RESUMO
AIMS/HYPOTHESIS: More than 40 regions of the human genome confer susceptibility for type 1 diabetes and could be used to establish population screening strategies. The aim of our study was to identify weighted sets of SNP combinations for type 1 diabetes prediction. METHODS: We applied multivariable logistic regression and Bayesian feature selection to the Type 1 Diabetes Genetics Consortium (T1DGC) dataset with genotyping of HLA plus 40 SNPs within other type 1 diabetes-associated gene regions in 4,574 cases and 1,207 controls. We tested the weighted models in an independent validation set (765 cases, 423 controls), and assessed their performance in 1,772 prospectively followed children. RESULTS: The inclusion of 40 non-HLA gene SNPs significantly improved the prediction of type 1 diabetes over that provided by HLA alone (p = 3.1 × 10(-25)), with a receiver operating characteristic AUC of 0.87 in the T1DGC set, and 0.84 in the validation set. Feature selection identified HLA plus nine SNPs from the PTPN22, INS, IL2RA, ERBB3, ORMDL3, BACH2, IL27, GLIS3 and RNLS genes that could achieve similar prediction accuracy as the total SNP set. Application of this ten SNP model to prospectively followed children was able to improve risk stratification over that achieved by HLA genotype alone. CONCLUSIONS: We provided a weighted risk model with selected SNPs that could be considered for recruitment of infants into studies of early type 1 diabetes natural history or appropriately safe prevention.
Assuntos
Diabetes Mellitus Tipo 1/diagnóstico , Diabetes Mellitus Tipo 1/genética , Predisposição Genética para Doença , Testes Genéticos/métodos , Polimorfismo de Nucleotídeo Único , Teorema de Bayes , Bases de Dados Genéticas , Genótipo , HumanosRESUMO
We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.
Assuntos
Internet , Modelos Químicos , Modelos Genéticos , Modelos Moleculares , Linguagens de Programação , Proteínas , Software , Sequência de Aminoácidos , Sequência de Bases , Simulação por Computador , Mineração de Dados/métodos , Bases de Dados de Proteínas , Dados de Sequência Molecular , Proteínas/química , Proteínas/genética , Proteínas/ultraestrutura , Análise de Sequência de Proteína/métodos , Relação Estrutura-AtividadeRESUMO
MOTIVATION: Protein sequence searching and alignment are fundamental tools of modern biology. Alignments are assessed using their similarity scores, essentially the sum of substitution matrix scores over all pairs of aligned amino acids. We previously proposed a generative probabilistic method that yields scores that take the sequence context around each aligned residue into account. This method showed drastically improved sensitivity and alignment quality compared with standard substitution matrix-based alignment. RESULTS: Here, we develop an alternative discriminative approach to predict sequence context-specific substitution scores. We applied our approach to compute context-specific sequence profiles for Basic Local Alignment Search Tool (BLAST) and compared the new tool (CS-BLASTdis) to BLAST and the previous context-specific version (CS-BLASTgen). On a dataset filtered to 20% maximum sequence identity, CS-BLASTdisis was 51% more sensitive than BLAST and 17% more sensitive than CS-BLASTgenin, detecting remote homologues at 10% false discovery rate. At 30% maximum sequence identity, its alignments contain 21 and 12% more correct residue pairs than those of BLAST and CS-BLASTgen, respectively. Clear improvements are also seen when the approach is combined with PSI-BLAST and HHblits. We believe the context-specific approach should replace substitution matrices wherever sensitivity and alignment quality are critical.
Assuntos
Substituição de Aminoácidos , Alinhamento de Sequência/métodos , Análise de Sequência de Proteína , Software , Algoritmos , Modelos Estatísticos , ProbabilidadeRESUMO
The MR (Mre11 nuclease and Rad50 ABC ATPase) complex is an evolutionarily conserved sensor for DNA double-strand breaks, highly genotoxic lesions linked to cancer development. MR can recognize and process DNA ends even if they are blocked and misfolded. To reveal its mechanism, we determined the crystal structure of the catalytic head of Thermotoga maritima MR and analyzed ATP-dependent conformational changes. MR adopts an open form with a central Mre11 nuclease dimer and two peripheral Rad50 molecules, a form suited for sensing obstructed breaks. The Mre11 C-terminal helix-loop-helix domain binds Rad50 and attaches flexibly to the nuclease domain, enabling large conformational changes. ATP binding to the two Rad50 subunits induces a rotation of the Mre11 helix-loop-helix and Rad50 coiled-coil domains, creating a clamp conformation with increased DNA-binding activity. The results suggest that MR is an ATP-controlled transient molecular clamp at DNA double-strand breaks.
