International Journal of Bioinformatics Research and Applications最新文献

In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.

Helicobacter pylori are one of the most common bacterial pathogens in humans whose seropositivity increases with age and low socio-economic status. Due to presence of its pathogenic-island causes chronic persistent and atrophic gastritis in adults and children that often culminate in development of gastric and duodenal ulcers. Studies indicate that infected individuals have two to sixfold increased risk of developing gastric cancer and mucosal associated lymphoid tissue lymphoma compared to their uninfected counterparts. The complete genome sequences have provided a plethora of potential drug targets. Subtractive study holds the promise of providing a conceptual framework for identification of potential drug targets and providing insights to understand the biological regulatory mechanisms in diseases, which are playing an increasingly important role in searching for novel drug targets from the information contained in genomics. In this paper, we discuss subtractive study approaches for identifying drug targets, with the emphasis on the modelling of target protein and docking of the modelled protein with probable ligand by using computational tools.

Structure prediction of proteins is considered a limiting step and determining factor in drug development and in the introduction of new therapies. Since the 3D structures of proteins determine their functionalities, prediction of dihedral angles remains an open and important problem in bioinformatics, as well as a major step in discovering tertiary structures. This work presents a method that predicts values of the dihedral angles φ and ψ for enzyme loops based on data derived from amino acid sequences. The prediction of dihedral angles is implemented through a neural network based mining mechanism. The amino acid sequence data represents 6342 enzyme loop chains with 18,882 residues. The initial neural network input was a selection of 115 features and the outputs were the predicted dihedral angles φ and ψ. The simulation results yielded a 0.64 Pearson's correlation coefficient. After feature selection through determining insignificant features, the input feature vector size was reduced to 45, while maintaining close to identical performance.

The human gut is one of the most densely populated microbial communities in the world. The interaction of microbes with human host cells is responsible for several disease conditions and of criticality to human health. It is imperative to understand the relationships between these microbial communities within the human gut and their roles in disease. In this study we analyse the microbial communities within the human gut and their role in Inflammatory Bowel Disease (IBD). The bacterial communities were interrogated using Length Heterogeneity PCR (LH-PCR) fingerprinting of mucosal and luminal associated microbial communities for a class of healthy and diseases patients.

The incidence of bacterial disease has increased tremendously in the last decade, because of the emergence of drug resistance strains within the bacterial pathogens. The present study was to investigate the antibacterial compound 2,5-di-tert-butyl-1,4-benzoquinone (DTBBQ) isolated from marine Streptomyces sp. VITVSK1 as a potent antibacterial agent. The antibacterial potential of DTBBQ was investigated against RNA Polymerase (PDB ID-1I6V) by in silico molecular docking tools. Results of our study showed the high affinity interaction between DTBBQ and RNA polymerase and also confirmed the drug likeliness of DTBBQ using ADMET in silico pharmacology tools. Our findings suggest that DTBBQ could be used as antibacterial drug to defend the emerging antibacterial resistance.

Single nucleotide polymorphism studies have recently received significant amount of attention from researchers in many life science disciplines. Previous researches indicated that a series of SNPs from the same chromosome, called haplotype, contains more information than individual SNPs. Hence, discovering ways to reconstruct reliable Single Individual Haplotypes becomes one of the core issues in the whole-genome research nowadays. However, obtaining sequence from current high-throughput sequencing technologies always contain inevitable sequencing errors and/or missing information. The SIH reconstruction problem can be formulated as bi-partitioning the input SNP fragment matrix into paternal and maternal sections to achieve minimum error correction; a problem that is proved to be NP-hard. In this study, we introduce a greedy approach, named RadixHap, to handle data sets with high error rates. The experimental results show that RadixHap can generate highly reliable results in most cases. Furthermore, the algorithm structure of RadixHap is particularly suitable for whole-genome scale data sets.

Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets. Our databases are E. coli and C. jejuni. In the first scenario, 20% and 17% of proteins in cut of E. coli and C. jejuni are drug targets and in the second scenario 53% and 63% of proteins in cut are drug targets respectively.

Genome analysis of thermophilic cyanobacterium, Thermosynechococcus elongatus BP-1 revealed factors ruling choices of codons in this organism. Multiple parameters like Nc, GC3s, RSCU, Codon Adaptation Index (CAI), optimal and rare codons, codon-pair context and amino acid usage were analysed and compositional constraint was identified as major factor. Wide range of Nc values for the same GC3 content suggested the role of translational selection. Mutational bias is suggested at synonymous position. Among optimal codons for translation, most were GC-ending. Seven codons (AGA, AGG, AUA, UAA, UAG, UCA and UGA) were found to have least occurrence in the entire genome and except stop codons all were A-ending (exception AGG). Most widely used codon-pair in the genome are G-ending or C-ending and A-ending or U-ending codons make pair with G-ending or C-ending codons. Amino acids which are largely distributed in T. elongatus tend to use G-ending or C-ending codons most frequently. Findings showed cumulative role of translational selection, translational accuracy and gene expression levels with mutational bias as key player in codon selection pattern of this organism.

We propose a new algorithm, PMS6, for the (l,d)-motif discovery problem in which we are to find all strings of length l that appear in every string of a given set of strings with at most d mismatches. The run time ratio PMS5/PMS6, where PMS5 is the fastest previously known algorithm for motif discovery in large instances, ranges from a high of 2.20 for the (21,8) challenge instances to a low of 1.69 for the (17,6) challenge instances. Both PMS5 and PMS6 require some amount of pre-processing. The pre-processing time for PMS6 is 34 times faster than that for PMS5 for (23,9) instances. When pre-processing time is factored in, the run time ratio PMS5/PMS6 is as high as 2.75 for (13,4) instances and as low as 1.95 for (17,6) instances.