Jason Y. Liu

Analysis of protein sequence and interaction data for candidate disease gene prediction

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Conformational changes in redox pairs of protein structures

Disulfides are conventionally viewed as structurally stabilizing elements in proteins but emerging evidence suggests two disulfide subproteomes exist. One group mediates the well known role of structural stabilization. A second redox‐active group are best known for their catalytic functions but are increasingly being recognized for their roles in regulation of protein function. Redox‐active disulfides are, by their very nature, more susceptible to reduction than structural disulfides; and conversely, the Cys pairs that form them are more susceptible to oxidation. In this study, we searched for potentially redox‐active Cys Pairs by scanning the Protein Data Bank for structures of proteins in alternate redox states. The PDB contains over 1134 unique redox pairs of proteins, many of which exhibit conformational differences between alternate redox states. Several classes of structural changes were observed, proteins that exhibit: disulfide oxidation following expulsion of metals such as zinc; major reorganisation of the polypeptide backbone in association with disulfide redox‐activity; order/disorder transitions; and changes in quaternary structure. Based on evidence gathered supporting disulfide redox activity, we propose disulfides present in alternate redox states are likely to have physiologically relevant redox activity.

Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

BackgroundAutomated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies.ResultsUsing a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs.ConclusionThe study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.

Estimating Relative Disulfide Energies: An Accurate Ab Initio Potential Energy Surface

Disulfide torsional energy, a good predictor of disulfide redox potential in proteins, may be estimated by interpolation on a potential energy surface (PES) describing the twisting of diethyl disulfide through its three central dihedral angles. Here we update PES calculations at the M05-2X level of theory with the 6-31G(d) basis set. Although the surface shows no qualitative differences from an earlier MP2(full) PES, energy differences greater than 1 kJ mol–1 were seen for conformations with χ2 between –60° and 30°, or with χ3 below 60° or above 130°. This is particularly significant for highly strained disulfides that are likely to be spontaneously reduced by mechanical means. In benchmarking against the high-level G3X method, M05-2X showed significantly reduced root mean squared deviation compared with MP2(full) (1.0 versus 2.0 kJ mol–1 respectively). Results are incorporated into a web application that calculates relative torsional energies from disulfide dihedral angles (http://www.sbinf.org/applications/pes.html).

Analysis of genome-wide association study data using the protein knowledge base

BackgroundGenome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.ResultsHere we developed and benchmarked several protocols for GWAS data analysis using different in-silico gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system Gentrepid to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without a priori knowledge, by exhaustive comparison of genes in distinct loci. Because Gentrepid uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.ConclusionsResults suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.

Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease.

Current single‐locus‐based analyses and candidate disease gene prediction methodologies used in genome‐wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple‐locus‐based approach, drawing on protein pathway‐ or domain‐based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC‐implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Gentrepid V2.0: a web server for candidate disease gene prediction

BackgroundCandidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required.DescriptionGentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype.ConclusionsThe system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/.

Potential Role of Forbidden Disulfides in Zn Signalling

Expulsion of Zn2+ from proteins following oxidation of ligating Cysteine residues is an emerging area of the oxidative stress response. During a recent data mining survey of protein structures with pairs of thiols in both reduced and oxidized (disulfide bonded) states, we found two structural motifs repeatedly associated with Zn2+ binding (1). Forbidden disulfides are a canonical set of disulfides with abnormal stereochemistry associated with redox-activity. Here we show through systematic analysis of Zinc finger structures and sequences, that one of these motifs is extremely prevalent in Zinc fingers. We show that in around 50% of Zinc finger structures two of the Zn2+-ligating thiols are embedded in a secondary structure similar to an anti-parallel β-diagonal disulfide-like motif (aBDD), located on the β-hairpin structure known as a Zinc knuckle. Formation of a disulfide by thiols of this motif has recently been characterized in the molecular chaperone Hsp33 and also demonstrated in several other transcription factors (2). Although other forbidden disulfide motifs are occasionally present in Zinc fingers, none are as ubiquitous as this aBDD-like motif. We show that the presence of this motif and its position in the structure is characteristic of different types of Zinc fingers, suggesting a functional relationship. As Zinc fingers comprise more than 17% of the human genome, this motif is likely important in Zn2+ signalling.1. Fan SW, George RA, Haworth NL, Feng LL, Liu JY, Wouters MA. Conformational changes in redox pairs of protein structures. Prot. Sci. 18: 1745–1765, 2009.2. Ilbert M, Horst J, Ahrens S, Winter J, Graf PCF, Lilie H, Jakob U. The redox-switch domain of Hsp33 functions as dual stress sensor. Nat. Struct. Mol. Biol. 14: 556–563, 2007.