Investigation of genetic determinants of cognitive change in later life.

Cognitive decline is a major health concern and identification of genes that may serve as drug targets to slow decline is important to adequately support an aging population. Whilst genetic studies of cross-sectional cognition have been carried out, cognitive change is less well-understood. Here, using data from the TOMMORROW trial, we investigate genetic associations with cognitive change in a cognitively normal older cohort. We conducted a genome-wide association study of trajectories of repeated cognitive measures (using generalised estimating equation (GEE) modelling) and tested associations with polygenic risk scores (PRS) of potential risk factors. We identified two genetic variants associated with change in attention domain scores, rs534221751 (p = 1 × 10-8 with slope 1) and rs34743896 (p = 5 × 10-10 with slope 2), implicating NCAM2 and CRIPT/ATP6V1E2 genes, respectively. We also found evidence for the association between an education PRS and baseline cognition (at >65 years of age), particularly in the language domain. We demonstrate the feasibility of conducting GWAS of cognitive change using GEE modelling and our results suggest that there may be novel genetic associations for cognitive change that have not previously been associated with cross-sectional cognition. We also show the importance of the education PRS on cognition much later in life. These findings warrant further investigation and demonstrate the potential value of using trial data and trajectory modelling to identify genetic variants associated with cognitive change.

Plasma proteomic associations with genetics and health in the UK Biobank.

The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.

Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19.

As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank (p-values as low as 3.96e-9, all with R2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of boosted COVID-19 polygenic models based on new data and tools to aid risk-prediction.

Mapping of UK Biobank clinical codes: Challenges and possible solutions.

OBJECTIVE: The UK Biobank provides a rich collection of longitudinal clinical data coming from different healthcare providers and sources in England, Wales, and Scotland. Although extremely valuable and available to a wide research community, the heterogeneous dataset contains inconsistent medical terminology that is either aligned to several ontologies within the same category or unprocessed. To make these data useful to a research community, data cleaning, curation, and standardization are needed. Significant efforts to perform data reformatting, mapping to any selected ontologies (such as SNOMED-CT) and harmonization are required from any data user to integrate UK Biobank hospital inpatient and self-reported data, data from various registers with primary care (GP) data. The integrated clinical data would provide a more comprehensive picture of one's medical history. MATERIALS AND METHODS: We evaluated several approaches to map GP clinical Read codes to International Classification of Diseases (ICD) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) terminologies. The results were compared, mapping inconsistencies were flagged, a quality category was assigned to each mapping to evaluate overall mapping quality. RESULTS: We propose a curation and data integration pipeline for harmonizing diagnosis. We also report challenges identified in mapping Read codes from UK Biobank GP tables to ICD and SNOMED CT. DISCUSSION AND CONCLUSION: Some of the challenges-the lack of precise one-to-one mapping between ontologies or the need for additional ontology to fully map terms-are general reflecting trade-offs to be made at different steps. Other challenges are due to automatic mapping and can be overcome by leveraging existing mappings, supplemented with automated and manual curation.

Genetically-informed prediction of short-term Parkinson's disease progression.

Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank.

The UK Biobank Exome Sequencing Consortium (UKB-ESC) is a private-public partnership between the UK Biobank (UKB) and eight biopharmaceutical companies that will complete the sequencing of exomes for all ~500,000 UKB participants. Here, we describe the early results from ~200,000 UKB participants and the features of this project that enabled its success. The biopharmaceutical industry has increasingly used human genetics to improve success in drug discovery. Recognizing the need for large-scale human genetics data, as well as the unique value of the data access and contribution terms of the UKB, the UKB-ESC was formed. As a result, exome data from 200,643 UKB enrollees are now available. These data include ~10 million exonic variants-a rich resource of rare coding variation that is particularly valuable for drug discovery. The UKB-ESC precompetitive collaboration has further strengthened academic and industry ties and has provided teams with an opportunity to interact with and learn from the wider research community.

Pan-ancestry exome-wide association analyses of COVID-19 outcomes in 586,157 individuals.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a respiratory illness that can result in hospitalization or death. We used exome sequence data to investigate associations between rare genetic variants and seven COVID-19 outcomes in 586,157 individuals, including 20,952 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome wide or when specifically focusing on (1) 13 interferon pathway genes in which rare deleterious variants have been reported in individuals with severe COVID-19, (2) 281 genes located in susceptibility loci identified by the COVID-19 Host Genetics Initiative, or (3) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, and results are publicly available through the Regeneron Genetics Center COVID-19 Results Browser.

NDEx, the Network Data Exchange.

Networks are a powerful and flexible methodology for expressing biological knowledge for computation and communication. Network-encoded information can include systematic screens for molecular interactions, biological relationships curated from literature, and outputs from analysis of Big Data. NDEx, the Network Data Exchange (www.ndexbio.org), is an online commons where scientists can upload, share, and publicly distribute networks. Networks in NDEx receive globally unique accession IDs and can be stored for private use, shared in pre-publication collaboration, or released for public access. Standard and novel data formats are accommodated in a flexible storage model. Organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx helps expand the role of networks in scientific discourse and facilitates the integration of networks as data in publications. It is a step towards an ecosystem in which networks bearing data, hypotheses, and findings flow easily between scientists.

Effective knowledge management in translational medicine.

BACKGROUND: The growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health. METHODS: The pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern. RESULTS: The system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface. CONCLUSIONS: The implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs.

Robust multi-tissue gene panel for cancer detection.

BACKGROUND: We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types. METHODS: Frozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA) and Asterand (Detroit, MI). Biotinylated targets were prepared using published methods (Affymetrix, CA) and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA). Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models. RESULTS: We validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets. CONCLUSIONS: Our result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays. Apart from possible use in diagnosis of early tumorigenesis, some other potential uses of our methodology and gene panel would be in assisting pathologists in diagnosis of pre-cancerous lesions, determining tumor boundaries, assessing levels of contamination in cell populations in vitro and identifying transformations in cell cultures after multiple passages. Moreover, based on the robustness of this gene panel in identifying normal vs. tumor, mislabelled or misinterpreted samples can be pinpointed with high confidence.

ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data.

We have created a stand-alone software tool, ConsensusCluster, for the analysis of high-dimensional single nucleotide polymorphism (SNP) and gene expression microarray data. Our software implements the consensus clustering algorithm and principal component analysis to stratify the data into a given number of robust clusters. The robustness is achieved by combining clustering results from data and sample resampling as well as by averaging over various algorithms and parameter settings to achieve accurate, stable clustering results. We have implemented several different clustering algorithms in the software, including K-Means, Partition Around Medoids, Self-Organizing Map, and Hierarchical clustering methods. After clustering the data, ConsensusCluster generates a consensus matrix heatmap to give a useful visual representation of cluster membership, and automatically generates a log of selected features that distinguish each pair of clusters. ConsensusCluster gives more robust and more reliable clusters than common software packages and, therefore, is a powerful unsupervised learning tool that finds hidden patterns in data that might shed light on its biological interpretation. This software is free and available from http://code.google.com/p/consensus-cluster .

Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (<7) and high (≥7) Gleason grade tumors. A comparison of their major hubs with those of the network for normal samples identified two types of changes associated with disease: (i) Some hub genes increased their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with gain of regulatory control in cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily be extended to identify and study networks associated with any two phenotypes.

The human orthologue of murine Mpzl3 with predicted adhesive and immune functions is a potential candidate gene for immune-related hereditary hair loss.

We have recently reported a mutation within the conserved immunoglobulin V-type domain of the predicted adhesion protein Mpzl3 (MIM 611707) in rough coat (rc) mice with severe skin abnormalities and progressive cyclic hair loss. In this study, we tested the hypothesis that the human orthologue MPZL3 on chromosome 11q23.3 is a candidate for similar symptoms in humans. The predicted conserved MPZL3 protein has two transmembrane motifs flanking an extracellular Ig-like domain. The R100Q rc mutation is within the Ig-domain recognition loop that has roles in T-cell receptors and cell adhesion. Results of the rc mouse study, 3D structure predictions, homology with Myelin Protein Zero and EVA1, comprehensive database analyses of polymorphisms and mutations within the human MPZL3 gene and its cell, tissue expression and immunostaining pattern indicate that homozygous or compound heterozygous mutations of MPZL3 might be involved in immune-mediated human hereditary disorders with hair loss.

Anti-calmodulins and tricyclic adjuvants in pain therapy block the TRPV1 channel.

Ca(2+)-loaded calmodulin normally inhibits multiple Ca(2+)-channels upon dangerous elevation of intracellular Ca(2+) and protects cells from Ca(2+)-cytotoxicity, so blocking of calmodulin should theoretically lead to uncontrolled elevation of intracellular Ca(2+). Paradoxically, classical anti-psychotic, anti-calmodulin drugs were noted here to inhibit Ca(2+)-uptake via the vanilloid inducible Ca(2+)-channel/inflamatory pain receptor 1 (TRPV1), which suggests that calmodulin inhibitors may block pore formation and Ca(2+) entry. Functional assays on TRPV1 expressing cells support direct, dose-dependent inhibition of vanilloid-induced (45)Ca(2+)-uptake at microM concentrations: calmidazolium (broad range) > or = trifluoperazine (narrow range) chlorpromazine/amitriptyline>fluphenazine>>W-7 and W-13 (only partially). Most likely a short acidic domain at the pore loop of the channel orifice functions as binding site either for Ca(2+) or anti-calmodulin drugs. Camstatin, a selective peptide blocker of calmodulin, inhibits vanilloid-induced Ca(2+)-uptake in intact TRPV1(+) cells, and suggests an extracellular site of inhibition. TRPV1(+), inflammatory pain-conferring nociceptive neurons from sensory ganglia, were blocked by various anti-psychotic and anti-calmodulin drugs. Among them, calmidazolium, the most effective calmodulin agonist, blocked Ca(2+)-entry by a non-competitive kinetics, affecting the TRPV1 at a different site than the vanilloid binding pocket. Data suggest that various calmodulin antagonists dock to an extracellular site, not found in other Ca(2+)-channels. Calmodulin antagonist-evoked inhibition of TRPV1 and NMDA receptors/Ca(2+)-channels was validated by microiontophoresis of calmidazolium to laminectomised rat monitored with extracellular single unit recordings in vivo. These unexpected findings may explain empirically noted efficacy of clinical pain adjuvant therapy that justify efforts to develop hits into painkillers, selective to sensory Ca(2+)-channels but not affecting motoneurons.

Three-dimensional models of histamine H3 receptor antagonist complexes and their pharmacophore.

Molecular modeling was used to analyze the binding mode and activities of histamine H3 receptor antagonists. A model of the H3 receptor was constructed through homology modeling methods based on the crystal structure of bovine rhodopsin. Known H3 antagonists were interactively docked into the putative antagonist binding pocket and the resultant model was subjected to molecular mechanics energy minimization and molecular dynamics simulations which included a continuum model of the lipid bilayer and intra- and extracellular aqueous environments surrounding the transmembrane helices. The transmembrane helices stayed well embedded in the dielectric slab representing the lipid bilayer and the intra- and extracellular loops remain situated in the aqueous solvent region of the model during molecular dynamics simulations of up to 200 ps in duration. A pharmacophore model was calculated by mapping the features common to three active compounds three-dimensionally in space. The 3D pharmacophore model complements our atomistic receptor/ligand modeling. The H3 antagonist pharmacophore consists of two protonation sites (i.e. basic centers) connected by a central aromatic ring or hydrophobic region. These two basic sites can simultaneously interact with Asp 114 (3.32) in helix III and a Glu 206 (5.46) in helix V which are believed to be the key residues that histamine interacts with to stabilize the receptor in the active state. The interaction with Glu 206 is consistent with the enhanced activity resulting from the additional basic site. In addition to these two salt bridging interactions, the central region of these antagonists contains a lipophilic group, usually an aromatic ring, that is found to interact with several nearby hydrophobic side chains. The picture of antagonist binding provided by these models is consistent with earlier pharmacophore models for H3 antagonists with some exceptions.

PNU-91325 increases fatty acid synthesis from glucose and mitochondrial long chain fatty acid degradation: a comparative tracer-based metabolomics study with rosiglitazone and pioglitazone in HepG2 cells.

The mitochondrial membrane protein termed "mitoNEET," is a putative secondary target for insulin-sensitizing thiazolidinedione (TZD) compounds but its role in regulating metabolic flux is not known. PNU-91325 is a thiazolidinedione derivative which exhibits high binding affinity to mitoNEET and lowers cholesterol, fatty acid and blood glucose levels in animal models. In this study we report the stable isotope-based dynamic metabolic profiles (SIDMAP) of rosiglitazone, pioglitazone and PNU-91325 in a dose-matching, dose-escalating study. One and 10 µM concentrations 1 and 10 µM drug concentrations were introduced into HepG2 cells in the presence of either [1,2-13C2]-D-glucose or [U-13C18]stearate, GC/MS used to determine positional tracer incorporation (mass isotopomer analysis) into multiple metabolites produced by the Krebs and pentose cycles, de novo fatty acid synthesis, long chain fatty acid oxidation, chain shortening and elongation. Rosiglitazone and pioglitazone (10 µM) increased pentose synthesis from [U-13C18]stearate by 127% and 185%, respectively, while PNU-91325 rather increased glutamate synthesis in the Krebs cycle by 113% as compared to control vehicle treated cells. PNU-91325 also increased stearate chain shortening into palmitate by 59%. Glucose tracer-derived de novo palmitate and stearate synthesis were increased by 1 and 10 µM rosiglitazone by 41% and 83%, respectively, and by 63% and 75% by PNU-91325. Stearate uptake was also increased by 10 µM PNU-91325 by 15.8%. We conclude that the entry of acetyl Co-A derived from long-chain fatty acid ß-oxidation into the mitochondria is facilitated by the mitoNEET ligand PNU-91325, which increases glucose-derived long chain fatty acid synthesis and breakdown via ß-oxidation and anaplerosis in the mitochondria.

A hidden Markov model with molecular mechanics energy-scoring function for transmembrane helix prediction.

A range of methods has been developed to predict transmembrane helices and their topologies. Although most of these algorithms give good predictions, no single method consistently outperforms the others. However, combining different algorithms is one approach that can potentially improve the accuracy of the prediction. We developed a new method that initially uses a hidden Markov model to predict alternative models for membrane spanning helices in proteins. The algorithm subsequently identifies the best among models by ranking them using a novel scoring function based on the folding energy of transmembrane helical fragments. This folding of helical fragments and the incorporation into membrane is modeled using CHARMm, extended with the Generalized Born surface area solvent model (GBSA/IM) with implicit membrane. The combined method reported here, TMHGB significantly increases the accuracy of the original hidden Markov model-based algorithm.

Automated evaluation of chemical shift perturbation spectra: New approaches to quantitative analysis of receptor-ligand interaction NMR spectra.

This paper presents new methods designed for quantitative analysis of chemical shift perturbation NMR spectra. The methods automatically trace the displacements of cross peaks between a perturbed test spectrum and the reference spectrum (or among a series of titration spectra), and measure the changes of chemical shifts, heights, and widths of the altered peaks. The methods are primary aimed at the (1)H-(15)N HSQC spectra of relatively small proteins (<15 kDa) assuming fast exchange between free and ligand-bound states on the chemical shift time scale, or for comparing spectra of free and fully bound states in the slow exchange situation. Using the (1)H-(15)N HSQC spectra from a titration experiment of the 74-residue Pex13p SH3 domain with a Pex14p peptide ligand (14 residues, K (d)= approximately 40 microM), we demonstrate the scope and limits of our automatic peak tracing (APET) algorithm for efficient scoring of high-throughput SAR by NMR type HSQC spectra, and progressive peak tracing (PROPET) algorithm for detailed analysis of ligand titration spectra. Simulated spectra with low signal-to-noise ratios (S/N ranged from 20 to 1) were used to demonstrate the reliability and reproducibility of the results when dealing with poor quality spectra. These algorithms have been implemented in a new software module, FELIX-Autoscreen, for streamlined processing, analysis and visualization of SAR by NMR and other high-throughput receptor/ligand interaction experiments.

Assessment of putative protein targets derived from the SARS genome.

The ability to rapidly and reliably develop hypotheses on the function of newly discovered protein sequences requires systematic and comprehensive analysis. Such an analysis, embodied within the DS GeneAtlas pipeline, has been used to critically evaluate the severe acute respiratory syndrome (SARS) genome with the goal of identifying new potential targets for viral therapeutic intervention. This paper discusses several new functional hypotheses on the roles played by the constituent gene products of SARS, and will serve as an example of how such assignments can be developed or extended on other systems of interest.

Common Structural Cliques: a tool for protein structure and function analysis.

Proposed is a method for locating functionally relevant atoms in protein structures and a representation of spatial arrangements of these atoms allowing for a flexible description of active sites in proteins. The search method is based on comparison of local structure features of proteins that share a common biochemical function. The method does not depend on overall similarity of structures and sequences of compared proteins or on previous knowledge about functionally relevant residues. The compared protein structures are condensed to a graph representation, with atoms as nodes and distances as edge labels. Protein graphs are then compared to extract all possible Common Structural Cliques. These cliques are merged to create Structural Templates: graphs that describe structural analogies between compared proteins. Structures of serine endopeptidases were compared in pairs using the presented algorithm with different geometrical parameters. Additionally, a Structural Template was extracted from the structures of aminotransferases, two different proteins that catalyze the same type of chemical reaction. The results presented show that the method works efficiently even in the case of large protein systems and allows for extraction of common structural features from proteins catalyzing a particular chemical reaction, but that evolved from different ancestors by convergent evolution.