Diversity in the intrinsic apoptosis pathway of nematodes.

Early studies of the free-living nematode C. elegans informed us how BCL-2-regulated apoptosis in humans is regulated. However, subsequent studies showed C. elegans apoptosis has several unique features compared with human apoptosis. To date, there has been no detailed analysis of apoptosis regulators in nematodes other than C. elegans. Here, we discovered BCL-2 orthologues in 89 free-living and parasitic nematode taxa representing four evolutionary clades (I, III, IV and V). Unlike in C. elegans, 15 species possess multiple (two to five) BCL-2-like proteins, and some do not have any recognisable BCL-2 sequences. Functional studies provided no evidence that BAX/BAK proteins have evolved in nematodes, and structural studies of a BCL-2 protein from the basal clade I revealed it lacks a functionally important feature of the C. elegans orthologue. Clade I CED-4/APAF-1 proteins also possess WD40-repeat sequences associated with apoptosome assembly, not present in C. elegans, or other nematode taxa studied.

Strategies to enable large-scale proteomics for reproducible research.

Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.

Regulation through MicroRNAs in Response to Low-Energy N+ Ion Irradiation in Oryza sativa.

MicroRNAs (miRNAs) are a non-coding regulatory RNAs that play significant roles in plant growth and development, especially in the stress response. Low-energy ion radiation, a type of environmental stress, can cause multiple biological effects. To understand the roles of miRNAs in response to low-energy N+ ion radiation in Oryza sativa, high-throughput sequencing of small RNAs was carried out to detect the expression of miRNAs in the shoots of the rice after 2 × 1017 N+/cm2 irradiation. The differentially expressed 28 known miRNAs were identified, 17 of these identified miRNAs were validated by real-time quantitative fluorescent PCR (q-PCR), including 9 up-regulated miRNAs (miR1320-3p, miR1320-5p, miR156b-3p, miR156c-5p, miR156c-3p/g-3p, miR1561-5p, miR398b and miR6250) and 8 down-regulated miRNAs (miR156a/e/i, miR156k, miR160f-5p, miR166j-5p, miR1846e and miR399d). In addition, 45 novel radiationresponsive miRNAs were predicted, and 8 of them were verified by q-PCR. The target genes of radiation-responsive miRNAs were predicted and gene function enrichment analysis was performed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of 9 targets of 4 known miRNA families (miR156, miR399, miR1320 and miR398) and 2 targets of 2 novel miRNAs were quantified by q-PCR, and a strong negative regulation relation between miRNAs and their targets were observed. Those targets including SQUAMOSA promoterbinding-like protein (SPL) genes, copper/zinc superoxide dismutase (Cu/Zn-SOD), copper chaperone for SOD (CCS1) and electron transporter/ heat-shock protein binding protein (HSP), which are involved in growth and defense against various stresses, especially associated with reactive oxygen species (ROS) scavenging. This work provides important information for understanding the ROS generation and elimination mechanisms closely related to miRNAs in rice seedlings after low-energy N+ radiation exposure.

Cross-strand disulfides in the hydrogen bonding site of antiparallel ß-sheet (aCSDhs): Forbidden disulfides that are highly strained, easily broken.

Some disulfide bonds perform important structural roles in proteins, but another group has functional roles via redox reactions. Forbidden disulfides are stressed disulfides found in recognizable protein contexts, which currently constitute more than 10% of all disulfides in the PDB. They likely have functional redox roles and constitute a major subset of all redox-active disulfides. The torsional strain of forbidden disulfides is typically higher than for structural disulfides, but not so high as to render them immediately susceptible to reduction under physionormal conditions. Previously we characterized the most abundant forbidden disulfide in the Protein Data Bank, the aCSDn: a canonical motif in which disulfide-bonded cysteine residues are positioned directly opposite each other on adjacent anti-parallel ß-strands such that the backbone hydrogen-bonded moieties are directed away from each other. Here we perform a similar analysis for the aCSDh, a less common motif in which the opposed cysteine residues are backbone hydrogen bonded. Oxidation of two Cys in this context places significant strain on the protein system, with the ß-chains tilting toward each other to allow disulfide formation. Only left-handed aCSDh conformations are compatible with the inherent right-handed twist of ß-sheets. aCSDhs tend to be more highly strained than aCSDns, particularly when both hydrogen bonds are formed. We discuss characterized roles of aCSDh motifs in proteins of the dataset, which include catalytic disulfides in ribonucleotide reductase and ahpC peroxidase as well as a redox-active disulfide in P1 lysozyme, involved in a major conformation change. The dataset also includes many binding proteins.

Interleukin 33 Signaling Restrains Sporadic Colon Cancer in an Interferon-γ-Dependent Manner.

Interleukin 33 (IL33) is an inflammatory cytokine released during necrotic cell death. The epithelium and stroma of the intestine express large amounts of IL33 and its receptor St2. IL33 is therefore continuously released during homeostatic turnover of the intestinal mucosa. Although IL33 can prevent colon cancer associated with inflammatory colitis, the contribution of IL33 signaling to sporadic colon cancer remains unknown. Here, we utilized a mouse model of sporadic colon cancer to investigate the contribution of IL33 signaling to tumorigenesis in the absence of preexisting inflammation. We demonstrated that genetic ablation of St2 enhanced colon tumor development. Conversely, administration of recombinant IL33 reduced growth of colon cancer cell allografts. In reciprocal bone marrow chimeras, the concurrent loss of IL33 signaling within radioresistant nonhematopoietic, and the radiosensitive hematopoietic, compartments was associated with increased tumor burden. We detected St2 expression within the radioresistant mesenchymal cell compartment of the colon whose stimulation with IL33 induced expression of bona fide NF-κB target genes. Mechanistically, we discovered that St2 deficiency within the nonhematopoietic compartment coincided with increased abundance of regulatory T cells and suppression of an IFNγ gene expression signature, whereas IL33 administration triggered IFNγ expression by tumor allograft-infiltrating T cells. The decrease of this IFNγ gene expression signature was associated with more aggressive disease in human colon cancer patients, suggesting that lack of IL33 signaling impaired the generation of a potent IFNγ-mediated antitumor immune response. Collectively, our data reveal that IL33 functions as a tumor suppressor in sporadic colon cancer. Cancer Immunol Res; 6(4); 409-21. ©2018 AACR.

Mapping genotype-phenotype associations of nsSNPs in coiled-coil oligomerization domains of the human proteome.

We assessed the impact of disease mutations (DMs) versus polymorphisms (PYs) in coiled-coil (CC) domains in UniProt by modeling the structural and functional impact of variants in silico with the CC prediction program Multicoil. The structural impact of variants was evaluated with respect to three main metrics: the oligomerization score-to determine whether the variant is stabilizing or destabilizing-the oligomerization state, and the register-specific score. The functional impact was queried indirectly in several ways. First, we examined marginally stable CCs that were either stabilized or destabilized by the variant. Second, we looked for variants that altered the register of the wild-type CC near wild-type irregularities of likely functional importance, such as skips and stammers. Third, we searched for variants that altered the oligomerization state of the CC. DMs tended to be more destabilizing than PYs; but interestingly, PYs were more frequently associated with predicted changes in the oligomerization state. The functional impact was also queried by testing the association of CC variants with multiple phenotypes, that is, pleiotropy. Mutations in CC regions of proteins cause 155 different phenotypes and are more frequently associated with pleiotropy than proteins in general. Importantly, the CC region itself often encodes the pleiotropy.

NKX2-5 mutations causative for congenital heart disease retain functionality and are directed to hundreds of targets.

We take a functional genomics approach to congenital heart disease mechanism. We used DamID to establish a robust set of target genes for NKX2-5 wild type and disease associated NKX2-5 mutations to model loss-of-function in gene regulatory networks. NKX2-5 mutants, including those with a crippled homeodomain, bound hundreds of targets including NKX2-5 wild type targets and a unique set of "off-targets", and retained partial functionality. NKXΔHD, which lacks the homeodomain completely, could heterodimerize with NKX2-5 wild type and its cofactors, including E26 transformation-specific (ETS) family members, through a tyrosine-rich homophilic interaction domain (YRD). Off-targets of NKX2-5 mutants, but not those of an NKX2-5 YRD mutant, showed overrepresentation of ETS binding sites and were occupied by ETS proteins, as determined by DamID. Analysis of kernel transcription factor and ETS targets show that ETS proteins are highly embedded within the cardiac gene regulatory network. Our study reveals binding and activities of NKX2-5 mutations on WT target and off-targets, guided by interactions with their normal cardiac and general cofactors, and suggest a novel type of gain-of-function in congenital heart disease.

Novel therapeutics for coronary artery disease from genome-wide association study data.

BACKGROUND: Coronary artery disease (CAD), one of the leading causes of death globally, is influenced by both environmental and genetic risk factors. Gene-centric genome-wide association studies (GWAS) involving cases and controls have been remarkably successful in identifying genetic loci contributing to CAD. Modern in silico platforms, such as candidate gene prediction tools, permit a systematic analysis of GWAS data to identify candidate genes for complex diseases like CAD. Subsequent integration of drug-target data from drug databases with the predicted candidate genes can potentially identify novel therapeutics suitable for repositioning towards treatment of CAD. METHODS: Previously, we were able to predict 264 candidate genes and 104 potential therapeutic targets for CAD using Gentrepid (http://www.gentrepid.org), a candidate gene prediction platform with two bioinformatic modules to reanalyze Wellcome Trust Case-Control Consortium GWAS data. In an expanded study, using five bioinformatic modules on the same data, Gentrepid predicted 647 candidate genes and successfully replicated 55% of the candidate genes identified by the more powerful CARDIoGRAMplusC4D consortium meta-analysis. Hence, Gentrepid was capable of enhancing lower quality genotype-phenotype data, using an independent knowledgebase of existing biological data. Here, we used our methodology to integrate drug data from three drug databases: the Therapeutic Target Database, PharmGKB and Drug Bank, with the 647 candidate gene predictions from Gentrepid. We utilized known CAD targets, the scientific literature, existing drug data and the CARDIoGRAMplusC4D meta-analysis study as benchmarks to validate Gentrepid predictions for CAD. RESULTS: Our analysis identified a total of 184 predicted candidate genes as novel therapeutic targets for CAD, and 981 novel therapeutics feasible for repositioning in clinical trials towards treatment of CAD. The benchmarks based on known CAD targets and the scientific literature showed that our results were significant (p < 0.05). CONCLUSIONS: We have demonstrated that available drugs may potentially be repositioned as novel therapeutics for the treatment of CAD. Drug repositioning can save valuable time and money spent on preclinical and phase I clinical studies.

Potential role of glutathione in evolution of thiol-based redox signaling sites in proteins.

Cysteine is susceptible to a variety of modifications by reactive oxygen and nitrogen oxide species, including glutathionylation; and when two cysteines are involved, disulfide formation. Glutathione-cysteine adducts may be removed from proteins by glutaredoxin, whereas disulfides may be reduced by thioredoxin. Glutaredoxin is homologous to the disulfide-reducing thioredoxin and shares similar binding modes of the protein substrate. The evolution of these systems is not well characterized. When a single Cys is present in a protein, conjugation of the redox buffer glutathione may induce conformational changes, resulting in a simple redox switch that effects a signaling cascade. If a second cysteine is introduced into the sequence, the potential for disulfide formation exists. In favorable protein contexts, a bistable redox switch may be formed. Because of glutaredoxin's similarities to thioredoxin, the mutated protein may be immediately exapted into the thioredoxin-dependent redox cycle upon addition of the second cysteine. Here we searched for examples of protein substrates where the number of redox-active cysteine residues has changed throughout evolution. We focused on cross-strand disulfides (CSDs), the most common type of forbidden disulfide. We searched for proteins where the CSD is present, absent and also found as a single cysteine in protein orthologs. Three different proteins were selected for detailed study-CD4, ERO1, and AKT. We created phylogenetic trees, examining when the CSD residues were mutated during protein evolution. We posit that the primordial cysteine is likely to be the cysteine of the CSD which undergoes nucleophilic attack by thioredoxin. Thus, a redox-active disulfide may be introduced into a protein structure by stepwise mutation of two residues in the native sequence to Cys. By extension, evolutionary acquisition of structural disulfides in proteins can potentially occur via transition through a redox-active disulfide state.

Identification of novel therapeutics for complex diseases from genome-wide association data.

BACKGROUND: Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials. METHODS: We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level. RESULTS: Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets. CONCLUSIONS: By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments.

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.

BACKGROUND: Candidate 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. DESCRIPTION: Gentrepid 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. CONCLUSIONS: The 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/.

Epistatic effects of potassium channel variation on cardiac repolarization and atrial fibrillation risk.

OBJECTIVES: The aim of this study was to evaluate the role of cardiac K(+) channel gene variants in families with atrial fibrillation (AF). BACKGROUND: The K(+) channels play a major role in atrial repolarization but single mutations in cardiac K(+) channel genes are infrequently present in AF families. The collective effect of background K(+) channel variants of varying prevalence and effect size on the atrial substrate for AF is largely unexplored. METHODS: Genes encoding the major cardiac K(+) channels were resequenced in 80 AF probands. Nonsynonymous coding sequence variants identified in AF probands were evaluated in 240 control subjects. Novel variants were characterized using patch-clamp techniques and in silico modeling was performed using the Courtemanche atrial cell model. RESULTS: Nineteen nonsynonymous variants in 9 genes were found, including 11 rare variants. Rare variants were more frequent in AF probands (18.8% vs. 4.2%, p < 0.001), and the mean number of variants was greater (0.21 vs. 0.04, p < 0.001). The majority of K(+) channel variants individually had modest functional effects. Modeling simulations to evaluate combinations of K(+) channel variants of varying population frequency indicated that simultaneous small perturbations of multiple current densities had nonlinear interactions and could result in substantial (>30 ms) shortening or lengthening of action potential duration as well as increased dispersion of repolarization. CONCLUSIONS: Families with AF show an excess of rare functional K(+) channel gene variants of varying phenotypic effect size that may contribute to an atrial arrhythmogenic substrate. Atrial cell modeling is a useful tool to assess epistatic interactions between multiple variants.

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

BACKGROUND: Genome-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. RESULTS: Here 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. CONCLUSIONS: Results 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.

Web tools for the prioritization of candidate disease genes.

Despite increasing sequencing capacity, genetic disease investigation still frequently results in the identification of loci containing multiple candidate disease genes that need to be tested for involvement in the disease. This process can be expedited by prioritizing the candidates prior to testing. Over the last decade, a large number of computational methods and tools have been developed to assist the clinical geneticist in prioritizing candidate disease genes. In this chapter, we give an overview of computational tools that can be used for this purpose, all of which are freely available over the web.

Thiol-based redox signalling: rust never sleeps.

Cysteine residues in proteins are covalently modified under conditions of oxidative and nitrosative stress by oxidation, nitrosation, glutathionylation and disulfide formation. Modifications induce conformational changes in substrate proteins, effecting signal cascades that evoke a biological response. A growing number of structures with modified cysteines are allowing a piecemeal understanding of the mechanistic aspects of these signalling pathways to emerge. Conformational changes upon conjugation of nitric oxide and glutathione are generally small and often accompanied by a local increase in protein disorder. Burial of nitric oxide is also apparent, which may increase the timeframe of signalling. Conformational changes upon disulfide formation/reduction range from the small to the spectacular. They include order/disorder transitions; oxidation of disulfides following expulsion of metals such as Zn; major reorganisation or "morphing" of portions of the polypeptide backbone; and changes in quaternary structure including domain swapping.

Cell cycle sensing of oxidative stress in Saccharomyces cerevisiae by oxidation of a specific cysteine residue in the transcription factor Swi6p.

Yeast cells begin to bud and enter the S phase when growth conditions are favorable during the G(1) phase. When subjected to some oxidative stresses, cells delay entry at G(1), allowing repair of cellular damage. Hence, oxidative stress sensing is coordinated with the regulation of cell cycle. We identified a novel function of the cell cycle regulator of Saccharomyces cerevisiae, Swi6p, as a redox sensor through its cysteine residue at position 404. When alanine was substituted at this position, the resultant mutant, C404A, was sensitive to several reactive oxygen species and oxidants including linoleic acid hydroperoxide, the superoxide anion, and diamide. This mutant lost the ability to arrest in G(1) phase upon treatment with lipid hydroperoxide. The Cys-404 residue of Swi6p in wild-type cells was oxidized to a sulfenic acid when cells were subjected to linoleic acid hydroperoxide. Mutation of Cys-404 to Ala abolished the down-regulation of expression of the G(1) cyclin genes CLN1, CLN2, PCL1, and PCL2 that occurred when cells of the wild type were exposed to the lipid hydroperoxide. In conclusion, oxidative stress signaling for cell cycle regulation occurs through oxidation of the G(1)/S-specific transcription factor Swi6p and consequently leads to suppression of the expression of G(1) cyclins and a delay in cells entering the cell cycle.

Two novel missense mutations in HAIRY-AND-ENHANCER-OF-SPLIT-7 in a family with spondylocostal dysostosis.

Spondylocostal dysostosis (SCD) is an inherited disorder with abnormal vertebral segmentation that results in extensive hemivertebrae, truncal shortening and abnormally aligned ribs. It arises during embryonic development by a disruption of formation of somites (the precursor tissue of the vertebrae, ribs and associated tendons and muscles). Four genes causing a subset of autosomal recessive forms of this disease have been identified: DLL3 (SCDO1: MIM 277300), MESP2 (SCDO2: MIM 608681), LFNG (SCDO3: MIM609813) and HES7 (SCDO4). These genes are all essential components of the Notch signalling pathway, which has multiple roles in development and disease. Previously, only a single SCD-causative missense mutation was described in HES7. In this study, we have identified two new missense mutations in the HES7 gene in a single family, with only individuals carrying both mutant alleles being affected by SCD. In vitro functional analysis revealed that one of the mutant HES7 proteins was unable to repress gene expression by DNA binding or protein heterodimerization.

Disulfides as redox switches: from molecular mechanisms to functional significance.

The molecular mechanisms underlying thiol-based redox control are poorly defined. Disulfide bonds between Cys residues are commonly thought to confer extra rigidity and stability to their resident protein, forming a type of proteinaceous spot weld. Redox biologists have been redefining the role of disulfides over the last 30-40 years. Disulfides are now known to form in the cytosol under conditions of oxidative stress. Isomerization of extracellular disulfides is also emerging as an important regulator of protein function. The current paradigm is that the disulfide proteome consists of two subproteomes: a structural group and a redox-sensitive group. The redox-sensitive group is less stable and often associated with regions of stress in protein structures. Some characterized redox-active disulfides are the helical CXXC motif, often associated with thioredoxin-fold proteins; and forbidden disulfides, a group of metastable disulfides that disobey elucidated rules of protein stereochemistry. Here we discuss the role of redox-active disulfides as switches in proteins.

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.