Visualization of protein 3D structures in reduced representation with simultaneous display of intra and inter-molecular interactions

Protein structure representation is an important tool in structural biology. There exists different methods of representing the protein 3D structures and different biologists favor different methods based on the information they require. Currently there is no available method of protein 3D structure representation which captures enough chemical information from the protein sequence and clearly shows the intra-molecular and the inter-molecular H-bonds and VDW interactions at the same time. This project aims to reduce the 3D structure of a protein and display the reduced representation along with inter-molecular and the intra-molecular H-bonds and van der Waals interactions. A reduced protein representation has a significantly lower atomicity (i.e., number of the coordinates) than one which is in all-atom representation. In this work, we transform the protein structure from \u27all- atom representation\u27 (AAR) to \u27double-centroid reduced representation\u27 (DCRR), which contains amino acid backbone (N, C[alpha], C\u27, O) and side chain (C[beta] and beyond) centroid coordinates instead of atomic coordinates. Another aim of this project is to develop a visualization interface for the reduced representation. This interface is implemented in MATLAB and displays the protein in DCRR along with its inter-molecular, as well as intra-molecular, interaction. Visually, DCRR is easier to comprehend than AAR. We also developed a Web Server called the Protein DCRR Web Server wherein users can enter the PDB id or upload a modeled protein and get the DCRR of that protein. The back end to the Web Server is a database which has the reduced representation for all the x-ray crystallographic structure in the PDB

Carrier testing for severe childhood recessive diseases by next-generation sequencing.

Of 7028 disorders with suspected Mendelian inheritance, 1139 are recessive and have an established molecular basis. Although individually uncommon, Mendelian diseases collectively account for ~20% of infant mortality and ~10% of pediatric hospitalizations. Preconception screening, together with genetic counseling of carriers, has resulted in remarkable declines in the incidence of several severe recessive diseases including Tay-Sachs disease and cystic fibrosis. However, extension of preconception screening to most severe disease genes has hitherto been impractical. Here, we report a preconception carrier screen for 448 severe recessive childhood diseases. Rather than costly, complete sequencing of the human genome, 7717 regions from 437 target genes were enriched by hybrid capture or microdroplet polymerase chain reaction, sequenced by next-generation sequencing (NGS) to a depth of up to 2.7 gigabases, and assessed with stringent bioinformatic filters. At a resultant 160x average target coverage, 93% of nucleotides had at least 20x coverage, and mutation detection/genotyping had ~95% sensitivity and ~100% specificity for substitution, insertion/deletion, splicing, and gross deletion mutations and single-nucleotide polymorphisms. In 104 unrelated DNA samples, the average genomic carrier burden for severe pediatric recessive mutations was 2.8 and ranged from 0 to 7. The distribution of mutations among sequenced samples appeared random. Twenty-seven percent of mutations cited in the literature were found to be common polymorphisms or misannotated, underscoring the need for better mutation databases as part of a comprehensive carrier testing strategy. Given the magnitude of carrier burden and the lower cost of testing compared to treating these conditions, carrier screening by NGS made available to the general population may be an economical way to reduce the incidence of and ameliorate suffering associated with severe recessive childhood disorders

Data from: Novel conserved genotypes correspond to antibiotic resistance phenotypes of E. coli clinical isolates

Current efforts to understand antibiotic resistance on the whole genome scale tend to focus on known genes even as high throughput sequencing strategies uncover novel mechanisms. To identify genomic variations associated with antibiotic resistance, we employed a modified genome-wide association study; we sequenced genomic DNA from pools of E. coli clinical isolates with similar antibiotic resistance phenotypes using SOLiD technology to uncover SNPs unanimously conserved in each pool. The multidrug-resistant pools were genotypically similar to SMS-3-5, a previously sequenced multidrug-resistant isolate from a polluted environment. The similarity was evenly spread across the entire genome and not limited to plasmid or pathogenicity island loci. Among the pools of clinical isolates, genomic variation was concentrated adjacent to previously reported inversion and duplication differences between the SMS-3-5 isolate and the drug-susceptible laboratory strain, DH10B. Single nucleotide polymorphisms (SNPs) that result in non-synonymous changes in gyrA (encoding the well-known S83L allele associated with fluoroquinolone resistance), mutM, ligB, and recG were unanimously conserved in every fluoroquinolone-resistant pool. Alleles of the latter three genes are tightly linked among most sequenced E. coli genomes, and had not been implicated in antibiotic resistance previously. The changes in these genes map to amino acid positions in alpha helices that are involved in DNA binding. Plasmid- encoded complementation of null strains with either allelic variant of mutM or ligB resulted in variable responses to ultraviolet light or hydrogen peroxide treatment as markers of induced DNA damage, indicating their importance in DNA metabolism and revealing a potential mechanism for fluoroquinolone resistance. Our approach uncovered evidence that additional DNA binding enzymes may contribute to fluoroquinolone resistance and further implicate environmental bacteria as a reservoir for antibiotic resistance

Missense variants in NOX1 and p22phox in a case of very-early-onset inflammatory bowel disease are functionally linked to NOD2

Whole-genome and whole-exome sequencing of individual patients allow the study of rare and potentially causative genetic variation. In this study, we sequenced DNA of a trio comprising a boy with very-early-onset inflammatory bowel disease (veoIBD) and his unaffected parents. We identified a rare, X-linked missense variant in the NAPDH oxidase NOX1 gene (c.C721T, p.R241C) in heterozygous state in the mother and in hemizygous state in the patient. We discovered that, in addition, the patient was homozygous for a common missense variant in the CYBA gene (c.T214C, p.Y72H). CYBA encodes the p22phox protein, a cofactor for NOX1. Functional assays revealed reduced cellular ROS generation and antibacterial capacity of NOX1 and p22phox variants in intestinal epithelial cells. Moreover, the identified NADPH oxidase complex variants affected NOD2-mediated immune responses, and p22phox was identified as a novel NOD2 interactor. In conclusion, we detected missense variants in a veoIBD patient that disrupt the host response to bacterial challenges and reduce protective innate immune signaling via NOD2. We assume that the patient's individual genetic makeup favored disturbed intestinal mucosal barrier function

Raw diBayes SNP calls for clinical isolate pools against three different reference E. coli genomes

The attached zip file contains the raw DiBayes SNP calls for the paper Swick et al, PONE-D-12-36402R1. Novel Conserved Genotypes Correspond to Antibiotic Resistance Phenotypes of E. coli Clinical Isolates. PLOS ONE. It contains 16 folders, each of which follows this structure: $PoolID >$ReferenceGenome > DB > $PoolID >$PoolID_SNP.gff3. The $ReferenceGenome codes correspond to the following NCBI accession numbers: NC_010473 - DH10B, NC_010498 - SMS-3-5, NC_012967 - REL60

Relationship of pool consensus sequences to susceptible genome DH10B and resistant genome SMS-3-5.

<p><b>A)</b> Total numbers of unanimous SNPs in each pool relative to DH10B and SMS-3-5. <b>B)</b> The similarity of each pool to DH10B and SMS-3-5. The logarithm of (unanimous SNPs relative to SMS-3-5/unanimous SNPs relative to DH10B) was plotted for each pool. Blue bars are “S” pools; red bars are “H” pools; gray bars are “M” pools. <b>C)</b> Dot plot of genomic similarity for each pool. The genomic similarity of each pool was calculated relative to each DH10B and SMS-3-5, resulting in a hierarchical clustering of the pools. Blue squares are “S” pools; red squares are “H” pools; black squares are “M” pools.</p