Prediction of enzymes and non-enzymes from protein sequences based on sequence derived features and PSSM matrix using artificial neural network

The problem of predicting the enzymes and non-enzymes from the protein sequence information is still an open problem in bioinformatics. It is further becoming more important as the number of sequenced information grows exponentially over time. We describe a novel approach for predicting the enzymes and non-enzymes from its amino-acid sequence using artificial neural network (ANN). Using 61 sequence derived features alone we have been able to achieve 79 percent correct prediction of enzymes/non-enzymes (in the set of 660 proteins). For the complete set of 61 parameters using 5-fold cross-validated classification, ANN model reveal a superior model (accuracy = 78.79 plus or minus 6.86 percent, Q(pred) = 74.734 plus or minus 17.08 percent, sensitivity = 84.48 plus or minus 6.73 percent, specificity = 77.13 plus or minus 13.39 percent). The second module of ANN is based on PSSM matrix. Using the same 5-fold cross-validation set, this ANN model predicts enzymes/non-enzymes with more accuracy (accuracy = 80.37 plus or minus 6.59 percent, Q(pred) = 67.466 plus or minus 12.41 percent, sensitivity = 0.9070 plus or minus 3.37 percent, specificity = 74.66 plus or minus 7.17 percent).     

Treatment of paper factory effluent using a phenol degrading Alcaligenes sp. under free and immobilized conditions

Treatment of the paper factory effluent was done with free and immobilized cells of a phenol degrading Alcaligenes sp. d(2). The free cells could bring a maximum of 99% reduction in phenol and 40% reduction in chemical oxygen demand (COD) after 32 and 20 h of treatment, respectively. In the case of immobilized cells, a maximum of 99% phenol reduction and 70% COD reduction was attained after 20 h of treatment under batch process. In the continuous mode of operation using packed bed reactor, the strain was able to give 99% phenol removal and 92% COD reduction in 8h of residence time The optimum flow rate was 2.5 ml/h and the half life period was 76 h. Even after the complete removal of phenol, the strain could further enhance reduction in chemical oxygen demand, which clearly indicated that in the paper factory effluent, this strain could also oxidize organic matter other than phenol.     

Comprehensive proteomic mass spectrometric characterization of human cannabinoid CB2 receptor

The CB1 and CB2 cannabinoid receptors belong to the GPCR superfamily and are associated with a variety of physiological and pathophysiological processes. Both receptors, with several lead compounds at different phases of development, are potentially useful targets for drug discovery. For this reason, fully elucidating the structural features of these membrane-associated proteins would be extremely valuable in designing more selective, novel therapeutic drug molecules. As a first step toward obtaining information on the structural features of the drug-receptor complex, we describe the full mass spectrometric (MS) analysis of the recombinant human cannabinoid CB2 receptor. This first complete proteomic characterization of a GPCR protein beyond rhodopsin was accomplished by a combination of several LC/MS approaches involving nanocapillary liquid chromatography, coupled with either a quadrupole-linear ion trap or linear ion trap-FTICR mass spectrometer. The CB2 receptor, with incorporated N-terminal FLAG and C-terminal HIS6 epitope tags, was functionally expressed in baculovirus cells and purified using a single step of anti-FLAG M2 affinity chromatography. To overcome the difficulties involved with in-gel digestion, due to the highly hydrophobic nature of this membrane-associated protein, we conducted in-solution trypsin and chymotrypsin digestions of purified and desalted samples in the presence of a low concentration of CYMAL5. This was followed by nanoLC peptide separation and analysis using a nanospray ESI source operated in the positive mode. The results can be reported confidently, based on the overlapping sequence data obtained using the highly mass accurate LTQ-FT and the 4000 Q-Trap mass spectrometers. Both instruments gave very similar patterns of identified peptides, with full coverage of all transmembrane helices, resulting in the complete characterization of the cannabinoid CB2 receptor. Mass spectrometric identification of all amino acid residues in the cannabinoid CB2 receptor is a key step toward the "Ligand Based Structural Biology" approach developed in our laboratory for characterizing ligand binding sites in GPCRs using a variety of covalent cannabinergic ligands.     

Flap Endonuclease 1 Limits Telomere Fragility on the Leading Strand

The existence of redundant replication and repair systems that ensure genome stability underscores the importance of faithful DNA replication. Nowhere is this complexity more evident than in challenging DNA templates, including highly repetitive or transcribed sequences. Here, we demonstrate that flap endonuclease 1 (FEN1), a canonical lagging strand DNA replication protein, is required for normal, complete leading strand replication at telomeres. We find that the loss of FEN1 nuclease activity, but not DNA repair activities, results in leading strand-specific telomere fragility. Furthermore, we show that FEN1 depletion-induced telomere fragility is increased by RNA polymerase II inhibition and is rescued by ectopic RNase H1 expression. These data suggest that FEN1 limits leading strand-specific telomere fragility by processing RNA:DNA hybrid/flap intermediates that arise from co-directional collisions occurring between the replisome and RNA polymerase. Our data reveal the first molecular mechanism for leading strand-specific telomere fragility and the first known role for FEN1 in leading strand DNA replication. Because FEN1 mutations have been identified in human cancers, our findings raise the possibility that unresolved RNA:DNA hybrid structures contribute to the genomic instability associated with cancer.     

PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors

Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0602-8) contains supplementary material, which is available to authorized users.