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Motivation: The human leukocyte antigen (HLA) system is the main cause of organ transplant loss through the recognition of HLAs present on the graft by donor-specific antibodies raised by the recipient. It is therefore of key importance to identify all potentially immunogenic B-cell epitopes on HLAs in order to refine organ allocation. Such HLAs epitopes are currently characterized by the presence of polymorphic residues called "eplets". However, many polymorphic positions in HLAs sequences are not yet experimentally confirmed as eplets associated with a HLA epitope. Moreover, structural studies of these epitopes only consider 3D static structures., Results: We present here a machine-learning approach for predicting HLA epitopes, based on 3D-surface patches and molecular dynamics simulations. A collection of 3D-surface patches labeled as Epitope (2117) or Nonepitope (4769) according to Human Leukocyte Antigen Eplet Registry information was derived from 207 HLAs (61 solved and 146 predicted structures). Descriptors derived from static and dynamic patch properties were computed and three tree-based models were trained on a reduced non-redundant dataset. HLA-Epicheck is the prediction system formed by the three models. It leverages dynamic descriptors of 3D-surface patches for more than half of its prediction performance. Epitope predictions on unconfirmed eplets (absent from the initial dataset) are compared with experimental results and notable consistency is found., Availability and Implementation: Structural data and MD trajectories are deposited as open data under doi: 10.57745/GXZHH8. In-house scripts and machine-learning models for HLA-EpiCheck are available from https://gitlab.inria.fr/capsid.public_codes/hla-epicheck., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

The current rise of Open Science and Reproducibility in the Life Sciences requires the creation of rich, machine-actionable metadata in order to better share and reuse biological digital resources such as datasets, bioinformatics tools, training materials, etc. For this purpose, FAIR principles have been defined for both data and metadata and adopted by large communities, leading to the definition of specific metrics. However, automatic FAIRness assessment is still difficult because computational evaluations frequently require technical expertise and can be time-consuming. As a first step to address these issues, we propose FAIR-Checker, a web-based tool to assess the FAIRness of metadata presented by digital resources. FAIR-Checker offers two main facets: a "Check" module providing a thorough metadata evaluation and recommendations, and an "Inspect" module which assists users in improving metadata quality and therefore the FAIRness of their resource. FAIR-Checker leverages Semantic Web standards and technologies such as SPARQL queries and SHACL constraints to automatically assess FAIR metrics. Users are notified of missing, necessary, or recommended metadata for various resource categories. We evaluate FAIR-Checker in the context of improving the FAIRification of individual resources, through better metadata, as well as analyzing the FAIRness of more than 25 thousand bioinformatics software descriptions., (© 2023. The Author(s).)

Motivation: Protein domains can be viewed as building blocks, essential for understanding structure-function relationships in proteins. However, each domain database classifies protein domains using its own methodology. Thus, in many cases, domain models and boundaries differ from one domain database to the other, raising the question of domain definition and enumeration of true domain instances., Results: We propose an automated iterative workflow to assess protein domain classification by cross-mapping domain structural instances between domain databases and by evaluating structural alignments. CroMaSt (for Cross-Mapper of domain Structural instances) will classify all experimental structural instances of a given domain type into four different categories ('Core', 'True', 'Domain-like' and 'Failed'). CroMast is developed in Common Workflow Language and takes advantage of two well-known domain databases with wide coverage: Pfam and CATH. It uses the Kpax structural alignment tool with expert-adjusted parameters. CroMaSt was tested with the RNA Recognition Motif domain type and identifies 962 'True' and 541 'Domain-like' structural instances for this domain type. This method solves a crucial issue in domain-centric research and can generate essential information that could be used for synthetic biology and machine-learning approaches of protein domain engineering., Availability and Implementation: The workflow and the Results archive for the CroMaSt runs presented in this article are available from WorkflowHub (doi: 10.48546/workflowhub.workflow.390.2)., Supplementary Information: Supplementary data are available at Bioinformatics Advances online., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press.)

Background : Data management is fast becoming an essential part of scientific practice, driven by open science and FAIR (findable, accessible, interoperable, and reusable) data sharing requirements. Whilst data management plans (DMPs) are clear to data management experts and data stewards, understandings of their purpose and creation are often obscure to the producers of the data, which in academic environments are often PhD students. Methods : Within the RNAct EU Horizon 2020 ITN project, we engaged the 10 RNAct early-stage researchers (ESRs) in a training project aimed at formulating a DMP. To do so, we used the Data Stewardship Wizard (DSW) framework and modified the existing Life Sciences Knowledge Model into a simplified version aimed at training young scientists, with computational or experimental backgrounds, in core data management principles. We collected feedback from the ESRs during this exercise. Results : Here, we introduce our new life-sciences training DMP template for young scientists. We report and discuss our experiences as principal investigators (PIs) and ESRs during this project and address the typical difficulties that are encountered in developing and understanding a DMP. Conclusions : We found that the DS-wizard can also be an appropriate tool for DMP training, to get terminology and concepts across to researchers. A full training in addition requires an upstream step to present basic DMP concepts and a downstream step to publish a dataset in a (public) repository. Overall, the DS-Wizard tool was essential for our DMP training and we hope our efforts can be used in other projects., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Devignes MD et al.)

The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns "ensemble embeddings" of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing., (© 2023. The Author(s).)

Background: Automatic functional annotation of proteins is an open research problem in bioinformatics. The growing number of protein entries in public databases, for example in UniProtKB, poses challenges in manual functional annotation. Manual annotation requires expert human curators to search and read related research articles, interpret the results, and assign the annotations to the proteins. Thus, it is a time-consuming and expensive process. Therefore, designing computational tools to perform automatic annotation leveraging the high quality manual annotations that already exist in UniProtKB/SwissProt is an important research problem RESULTS: In this paper, we extend and adapt the GrAPFI (graph-based automatic protein function inference) (Sarker et al. in BMC Bioinform 21, 2020; Sarker et al., in: Proceedings of 7th international conference on complex networks and their applications, Cambridge, 2018) method for automatic annotation of proteins with gene ontology (GO) terms renaming it as GrAPFI-GO. The original GrAPFI method uses label propagation in a similarity graph where proteins are linked through the domains, families, and superfamilies that they share. Here, we also explore various types of similarity measures based on common neighbors in the graph. Moreover, GO terms are arranged in a hierarchical manner according to semantic parent-child relations. Therefore, we propose an efficient pruning and post-processing technique that integrates both semantic similarity and hierarchical relations between the GO terms. We produce experimental results comparing the GrAPFI-GO method with and without considering common neighbors similarity. We also test the performance of GrAPFI-GO and other annotation tools for GO annotation on a benchmark of proteins with and without the proposed pruning and post-processing procedure., Conclusion: Our results show that the proposed semantic hierarchical post-processing potentially improves the performance of GrAPFI-GO and of other annotation tools as well. Thus, GrAPFI-GO exposes an original efficient and reusable procedure, to exploit the semantic relations among the GO terms in order to improve the automatic annotation of protein functions., (© 2022. The Author(s).)

Aims: End-stage renal disease (ESRD) treated by chronic hemodialysis (HD) is associated with poor cardiovascular (CV) outcomes, with no available evidence-based therapeutics. A multiplexed proteomic approach may identify new pathophysiological pathways associated with CV outcomes, potentially actionable for precision medicine., Methods and Results: The AURORA trial was an international, multicentre, randomized, double-blind trial involving 2776 patients undergoing maintenance HD. Rosuvastatin vs. placebo had no significant effect on the composite primary endpoint of death from CV causes, nonfatal myocardial infarction or nonfatal stroke. We first compared CV risk-matched cases and controls ( n = 410) to identify novel biomarkers using a multiplex proximity extension immunoassay (276 proteomic biomarkers assessed with Olink TM ). We replicated our findings in 200 unmatched cases and 200 controls. External validation was conducted from a multicentre real-life Danish cohort [Aarhus-Aalborg (AA), n = 331 patients] in which 92 Olink TM biomarkers were assessed. In AURORA, only N -terminal pro-brain natriuretic peptide (NT-proBNP, positive association) and stem cell factor (SCF) (negative association) were found consistently associated with the trial's primary outcome across exploration and replication phases, independently from the baseline characteristics. Stem cell factor displayed a lower added predictive ability compared with NT-ProBNP. In the AA cohort, in multivariable analyses, BNP was found significantly associated with major CV events, while higher SCF was associated with less frequent CV deaths., Conclusions: Our findings suggest that NT-proBNP and SCF may help identify ESRD patients with respectively high and low CV risk, beyond classical clinical predictors and also point at novel pathways for prevention and treatment., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Machine learning is now an essential part of any biomedical study but its integration into real effective Learning Health Systems, including the whole process of Knowledge Discovery from Data (KDD), is not yet realised. We propose an original extension of the KDD process model that involves an inductive database. We designed for the first time a generic model of Inductive Clinical DataBase (ICDB) aimed at hosting both patient data and learned models. We report experiments conducted on patient data in the frame of a project dedicated to fight heart failure. The results show how the ICDB approach allows to identify biomarker combinations, specific and predictive of heart fibrosis phenotype, that put forward hypotheses relative to underlying mechanisms. Two main scenarios were considered, a local-to-global KDD scenario and a trans-cohort alignment scenario. This promising proof of concept enables us to draw the contours of a next-generation Knowledge Discovery Environment (KDE)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier Inc.)

Emerging SARS-CoV-2 variants raise concerns about our ability to withstand the Covid-19 pandemic, and therefore, understanding mechanistic differences of those variants is crucial. In this study, we investigate disparities between the SARS-CoV-2 wild type and five variants that emerged in late 2020, focusing on the structure and dynamics of the spike protein interface with the human angiotensin-converting enzyme 2 (ACE2) receptor, by using crystallographic structures and extended analysis of microsecond molecular dynamics simulations. Dihedral angle principal component analysis (PCA) showed the strong similarities in the spike receptor binding domain (RBD) dynamics of the Alpha, Beta, Gamma, and Delta variants, in contrast with those of WT and Epsilon. Dynamical perturbation networks and contact PCA identified the peculiar interface dynamics of the Delta variant, which cannot be directly imputable to its specific L452R and T478K mutations since those residues are not in direct contact with the human ACE2 receptor. Our outcome shows that in the Delta variant the L452R and T478K mutations act synergistically on neighboring residues to provoke drastic changes in the spike/ACE2 interface; thus a singular mechanism of action eventually explains why it dominated over preceding variants.

Objectives: This study sought to identify homogenous echocardiographic phenotypes in community-based cohorts and assess their association with outcomes., Background: Asymptomatic cardiac dysfunction leads to a high risk of long-term cardiovascular morbidity and mortality; however, better echocardiographic classification of asymptomatic individuals remains a challenge., Methods: Echocardiographic phenotypes were identified using K-means clustering in the first generation of the STANISLAS (Yearly non-invasive follow-up of Health status of Lorraine insured inhabitants) cohort (N = 827; mean age: 60 ± 5 years; men: 48%), and their associations with vascular function and circulating biomarkers were also assessed. These phenotypes were externally validated in the Malmö Preventive Project cohort (N = 1,394; mean age: 67 ± 6 years; men: 70%), and their associations with the composite of cardiovascular mortality (CVM) or heart failure hospitalization (HFH) were assessed as well., Results: Three echocardiographic phenotypes were identified as "mostly normal (MN)" (n = 334), "diastolic changes (D)" (n = 323), and "diastolic changes with structural remodeling (D/S)" (n = 170). The D and D/S phenotypes had similar ages, body mass indices, cardiovascular risk factors, vascular impairments, and diastolic function changes. The D phenotype consisted mainly of women and featured increased levels of inflammatory biomarkers, whereas the D/S phenotype, consisted predominantly of men, displayed the highest values of left ventricular mass, volume, and remodeling biomarkers. The phenotypes were predicted based on a simple algorithm including e', left ventricular mass and volume (e'VM algorithm). In the Malmö cohort, subgroups derived from e'VM algorithm were significantly associated with a higher risk of CVM and HFH (adjusted HR in the D phenotype = 1.87; 95% CI: 1.04 to 3.37; adjusted HR in the D/S phenotype = 3.02; 95% CI: 1.71 to 5.34)., Conclusions: Among asymptomatic, middle-aged individuals, echocardiographic data-driven classification based on the simple e'VM algorithm identified profiles with different long-term HF risk. (4th Visit at 17 Years of Cohort STANISLAS-Stanislas Ancillary Study ESCIF [STANISLASV4]; NCT01391442)., Competing Interests: Funding Support and Author Disclosures The STANISLAS Cohort visit 4 was sponsored by the Nancy CHRU and was funded in part by the Programme Hospitalier de Recherche Clinique Interrégional. Biomarker studies are co-funded by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004) and FEDER Lorraine, and all French co-authors are supported by the French Programme d'investissements d'avenir project “Lorraine Université d’Excellence” GEENAGE (ANR-15-IDEX-04-LUE) programs, and the Contrat de Plan Etat Région Lorraine and FEDER IT2MP. The research leading to these results also received support from the European Union Commission’s Seventh Framework program under grant 305507 (Heart OMics in Aging). Support was also provided from the “EXPERT” ERA-CVD 2016 and MR-Focus (both grants managed by the French National Research Agency). Drs. Girerd, Rossignol, and Zannad are supported by the French National Research Agency Fighting Heart Failure (ANR-15-RHU-0004), the French PIA project Lorraine Université d’Excellence GEENAGE (ANR-15-IDEX-04-LUE) programs, and the Contrat de Plan Etat Région Lorraine and FEDER IT2MP. Dr Girerd is a consultant for Novartis, AstraZeneca, and Boehringer Ingelheim. Dr Rossignol has received grants and personal fees from AstraZeneca, Bayer, CVRx, Fresenius, and Novartis; and personal fees from Grunenthal, Servier, Stealth Peptides, Vifor Fresenius Medical Care Renal Pharma, Idorsia, NovoNordisk, Ablative Solutions, G3P, Corvidia, and Relypsa. Dr Zannad has received personal fees from Boehringer Ingelheim, Janssen, Novartis, Boston Scientific, Amgen, CVRx, AstraZeneca, Vifor Fresenius, Cardior, Cereno Pharmaceutical, Applied Therapeutics, Merck Sharpe and Dohme, Bayer, and Cellprothera outside the submitted work; and has received other support from CVCT and Cardiorenal, outside the submitted work. Dr Ferreira is a consultant for Boehringer Ingelheim. Dr Magnusson is supported by grants from the Medical Faculty of Lund University; Skane University Hospital; the Crafoord Foundation; the Ernhold Lundstroms Research Foundation; the Region Skane; the Hulda and Conrad Mossfelt Foundation; the Southwest Skanes Diabetes Foundation; the Kockska Foundation; the Research Funds of Region Skåne; the Swedish Heart and Lung Foundation; and the Wallenberg Center for Molecular Medicine, Lund University. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing. We describe a new computational approach called "PPIDM" (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described "CODAC" (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as "Gold-Standard" a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided. Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/., Competing Interests: The authors have declared that no competing interests exist. Author Dave W Ritchie was unable to confirm their authorship contributions. On their behalf, the corresponding author has reported their contributions to the best of their knowledge.

The choice of the most appropriate unsupervised machine-learning method for "heterogeneous" or "mixed" data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of "ready-to-use" tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

Objective: Determining the factors associated with new-onset pre-diabetes and type 2 diabetes mellitus (T2D) is important for improving the current prevention strategies and for a better understanding of the disease., Design: To study the factors (clinical, circulating protein and genetic) associated with new onset pre-diabetes and T2D in an initially healthy (without diabetes) populational familial cohort with a long follow-up (STANISLAS cohort)., Methods: A total of 1506 participants attended both the visit 1 and visit 4, separated by ≈20 years. Over 400 proteins, GWAS and genetic associations were studied using models adjusted for potential confounders. Both prospective (V1 to V4) and cross-sectional (V4) analyses were performed., Results: People who developed pre-diabetes (n = 555) and/or T2D (n = 73) were older, had higher BMI, blood pressure, glucose, LDL cholesterol, and lower eGFR. After multivariable selection, PAPP-A (pappalysin-1) was the only circulating protein associated with the onset of both pre-diabetes and T2D with associations persisting at visit 4 (i.e. ≈20 years later). FGF-21 (fibroblast growth factor 21) was a strong prognosticator for incident T2D in the longitudinal analysis, but not in the cross-sectional analysis. The heritability of the circulating PAPP-A was estimated at 44%. In GWAS analysis, the SNP rs634737 was associated with PAPP-A both at V1 and V4. External replication also showed lower levels of PAPP-A in patients with T2D., Conclusions: The risk of developing pre-diabetes and T2D increases with age and with features of the metabolic syndrome. Circulating PAPP-A, which has an important genetic component, was associated with both the development and presence of pre-diabetes and T2D.

Background: Many patients with heart failure with preserved ejection fraction (HFpEF) are women. Exploring mechanisms underlying the sex differences may improve our understanding of the pathophysiology of HFpEF. Studies focusing on sex differences in circulating proteins in HFpEF patients are scarce., Methods: A total of 415 proteins were analyzed in 392 HFpEF patients included in The Metabolic Road to Diastolic Heart Failure: Diastolic Heart Failure study (MEDIA-DHF). Sex differences in these proteins were assessed using adjusted logistic regression analyses. The associations between candidate proteins and cardiovascular (CV) death or CV hospitalization (with sex interaction) were assessed using Cox regression models., Results: We found 9 proteins to be differentially expressed between female and male patients. Women expressed more LPL and PLIN1, which are markers of lipid metabolism; more LHB, IGFBP3, and IL1RL2 as markers of transcriptional regulation; and more Ep-CAM as marker of hemostasis. Women expressed less MMP-3, which is a marker associated with extracellular matrix organization; less NRP1, which is associated with developmental processes; and less ACE2, which is related to metabolism. Sex was not associated with the study outcomes (adj. HR 1.48, 95% CI 0.83-2.63), p = 0.18., Conclusion: In chronic HFpEF, assessing sex differences in a wide range of circulating proteins led to the identification of 9 proteins that were differentially expressed between female and male patients. These findings may help further investigations into potential pathophysiological processes contributing to HFpEF.

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes. Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression. Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12-3.32, p  = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism. Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.Clinical significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolismBiomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.

Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes PGx-related knowledge a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly reusable by humans or software. Natural language processing techniques have been developed to guide experts who curate this amount of knowledge. But existing works are limited by the absence of a high quality annotated corpus focusing on PGx domain. In particular, this absence restricts the use of supervised machine learning. This article introduces PGxCorpus, a manually annotated corpus, designed to fill this gap and to enable the automatic extraction of PGx relationships from text. It comprises 945 sentences from 911 PubMed abstracts, annotated with PGx entities of interest (mainly gene variations, genes, drugs and phenotypes), and relationships between those. In this article, we present the corpus itself, its construction and a baseline experiment that illustrates how it may be leveraged to synthesize and summarize PGx knowledge.

Background: Hypertension, obesity and diabetes are major and potentially modifiable "risk factors" for cardiovascular diseases. Identification of biomarkers specific to these risk factors may help understanding the underlying pathophysiological pathways, and developing individual treatment., Methods: The FIBRO-TARGETS (targeting cardiac fibrosis for heart failure treatment) consortium has merged data from 12 patient cohorts in 1 common database of > 12,000 patients. Three mutually exclusive main phenotypic groups were identified ("cases"): (1) "hypertensive"; (2) "obese"; and (3) "diabetic"; age-sex matched in a 1:2 proportion with "healthy controls" without any of these phenotypes. Proteomic associations were studied using a biostatistical method based on LASSO and confronted with machine-learning and complex network approaches., Results: The case:control distribution by each cardiovascular phenotype was hypertension (50:100), obesity (50:98), and diabetes (36:72). Of the 86 studied proteins, 4 were found to be independently associated with hypertension: GDF-15, LEP, SORT-1 and FABP-2; 3 with obesity: CEACAM-8, LEP and PRELP; and 4 with diabetes: GDF-15, REN, CXCL-1 and SCF. GDF-15 (hypertension + diabetes) and LEP (hypertension + obesity) are shared by 2 different phenotypes. A machine-learning approach confirmed GDF-15, LEP and SORT-1 as discriminant biomarkers for the hypertension group, and LEP plus PRELP for the obesity group. Complex network analyses provided insight on the mechanisms underlying these disease phenotypes where fibrosis may play a central role., Conclusion: Patients with "mutually exclusive" phenotypes display distinct bioprofiles that might underpin different biological pathways, potentially leading to fibrosis. Plasma protein biomarkers and their association with mutually exclusive cardiovascular phenotypes: the FIBRO-TARGETS case-control analyses. Patients with "mutually exclusive" phenotypes (blue: obesity, hypertension and diabetes) display distinct protein bioprofiles (green: decreased expression; red: increased expression) that might underpin different biological pathways (orange arrow), potentially leading to fibrosis.

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities., (Copyright © 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Introduction: The confirmation time interval for the presence of antiphospholipid antibodies (aPL) has been extended to 12 weeks as epiphenomenal antibodies may disappear after 6 weeks. Our aim was to analyse extended persistence of aPL positivity beyond the 12-week interval., Methods: We retrospectively analysed our database of 23 856 aPL test samples collected between 2005 and 2017 from 17 367 consecutive patients. Two groups of patients were identified among aPL-positive patients, confirmed at 12 weeks: with or without extended persistence beyond confirmatory testing. Percentages of extended persistence are given according to the initial aPL positivity profiles, and baseline laboratory variables are compared between the two groups., Results: Three hundred and twenty-seven patients confirmed aPL-positive had subsequent testing. The vast majority of them displayed extended persistence in the long term: 89.6% and up to 97.9% for patients with initial triple positivity. In extended persistent positive patients, there were more LA-positive initial samples, and baseline LA test values and IgG aCL titres were higher than in nonpersistent positive patients., Conclusion: Data from a large database of an aPL referral laboratory showed that the time interval of 12 weeks defining persistence of aPL positivity was appropriate for the majority of patients. Furthermore, we found baseline features associated with extended persistence., (© 2019 John Wiley & Sons Ltd.)

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements., (© 2019 Wiley Periodicals, Inc.)

Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function., Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory., Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Background: Conjugative spread of antibiotic resistance and virulence genes in bacteria constitutes an important threat to public health. Beyond the well-known conjugative plasmids, recent genome analyses have shown that integrative and conjugative elements (ICEs) are the most widespread conjugative elements, even if their transfer mechanism has been little studied until now. The initiator of conjugation is the relaxase, a protein catalyzing a site-specific nick on the origin of transfer ( oriT ) of the ICE. Besides canonical relaxases, recent studies revealed non-canonical ones, such as relaxases of the MOB T family that are related to rolling-circle replication proteins of the Rep_trans family. MOB T relaxases are encoded by ICEs of the ICE St3 /ICE Bs1 /Tn 916 superfamily, a superfamily widespread in Firmicutes, and frequently conferring antibiotic resistance., Results: Here, we present the first biochemical and structural characterization of a MOB T relaxase: the RelSt3 relaxase encoded by ICE St3 from Streptococcus thermophilus . We identified the oriT region of ICE St3 and demonstrated that RelSt3 is required for its conjugative transfer. The purified RelSt3 protein is a stable dimer that provides a Mn 2+ -dependent single-stranded endonuclease activity. Sequence comparisons of MOB T relaxases led to the identification of MOB T conserved motifs. These motifs, together with the construction of a 3D model of the relaxase domain of RelSt3, allowed us to determine conserved residues of the RelSt3 active site. The involvement of these residues in DNA nicking activity was demonstrated by targeted mutagenesis., Conclusions: All together, this work argues in favor of MOB T being a full family of non-canonical relaxases. The biochemical and structural characterization of a MOB T member provides new insights on the molecular mechanism of conjugative transfer mediated by ICEs in Gram-positive bacteria. This could be a first step towards conceiving rational strategies to control gene transfer in these bacteria., Competing Interests: Not applicable.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Background: Families of related proteins and their different functions may be described systematically using common classifications and ontologies such as Pfam and GO (Gene Ontology), for example. However, many proteins consist of multiple domains, and each domain, or some combination of domains, can be responsible for a particular molecular function. Therefore, identifying which domains should be associated with a specific function is a non-trivial task., Results: We describe a general approach for the computational discovery of associations between different sets of annotations by formalising the problem as a bipartite graph enrichment problem in the setting of a tripartite graph. We call this approach "CODAC" (for COmputational Discovery of Direct Associations using Common Neighbours). As one application of this approach, we describe "GODomainMiner" for associating GO terms with protein domains. We used GODomainMiner to predict GO-domain associations between each of the 3 GO ontology namespaces (MF, BP, and CC) and the Pfam, CATH, and SCOP domain classifications. Overall, GODomainMiner yields average enrichments of 15-, 41- and 25-fold GO-domain associations compared to the existing GO annotations in these 3 domain classifications, respectively., Conclusions: These associations could potentially be used to annotate many of the protein chains in the Protein Databank and protein sequences in UniProt whose domain composition is known but which currently lack GO annotation.

Fetal and neonatal exposure to long-chain alkylphenols has been suspected to promote breast developmental disorders and consequently to increase breast cancer risk. However, disease predisposition from developmental exposures remains unclear. In this work, human MCF-10A mammary epithelial cells were exposed in vitro to a low dose of a realistic (4-nonylphenol + 4-tert-octylphenol) mixture. Transcriptome and cell-phenotype analyses combined to functional and signaling network modeling indicated that long-chain alkylphenols triggered enhanced proliferation, migration ability, and apoptosis resistance and shed light on the underlying molecular mechanisms which involved the human estrogen receptor alpha 36 (ERα36) variant. A male mouse-inherited transgenerational model of exposure to three environmentally relevant doses of the alkylphenol mix was set up in order to determine whether and how it would impact on mammary gland architecture. Mammary glands from F3 progeny obtained after intrabuccal chronic exposure of C57BL/6J P0 pregnant mice followed by F1-F3 male inheritance displayed an altered histology which correlated with the phenotypes observed in vitro in human mammary epithelial cells. Since cellular phenotypes are similar in vivo and in vitro and involve the unique ERα36 human variant, such consequences of alkylphenol exposure could be extrapolated from mouse model to human. However, transient alkylphenol treatments combined to ERα36 overexpression in mammary epithelial cells were not sufficient to trigger tumorigenesis in xenografted Nude mice. Therefore, it remains to be determined if low-dose alkylphenol transgenerational exposure and subsequent abnormal mammary gland development could account for an increased breast cancer susceptibility.

Background: Patient data, such as electronic health records or adverse event reporting systems, constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequently associated ADEs in subgroups of patients., Results: Because ADEs have complex manifestations, we use formal concept analysis and its pattern structures, a mathematical framework that allows generalization using domain knowledge formalized in medical ontologies. Results obtained with three different settings and two different datasets show that this approach is flexible and allows extraction of association rules at various levels of generalization., Conclusions: The chosen approach permits an expressive representation of a patient ADEs. Extracted association rules point to distinct ADEs that occur in a same group of patients, and could serve as a basis for a recommandation system. The proposed representation is flexible and can be extended to make use of additional ontologies and various patient records.

Recent analyses of bacterial genomes have shown that integrated elements that transfer by conjugation play an essential role in horizontal gene transfer. Among these elements, the integrative and mobilizable elements (IMEs) are known to encode their own excision and integration machinery, and to carry all the sequences or genes necessary to hijack the mating pore of a conjugative element for their own transfer. However, knowledge of their prevalence and diversity is still severely lacking. In this work, an extensive analysis of 124 genomes from 27 species of Streptococcus reveals 144 IMEs. These IMEs encode either tyrosine or serine integrases. The identification of IME boundaries shows that 141 are specifically integrated in 17 target sites. The IME-encoded relaxases belong to nine superfamilies, among which four are previously unknown in any mobilizable or conjugative element. A total of 118 IMEs are found to encode a non-canonical relaxase related to rolling circle replication initiators (belonging to the four novel families or to MobT). Surprisingly, among these, 83 encode a TcpA protein (i.e., a non-canonical coupling protein (CP) that is more closely related to FtsK than VirD4) that was not previously known to be encoded by mobilizable elements. Phylogenetic analyses reveal not only many integration/excision module replacements but also losses, acquisitions or replacements of TcpA genes between IMEs. This glimpse into the still poorly known world of IMEs reveals that mobilizable elements have a very high prevalence. Their diversity is even greater than expected, with most encoding a CP and/or a non-canonical relaxase.

Many of the modeling targets in the blind CASP-11/CAPRI-30 experiment were protein homo-dimers and homo-tetramers. Here, we perform a retrospective docking-based analysis of the perfectly symmetrical CAPRI Round 30 targets whose crystal structures have been published. Starting from the CASP "stage-2" fold prediction models, we show that using our recently developed "SAM" polar Fourier symmetry docking algorithm combined with NAMD energy minimization often gives acceptable or better 3D models of the target complexes. We also use SAM to analyze the overall quality of all CASP structural models for the selected targets from a docking-based perspective. We demonstrate that docking only CASP "center" structures for the selected targets provides a fruitful and economical docking strategy. Furthermore, our results show that many of the CASP models are dockable in the sense that they can lead to acceptable or better models of symmetrical complexes. Even though SAM is very fast, using docking and NAMD energy minimization to pull out acceptable docking models from a large ensemble of docked CASP models is computationally expensive. Nonetheless, thanks to our SAM docking algorithm, we expect that applying our docking protocol on a modern computer cluster will give us the ability to routinely model 3D structures of symmetrical protein complexes from CASP-quality models. Proteins 2017; 85:463-469. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Background: Many entries in the protein data bank (PDB) are annotated to show their component protein domains according to the Pfam classification, as well as their biological function through the enzyme commission (EC) numbering scheme. However, despite the fact that the biological activity of many proteins often arises from specific domain-domain and domain-ligand interactions, current on-line resources rarely provide a direct mapping from structure to function at the domain level. Since the PDB now contains many tens of thousands of protein chains, and since protein sequence databases can dwarf such numbers by orders of magnitude, there is a pressing need to develop automatic structure-function annotation tools which can operate at the domain level., Results: This article presents ECDomainMiner, a novel content-based filtering approach to automatically infer associations between EC numbers and Pfam domains. ECDomainMiner finds a total of 20,728 non-redundant EC-Pfam associations with a F-measure of 0.95 with respect to a "Gold Standard" test set extracted from InterPro. Compared to the 1515 manually curated EC-Pfam associations in InterPro, ECDomainMiner infers a 13-fold increase in the number of EC-Pfam associations., Conclusion: These EC-Pfam associations could be used to annotate some 58,722 protein chains in the PDB which currently lack any EC annotation. The ECDomainMiner database is publicly available at http://ecdm.loria.fr/ .

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Recent genome analyses suggest that integrative and conjugative elements (ICEs) are widespread in bacterial genomes and therefore play an essential role in horizontal transfer. However, only a few of these elements are precisely characterized and correctly delineated within sequenced bacterial genomes. Even though previous analysis showed the presence of ICEs in some species of Streptococci, the global prevalence and diversity of ICEs was not analyzed in this genus. In this study, we searched for ICEs in the completely sequenced genomes of 124 strains belonging to 27 streptococcal species. These exhaustive analyses revealed 105 putative ICEs and 26 slightly decayed elements whose limits were assessed and whose insertion site was identified. These ICEs were grouped in seven distinct unrelated or distantly related families, according to their conjugation modules. Integration of these streptococcal ICEs is catalyzed either by a site-specific tyrosine integrase, a low-specificity tyrosine integrase, a site-specific single serine integrase, a triplet of site-specific serine integrases or a DDE transposase. Analysis of their integration site led to the detection of 18 target-genes for streptococcal ICE insertion including eight that had not been identified previously (ftsK, guaA, lysS, mutT, rpmG, rpsI, traG, and ebfC). It also suggests that all specificities have evolved to minimize the impact of the insertion on the host. This overall analysis of streptococcal ICEs emphasizes their prevalence and diversity and demonstrates that exchanges or acquisitions of conjugation and recombination modules are frequent.

Comparing and classifying protein domain interactions according to their three-dimensional (3D) structures can help to understand protein structure-function and evolutionary relationships. Additionally, structural knowledge of existing domain-domain interactions can provide a useful way to find structural templates with which to model the 3D structures of unsolved protein complexes. Here we present a straightforward guide to using the "Kbdock" protein domain structure database and its associated web site for exploring and comparing protein domain-domain interactions (DDIs) and domain-peptide interactions (DPIs) at the Pfam domain family level. We also briefly explain how the Kbdock web site works, and we provide some notes and suggestions which should help to avoid some common pitfalls when working with 3D protein domain structures.

While the number of solved 3D protein structures continues to grow rapidly, the structural rules that distinguish protein-protein interactions between different structural families are still not clear. Here, we classify and analyse the secondary structural features and promiscuity of a comprehensive non-redundant set of domain family binding sites (DFBSs) and hetero domain-domain interactions (DDIs) extracted from our updated KBDOCK resource. We have partitioned 4001 DFBSs into five classes using their propensities for three types of secondary structural elements ("α" for helices, "β" for strands, and "γ" for irregular structure) and we have analysed how frequently these classes occur in DDIs. Our results show that β elements are not highly represented in DFBSs compared to α and γ elements. At the DDI level, all classes of binding sites tend to preferentially bind to the same class of binding sites and α/β contacts are significantly disfavored. Very few DFBSs are promiscuous: 80% of them interact with just one Pfam domain. About 50% of our Pfam domains bear only one single-partner DFBS and are therefore monogamous in their interactions with other domains. Conversely, promiscuous Pfam domains bear several DFBSs among which one or two are promiscuous, thereby multiplying the promiscuity of the concerned protein.

Nonribosomal peptides represent a large variety of natural active compounds produced by microorganisms. Due to their specific biosynthesis pathway through large assembly lines called NonRibosomal Peptide Synthetases (NRPSs), they often display complex structures with cycles and branches. Moreover they often contain non proteogenic or modified monomers, such as the D-monomers produced by epimerization. We investigate here some sequence specificities of the condensation (C) and epimerization (E) domains of NRPS that can be used to predict the possible isomeric state (D or L) of each monomer in a putative peptide. We show that C- and E- domains can be divided into 2 sub-regions called Up-Seq and Down-Seq. The Up-Seq region corresponds to an InterPro domain (IPR001242) and is shared by C- and E-domains. The Down-Seq region is specific to the enzymatic activity of the domain. Amino-acid signatures (represented as sequence logos) previously described for complete C-and E-domains have been restricted to the Down-Seq region and amplified thanks to additional sequences. Moreover a new Down-Seq signature has been found for Ct-domains found in fungi and responsible for terminal cyclization of the peptides. The identification of these signatures has been included in a workflow named Florine, aimed to predict nonribosomal peptides from NRPS sequence analyses. In some cases, the prediction of isomery is guided by genus-specific rules. Florine was used on a Pseudomonas genome to allow the determination of the type of pyoverdin produced, the update of syringafactin structure and the identification of novel putative products.

Comparing, classifying and modelling protein structural interactions can enrich our understanding of many biomolecular processes. This contribution describes Kbdock (http://kbdock.loria.fr/), a database system that combines the Pfam domain classification with coordinate data from the PDB to analyse and model 3D domain-domain interactions (DDIs). Kbdock can be queried using Pfam domain identifiers, protein sequences or 3D protein structures. For a given query domain or pair of domains, Kbdock retrieves and displays a non-redundant list of homologous DDIs or domain-peptide interactions in a common coordinate frame. Kbdock may also be used to search for and visualize interactions involving different, but structurally similar, Pfam families. Thus, structural DDI templates may be proposed even when there is little or no sequence similarity to the query domains.

Protein docking algorithms aim to calculate the three-dimensional (3D) structure of a protein complex starting from its unbound components. Although ab initio docking algorithms are improving, there is a growing need to use homology modeling techniques to exploit the rapidly increasing volumes of structural information that now exist. However, most current homology modeling approaches involve finding a pair of complete single-chain structures in a homologous protein complex to use as a 3D template, despite the fact that protein complexes are often formed from one or more domain-domain interactions (DDIs). To model 3D protein complexes by domain-domain homology, we have developed a case-based reasoning approach called KBDOCK which systematically identifies and reuses domain family binding sites from our database of nonredundant DDIs. When tested on 54 protein complexes from the Protein Docking Benchmark, our approach provides a near-perfect way to model single-domain protein complexes when full-homology templates are available, and it extends our ability to model more difficult cases when only partial or incomplete templates exist. These promising early results highlight the need for a new and diverse docking benchmark set, specifically designed to assess homology docking approaches., (This article is protected by copyright. All rights reserved.)

Intellectual disability (ID) is a clinical sign reflecting diverse neurodevelopmental disorders that are genetically and phenotypically heterogeneous. Just recently, partial or complete deletion of methyl-CpG-binding domain 5 (MBD5) gene has been implicated as causative in the phenotype associated with 2q23.1 microdeletion syndrome. In the course of systematic whole-genome screening of individuals with unexplained ID by array-based comparative genomic hybridization, we identified de novo intragenic deletions of MBD5 in three patients leading, as previously documented, to haploinsufficiency of MBD5. In addition, we described a patient with an unreported de novo MBD5 intragenic duplication. Reverse transcriptase-PCR and sequencing analyses showed the presence of numerous aberrant transcripts leading to premature termination codon. To further elucidate the involvement of MBD5 in ID, we sequenced ten coding, five non-coding exons and an evolutionary conserved region in intron 2, in a selected cohort of 78 subjects with a phenotype reminiscent of 2q23.1 microdeletion syndrome. Besides variants most often inherited from an healthy parent, we identified for the first time a de novo nonsense mutation associated with a much more damaging phenotype. Taken together, these results extend the mutation spectrum in MBD5 gene and contribute to refine the associated phenotype of neurodevelopmental disorder.

Background: Drug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence., Results: In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site., Conclusions: Side effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs.

Functional classification aims at grouping genes according to their molecular function or the biological process they participate in. Evaluating the validity of such unsupervised gene classification remains a challenge given the variety of distance measures and classification algorithms that can be used. We evaluate here functional classification of genes with the help of reference sets: KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathways and Pfam clans. These sets represent ground truth for any distance based on GO (Gene Ontology) biological process and molecular function annotations respectively. Overlaps between clusters and reference sets are estimated by the F-score method. We test our previously described IntelliGO semantic distance with hierarchical and fuzzy C-means clustering and we compare results with the state-of-the-art DAVID (Database for Annotation Visualisation and Integrated Discovery) functional classification method. Finally, study of best matching clusters to reference sets leads us to propose a set-difference method for discovering missing information.

Motivation: In recent years, much structural information on protein domains and their pair-wise interactions has been made available in public databases. However, it is not yet clear how best to use this information to discover general rules or interaction patterns about structural protein-protein interactions. Improving our ability to detect and exploit structural interaction patterns will help to provide a better 3D picture of the known protein interactome, and will help to guide docking-based predictions of the 3D structures of unsolved protein complexes., Results: This article presents KBDOCK, a 3D database approach for spatially clustering protein binding sites and for performing template-based (knowledge-based) protein docking. KBDOCK combines residue contact information from the 3DID database with the Pfam protein domain family classification together with coordinate data from the Protein Data Bank. This allows the 3D configurations of all known hetero domain-domain interactions to be superposed and clustered for each Pfam family. We find that most Pfam domain families have up to four hetero binding sites, and over 60% of all domain families have just one hetero binding site. The utility of this approach for template-based docking is demonstrated using 73 complexes from the Protein Docking Benchmark. Overall, up to 45 out of 73 complexes may be modelled by direct homology to existing domain interfaces, and key binding site information is found for 24 of the 28 remaining complexes. These results show that KBDOCK can often provide useful information for predicting the structures of unknown protein complexes., Availability: http://kbdock.loria.fr/, Contact: Dave.Ritchie@inria.fr, Supplementary Information: Supplementary data are available at Bioinformatics online.

One current challenge in biomedicine is to analyze large amounts of complex biological data for extracting domain knowledge. This work holds on the use of knowledge-based techniques such as knowledge discovery (KD) and knowledge representation (KR) in pharmacogenomics, where knowledge units represent genotype-phenotype relationships in the context of a given treatment. An objective is to design knowledge base (KB, here also mentioned as an ontology) and then to use it in the KD process itself. A method is proposed for dealing with two main tasks: (1) building a KB from heterogeneous data related to genotype, phenotype, and treatment, and (2) applying KD techniques on knowledge assertions for extracting genotype-phenotype relationships. An application was carried out on a clinical trial concerned with the variability of drug response to montelukast treatment. Genotype-genotype and genotype-phenotype associations were retrieved together with new associations, allowing the extension of the initial KB. This experiment shows the potential of KR and KD processes, especially for designing KB, checking KB consistency, and reasoning for problem solving.

Background: The Gene Ontology (GO) is a well known controlled vocabulary describing the biological process, molecular function and cellular component aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both. However, only a few of the semantic similarity measures described so far can handle GO annotations differently according to their origin (i.e. their evidence codes)., Results: We present here a new semantic similarity measure called IntelliGO which integrates several complementary properties in a novel vector space model. The coefficients associated with each GO term that annotates a given gene or protein include its information content as well as a customized value for each type of GO evidence code. The generalized cosine similarity measure, used for calculating the dot product between two vectors, has been rigorously adapted to the context of the GO graph. The IntelliGO similarity measure is tested on two benchmark datasets consisting of KEGG pathways and Pfam domains grouped as clans, considering the GO biological process and molecular function terms, respectively, for a total of 683 yeast and human genes and involving more than 67,900 pair-wise comparisons. The ability of the IntelliGO similarity measure to express the biological cohesion of sets of genes compares favourably to four existing similarity measures. For inter-set comparison, it consistently discriminates between distinct sets of genes. Furthermore, the IntelliGO similarity measure allows the influence of weights assigned to evidence codes to be checked. Finally, the results obtained with a complementary reference technique give intermediate but correct correlation values with the sequence similarity, Pfam, and Enzyme classifications when compared to previously published measures., Conclusions: The IntelliGO similarity measure provides a customizable and comprehensive method for quantifying gene similarity based on GO annotations. It also displays a robust set-discriminating power which suggests it will be useful for functional clustering., Availability: An on-line version of the IntelliGO similarity measure is available at: http://bioinfo.loria.fr/Members/benabdsi/intelligo_project/

HexServer (http://hexserver.loria.fr/) is the first Fourier transform (FFT)-based protein docking server to be powered by graphics processors. Using two graphics processors simultaneously, a typical 6D docking run takes approximately 15 s, which is up to two orders of magnitude faster than conventional FFT-based docking approaches using comparable resolution and scoring functions. The server requires two protein structures in PDB format to be uploaded, and it produces a ranked list of up to 1000 docking predictions. Knowledge of one or both protein binding sites may be used to focus and shorten the calculation when such information is available. The first 20 predictions may be accessed individually, and a single file of all predicted orientations may be downloaded as a compressed multi-model PDB file. The server is publicly available and does not require any registration or identification by the user.

In silico screening methodologies are widely recognized as efficient approaches in early steps of drug discovery. However, in the virtual high-throughput screening (VHTS) context, where hit compounds are searched among millions of candidates, three-dimensional comparison techniques and knowledge discovery from databases should offer a better efficiency to finding novel drug leads than those of computationally expensive molecular dockings. Therefore, the present study aims at developing a filtering methodology to efficiently eliminate unsuitable compounds in VHTS process. Several filters are evaluated in this paper. The first two are structure-based and rely on either geometrical docking or pharmacophore depiction. The third filter is ligand-based and uses knowledge-based and fingerprint similarity techniques. These filtering methods were tested with the Liver X Receptor (LXR) as a target of therapeutic interest, as LXR is a key regulator in maintaining cholesterol homeostasis. The results show that the three considered filters are complementary so that their combination should generate consistent compound lists of potential hits.

Human cytomegalovirus (HCMV) contributes to pathogenic processes in immunosuppressed individuals, in fetuses, and in neonates. In the present report, by using reporter gene activation assays and confocal microscopy in the presence of a specific antagonist, we show for the first time that HCMV infection induces peroxisome proliferator-activated receptor gamma (PPARgamma) transcriptional activity in infected cells. We demonstrate that the PPARgamma antagonist dramatically impairs virus production and that the major immediate-early promoter contains PPAR response elements able to bind PPARgamma, as assessed by electrophoretic mobility shift and chromatin immunoprecipitation assays. Due to the key role of PPARgamma in placentation and its specific trophoblast expression within the human placenta, we then provided evidence that by activating PPARgamma human cytomegalovirus dramatically impaired early human trophoblast migration and invasiveness, as assessed by using well-established in vitro models of invasive trophoblast, i.e., primary cultures of extravillous cytotrophoblasts (EVCT) isolated from first-trimester placentas and the EVCT-derived cell line HIPEC. Our data provide new clues to explain how early infection during pregnancy could impair implantation and placentation and therefore embryonic development.

Motivation: Computational methods are widely used to discover gene-disease relationships hidden in vast masses of available genomic and post-genomic data. In most current methods, a similarity measure is calculated between gene annotations and known disease genes or disease descriptions. However, more explicit gene-disease relationships are required for better insights into the molecular bases of diseases, especially for complex multi-gene diseases., Results: Explicit relationships between genes and diseases are formulated as candidate gene definitions that may include intermediary genes, e.g. orthologous or interacting genes. These definitions guide data modelling in our database approach for gene-disease relationship discovery and are expressed as views which ultimately lead to the retrieval of documented sets of candidate genes. A system called ACGR (Approach for Candidate Gene Retrieval) has been implemented and tested with three case studies including a rare orphan gene disease.

Background: Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning., Results: This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results., Conclusions: The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.

Numerous methods are available for use as part of a virtual screening strategy but, as yet, no single method is able to guarantee both a level of confidence comparable to experimental screening and a level of computing efficiency that could drastically cut the costs of early phase drug discovery campaigns. Here, we present VSM-G (virtual screening manager for computational grids), a virtual screening platform that combines several structure-based drug design tools. VSM-G aims to be as user-friendly as possible while retaining enough flexibility to accommodate other in silico techniques as they are developed. In order to illustrate VSM-G concepts, we present a proof-of-concept study of a fast geometrical matching method based on spherical harmonics expansions surfaces. This technique is implemented in VSM-G as the first module of a multiple-step sequence tailored for high-throughput experiments. We show that, using this protocol, notable enrichment of the input molecular database can be achieved against a specific target, here the liver-X nuclear receptor. The benefits, limitations and applicability of the VSM-G approach are discussed. Possible improvements of both the geometrical matching technique and its implementation within VSM-G are suggested.

Aicardi syndrome (AIC) is an uncommon neurodevelopmental disorder affecting almost exclusively females. Chief features include infantile spasms, corpus callosal agenesis, and chorioretinal abnormalities. AIC is a sporadic disorder and hypothesized to be caused by heterozygous mutations in an X-linked gene but up to now without any defined candidate region on the X chromosome. Array based comparative genomic hybridisation (array-CGH) has become the method of choice for the detection of microdeletions and microduplications at high resolution. In this study, for the first time, 18 AIC patients were analyzed with a full coverage X chromosomal BAC arrays at a theoretical resolution of 82 kb. Copy number changes were validated by real-time quantitation (qPCR). No disease associated aberrations were identified. For such conditions as AIC, in which there are no familial cases, additional patients should be studied in order to identify rare cases with submicroscopic abnormalities, and to pursue a positional candidate gene approach.