tandem repeats.