Current Issue : July - September Volume : 2014 Issue Number : 3 Articles : 5 Articles
Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt\nto solve the problem by exploring possible structures and finding the one with the minimum free energy.However, these algorithms\nperform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral\nsearch framework that uses parallel processing techniques to expedite exploration by starting fromdifferent points. In our approach,\na set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined\nperiod of time. The improved solutions are stored threadwise.When the threads finish, the solutions are merged together and the\nduplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure\nprediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping.We use both the low\nresolution hydrophobic-polar energy model and the high-resolution 20 Ã?â?? 20 energy model for search guiding. The experimental\nresults show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search\napproaches for both energy models on three-dimensional face-centred-cubic lattice.We also experimentally show the effectiveness\nof mixing energy models within parallel threads....
We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network\ncompletion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent\nwith the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we\npresent a new method for network completion using dynamic programming and least-squares fitting. This method can find an\noptimal solution in polynomial time if themaximumindegree of the network is bounded by a constant.We evaluate the effectiveness\nof our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method\ncan distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal\ngene expression data....
Background. Large-scale bisulfite treatment and short reads sequencing technology allow comprehensive estimation of methylation\nstates of Cs in the genomes of different tissues, cell types, and developmental stages. Accurate characterization of DNA methylation\nis essential for understanding genotype phenotype association, gene and environment interaction, diseases, and cancer. Aligning\nbisulfite short reads to a reference genome has been a challenging task.We compared five bisulfite short read mapping tools, BSMAP,\nBismark, BS-Seeker, BiSS, and BRAT-BW, representing two classes of mapping algorithms (hash table and suffix/prefix tries). We\nexamined their mapping efficiency (i.e., the percentage of reads that can be mapped to the genomes), usability, running time,\nand effects of changing default parameter settings using both real and simulated reads. We also investigated how preprocessing\ndata might affect mapping efficiency. Conclusion. Among the five programs compared, in terms of mapping efficiency, Bismark\nperforms the best on the real data, followed by BiSS, BSMAP, and finally BRAT-BWand BS-Seeker with very similar performance.\nIf CPU time is not a constraint, Bismark is a good choice of program formapping bisulfite treated short reads. Data quality impacts\na great deal mapping efficiency. Although increasing the number of mismatches allowed can increase mapping efficiency, it not only\nsignificantly slows down the program, but also runs the risk of having increased false positives. Therefore, users should carefully\nset the related parameters depending on the quality of their sequencing data....
Listeria monocytogenes is a gram-positive, foodborne bacterium responsible for disease in humans and animals. Listeriolysin O\n(LLO) is a required virulence factor for the pathogenic effects of L. monocytogenes. Bioinformatics revealed conserved putative\nepitopes of LLO that could be used to develop monoclonal antibodies against LLO. Continuous and discontinuous epitopes were\nlocated by using four different B-cell prediction algorithms. Three-dimensional molecularmodels were generated to more precisely\ncharacterize the predicted antigenicity of LLO.Domain 4 was predicted to contain five of eleven continuous epitopes.Alarge portion\nof domain 4 was also predicted to comprise discontinuous immunogenic epitopes. Domain 4 of LLO may serve as an immunogen\nfor eliciting monoclonal antibodies that can be used to study the pathogenesis of L. monocytogenes as well as develop an inexpensive\nassay....
3D structures of proteins with coordinated Mn2+ ions from bacteria with low, average, and high genomic GC-content have been\nanalyzed (149 PDB files were used).MajorMn2+ binders are aspartic acid (6.82% ofAsp residues), histidine (14.76% ofHis residues),\nand glutamic acid (3.51% of Glu residues).We found out that the motif of secondary structure ââ?¬Å?beta strand-major binder-random\ncoilââ?¬Â is overrepresented around all the three major Mn2+ binders. That motif may be followed by either alpha helix or beta strand.\nBeta strands nearMn2+ binding residues should be stable because they are enriched by such beta formers as valine and isoleucine,\nas well as by specific combinations of hydrophobic and hydrophilic amino acid residues characteristic to beta sheet. In the group of\nproteins from GC-rich bacteria glutamic acid residues situated in alpha helices frequently coordinateMn2+ ions, probably, because\nof the decrease of Lys usage under the influence of mutational GC-pressure. On the other hand, the percentage of Mn2+ sites with\nat least one amino acid in the ââ?¬Å?beta strand-major binder-random coilââ?¬Â motif of secondary structure (77.88%) does not depend on\ngenomic GC-content....
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