Amino acid distributions around O-linked glycosylation sites.
Ontology highlight
ABSTRACT:
SUBMITTER: Wilson IB
PROVIDER: S-EPMC1150083 | biostudies-other | 1991 Apr
REPOSITORIES: biostudies-other
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Project description:BACKGROUND: While occurring enzymatically in biological systems, O-linked glycosylation affects protein folding, localization and trafficking, protein solubility, antigenicity, biological activity, as well as cell-cell interactions on membrane proteins. Catalytic enzymes involve glycotransferases, sugar-transferring enzymes and glycosidases which trim specific monosaccharides from precursors to form intermediate structures. Due to the difficulty of experimental identification, several works have used computational methods to identify glycosylation sites. RESULTS: By investigating glycosylated sites that contain various motifs between Transmembrane (TM) and non-Transmembrane (non-TM) proteins, this work presents a novel method, GlycoRBF, that implements radial basis function (RBF) networks with significant amino acid pairs (SAAPs) for identifying O-linked glycosylated serine and threonine on TM proteins and non-TM proteins. Additionally, a membrane topology is considered for reducing the false positives on glycosylated TM proteins. Based on an evaluation using five-fold cross-validation, the consideration of a membrane topology can reduce 31.4% of the false positives when identifying O-linked glycosylation sites on TM proteins. Via an independent test, GlycoRBF outperforms previous O-linked glycosylation site prediction schemes. CONCLUSION: A case study of Cyclic AMP-dependent transcription factor ATF-6 alpha was presented to demonstrate the effectiveness of GlycoRBF. Web-based GlycoRBF, which can be accessed at http://GlycoRBF.bioinfo.tw, can identify O-linked glycosylated serine and threonine effectively and efficiently. Moreover, the structural topology of Transmembrane (TM) proteins with glycosylation sites is provided to users. The stand-alone version of GlycoRBF is also available for high throughput data analysis.
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Project description:ObjectiveThe current study aimed to understand epidemiological feature and critical factors associated with pathogenesis of circulating recombinant form (CRF) 01_AE strains in Northeast China.DesignCompared analysis was made between CRF01_AE and non-CRF01_AE samples to understand the pathogenicity features of CRF01_AE. Further analyses between CRF01_AE samples with high or low CD4 cell counts and between samples with different coreceptor usages were done to explore the possible factors correlating to the pathogenesis of CRF01_AE viruses.MethodsThe genotypes of newly identified strains were determined by phylogenetic analyses using Mega 6.06. Coreceptor usage was predicted by Geno2Pheno algorithm. Potential N-linked glycosylation site (PNGS) number was calculated using the online N-glycosite software. The properties of amino acid sequences were analyzed by the online ProtParam tool.ResultsCRF01_AE become the main HIV-1 genotype since 2010. Compared with non-CRF01_AE group, the CRF01_AE group showed a higher proportion of samples with CD4 cell count less than 200 cells/μl. Shorter amino acid length, fewer PNGSs and the presence of a basic motif R/KNXT or NR/KT in V4 correlated to a lower CD4 cell count, and existence or coexistence of Thr12, Arg13, Val21 and Lys33, presence of more than 4 of net charges and lack of the PNGS within V3 favored to the X4/R5X4 coreceptor usage of CRF01_AE viruses.ConclusionCRF01_AE has dominated HIV-1 genotype in Northeast China. Infection with CRF01_AE exhibited a fast disease progression, which may be associated with specific amino acid residues and PNGSs in V3 and V4 regions as well as amino acid length of V4 region.
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Project description:BackgroundSince thermodynamic stability is a global property of proteins that has to be conserved during evolution, the selective pressure at a given site of a protein sequence depends on the amino acids present at other sites. However, models of molecular evolution that aim at reconstructing the evolutionary history of macromolecules become computationally intractable if such correlations between sites are explicitly taken into account.ResultsWe introduce an evolutionary model with sites evolving independently under a global constraint on the conservation of structural stability. This model consists of a selection process, which depends on two hydrophobicity parameters that can be computed from protein sequences without any fit, and a mutation process for which we consider various models. It reproduces quantitatively the results of Structurally Constrained Neutral (SCN) simulations of protein evolution in which the stability of the native state is explicitly computed and conserved. We then compare the predicted site-specific amino acid distributions with those sampled from the Protein Data Bank (PDB). The parameters of the mutation model, whose number varies between zero and five, are fitted from the data. The mean correlation coefficient between predicted and observed site-specific amino acid distributions is larger than <r> = 0.70 for a mutation model with no free parameters and no genetic code. In contrast, considering only the mutation process with no selection yields a mean correlation coefficient of <r> = 0.56 with three fitted parameters. The mutation model that best fits the data takes into account increased mutation rate at CpG dinucleotides, yielding <r> = 0.90 with five parameters.ConclusionThe effective selection process that we propose reproduces well amino acid distributions as observed in the protein sequences in the PDB. Its simplicity makes it very promising for likelihood calculations in phylogenetic studies. Interestingly, in this approach the mutation process influences the effective selection process, i.e. selection and mutation must be entangled in order to obtain effectively independent sites. This interdependence between mutation and selection reflects the deep influence that mutation has on the evolutionary process: The bias in the mutation influences the thermodynamic properties of the evolving proteins, in agreement with comparative studies of bacterial proteomes, and it also influences the rate of accepted mutations.
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