Tally-2.0: upgraded validator of tandem repeat detection in protein sequences.

MOTIVATION: Proteins containing tandem repeats (TRs) are abundant, frequently fold in elongated non-globular structures and perform vital functions. A number of computational tools have been developed to detect TRs in protein sequences. A blurred boundary between imperfect TR motifs and non-repetitive sequences gave rise to necessity to validate the detected TRs. RESULTS: Tally-2.0 is a scoring tool based on a machine learning (ML) approach, which allows to validate the results of TR detection. It was upgraded by using improved training datasets and additional ML features. Tally-2.0 performs at a level of 93% sensitivity, 83% specificity and an area under the receiver operating characteristic curve of 95%. AVAILABILITY AND IMPLEMENTATION: Tally-2.0 is available, as a web tool and as a standalone application published under Apache License 2.0, on the URL https://bioinfo.crbm.cnrs.fr/index.php? route=tools&tool=27. It is supported on Linux. Source code is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis.

SUMMARY: Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design. AVAILABILITY AND IMPLEMENTATION: DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automated feature engineering improves prediction of protein-protein interactions.

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php .

DisProt 7.0: a major update of the database of disordered proteins.

The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins.

TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation.

The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions. AVAILABILITY AND IMPLEMENTATION: http://www.vin.bg.ac.rs/180/tools/tfpred.php CONTACT: vladaper@vinca.rs; nevenav@vinca.rsSupplementary information: Supplementary data are available at Bioinformatics online.

Genetic Markers for Coronary Artery Disease.

Coronary artery disease (CAD) and myocardial infarction (MI) are recognized as leading causes of mortality in developed countries. Although typically associated with behavioral risk factors, such as smoking, sedentary lifestyle, and poor dietary habits, such vascular phenotypes have also long been recognized as being related to genetic background. We review the currently available data concerning genetic markers for CAD in English and non-English articles with English abstracts published between 2003 and 2018. As genetic testing is increasingly available, it may be possible to identify adequate genetic markers representing the risk profile and to use them in a clinical setting.

A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin.

The clonidine-like central antihypertensive agent rilmenidine, which has high affinity for I1-type imidazoline receptors (I1-IR) was recently found to have cytotoxic effects on cultured cancer cell lines. However, due to its pharmacological effects resulting also from α2-adrenoceptor activation, rilmenidine cannot be considered a suitable anticancer drug candidate. Here, we report the identification of novel rilmenidine-derived compounds with anticancer potential and devoid of α2-adrenoceptor effects by means of ligand- and structure-based drug design approaches. Starting from a large virtual library, eleven compounds were selected, synthesized and submitted to biological evaluation. The most active compound 5 exhibited a cytotoxic profile similar to that of rilmenidine, but without appreciable affinity to α2-adrenoceptors. In addition, compound 5 significantly enhanced the apoptotic response to doxorubicin, and may thus represent an important tool for the development of better adjuvant chemotherapeutic strategies for doxorubicin-insensitive cancers.

Feature-based classification of amino acid substitutions outside conserved functional protein domains.

There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study.

INTRODUCTION: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. OBJECTIVE: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. METHODS: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. RESULTS: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. CONCLUSIONS: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.

Cytoplasmatic compartmentalization by Bcr-Abl promotes TET2 loss-of-function in chronic myeloid leukemia.

The loss-of-function of ten-eleven-translocation (TET) 2, a Fe(2+) -oxoglutarate-dependent dioxygenase catalyzing 5 methyl cytosine (5mC) conversion into 5-hydroxymethylcytosine (5hmC), contributes to the hematopoietic transformation in vivo. The aim of our study was to elucidate its role in the phenotype of chronic myeloid leukemia (CML), a myeloproliferative disease caused by the Bcr-Abl rearranged gene. We first confirmed TET2 interaction with the Bcr-Abl protein predicted by a Fourier-based bioinformatic method. Such interaction led to TET2 cytoplasmatic compartmentalization in a complex tethered by the fusion protein tyrosine kinase (TK) and encompassing the Forkhead box O3a (FoxO3a) transcription factor. We then focused the impact of TET2 loss-of-function on epigenetic transcriptional regulation of Bcl2-interacting mediator (BIM), a pro-apoptotic protein transcriptionally regulated by FoxO3a. BIM downregulation is a critical component of CML progenitor extended survival and is also involved in the disease resistance to imatinib (IM). Here we reported that TET2 release from Bcr-Abl protein following TK inhibition in response to IM triggers a chain of events including TET2 nuclear translocation, re-activation of its enzymatic function at 5mC and recruitment at the BIM promoter followed by BIM transcriptional induction. 5hmC increment following TET2 re-activation was associated with the reduction of histone H3 tri-methylation at lysine 9 (H3K9me3), which may contribute with DNA de-methylation reported elsewhere to recast a permissive epigenetic "landscape" for FoxO3a transcriptional activity.

14-3-3 ligand prevents nuclear import of c-ABL protein in chronic myeloid leukemia.

Here we demonstrated that the 'loss of function' of not-rearranged c-ABL in chronic myeloid leukemia (CML) is promoted by its cytoplasmic compartmentalization bound to 14-3-3 sigma scaffolding protein. In particular, constitutive tyrosine kinase (TK) activity of p210 BCR-ABL blocks c-Jun N-terminal kinase (JNK) phosphorylation leading to 14-3-3 sigma phosphorylation at a critical residue (Ser(186)) for c-ABL binding in response to DNA damage. Moreover, it is associated with 14-3-3 sigma over-expression arising from epigenetic mechanisms (promoter hyper-acetylation). Accordingly, p210 BCR-ABL TK inhibition by the TK inhibitor Imatinib mesylate (IM) evokes multiple events, including JNK phosphorylation at Thr(183), p38 mitogen-activated protein kinase (MAPK) phosphorylation at Thr(180), c-ABL de-phosphorylation at Ser residues involved in 14-3-3 binding and reduction of 14-3-3 sigma expression, that let c-ABL release from 14-3-3 sigma and nuclear import, and address BCR-ABL-expressing cells towards apoptotic death. Informational spectrum method (ISM), a virtual spectroscopy method for analysis of protein interactions based on their structure, and mathematical filtering in cross spectrum (CS) analysis identified 14-3-3 sigma/c-ABL binding sites. Further investigation on CS profiles of c-ABL- and p210 BCR-ABL-containing complexes revealed the mechanism likely involved 14-3-3 precluded phosphorylation in CML cells.

Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies.

For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm-Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.

The first insight into the genetic structure of the population of modern Serbia.

The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever targeted sequencing dataset of variants in clinically relevant genes. By measuring population differentiation and applying the Principal Component and Admixture analysis we demonstrated that the Serbian population differs little from other European populations, yet we identified several novel and more frequent variants that appear as its unique genetic determinants. We explored thoroughly the functional impact of frequent variants and its correlation with the health burden of the population of Serbia based on a sample of 144 individuals. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to research on ancestry, and aids in improvements of well-being and health equity. In addition, this resource may also be applicable in neighboring regions and valuable in worldwide functional analyses of genetic variants in individuals of European descent.

Meta-Analysis of Circulating Cell-Free DNA's Role in the Prognosis of Pancreatic Cancer.

INTRODUCTION: The analysis of cell-free DNA (cfDNA) for genetic abnormalities is a promising new approach for the diagnosis and prognosis of pancreatic cancer patients. Insights into the molecular characteristics of pancreatic cancer may provide valuable information, leading to its earlier detection and the development of targeted therapies. MATERIAL AND METHODS: We conducted a systematic review and a meta-analysis of studies that reported cfDNA in pancreatic ductal adenocarcinoma (PDAC). The studies were considered eligible if they included patients with PDAC, if they had blood tests for cfDNA/ctDNA, and if they analyzed the prognostic value of cfDNA/ctDNA for patients' survival. The studies published before 22 October 2020 were identified through the PubMED, EMBASE, Web of Science and Cochrane Library databases. The assessed outcomes were the overall (OS) and progression-free survival (PFS), expressed as the log hazard ratio (HR) and standard error (SE). The summary of the HR effect size was estimated by pooling the individual trial results using the Review Manager, version 5.3, Cochrane Collaboration. The heterogeneity was assessed using the Cochran Q test and I2 statistic. RESULTS: In total, 48 studies were included in the qualitative review, while 44 were assessed in the quantitative synthesis, with the total number of patients included being 3524. Overall negative impacts of cfDNA and KRAS mutations on OS and PFS in PDAC (HR = 2.42, 95% CI: 1.95-2.99 and HR = 2.46, 95% CI: 2.01-3.00, respectively) were found. The subgroup analysis of the locally advanced and metastatic disease presented similar results (HR = 2.51, 95% CI: 1.90-3.31). In the studies assessing the pre-treatment presence of KRAS, there was a moderate to high degree of heterogeneity (I2 = 87% and I2 = 48%, for OS and PFS, respectively), which was remarkably decreased in the analysis of the studies measuring post-treatment KRAS (I2 = 24% and I2 = 0%, for OS and PFS, respectively). The patients who were KRAS positive before but KRAS negative after treatment had a better prognosis than the persistently KRAS-positive patients (HR = 5.30, 95% CI: 1.02-27.63). CONCLUSION: The assessment of KRAS mutation by liquid biopsy can be considered as an additional tool for the estimation of the disease course and outcome in PDAC patients.

Computational studies of the interaction between the HIV-1 integrase tetramer and the cofactor LEDGF/p75: insights from molecular dynamics simulations and the informational spectrum method.

A crystal structure of the integrase binding domain (IBD) of the lens epithelium-derived growth factor (LEDGF/p75) in complex with the dimer of the HIV-1 integrase (IN) catalytic core domain (CCD) provides useful information that might help in the understanding of essential protein-protein contacts in HIV-1. However, mutagenic studies indicated that interactions between the full-length proteins were more extensive than the contacts observed in the co-crystal structure of the isolated domains. On the other hand, the biochemical characterization of the interaction between full-length IN and LEDGF/p75 has recently proved that LEDGF/p75 promotes IN tetramerization with two LEDGF/p75 IBD molecules bound to the IN tetramer. This experimental evidence suggests that to obtain a complete structural description of the interactions between the two proteins, the full-length tetrameric structure of IN should be considered. Our aim was to obtain a detailed picture of HIV-1 IN interactions with cellular co-factors that was of general interest, particularly for the development of small molecule IN inhibitors, which mimic the IBD of LEDGF/p75. To this end, we performed bioinformatics analyses to identify protein sequence domains involved in long-range recognition. Subsequently, we applied molecular dynamics techniques to investigate the detailed interactions between the complete tetrameric form of IN and two molecules of the IBD of LEDGF/p75. Our dynamic picture is in agreement with experimental data and, thereby, provides new details of the IN-LEDGF/p75 interaction.

Deciphering Imidazoline Off-targets by Fishing in the Class A of GPCR field.

Based on the finding that a central antihypertensive agent with high affinity for I1-type imidazoline receptors - rilmenidine, shows cytotoxic effects on cultured cancer cell lines, it has been suggested that imidazoline receptors agonists might have a therapeutic potential in the cancer therapy. Nevertheless, potential rilmenidine side effects caused by activation of α-adrenoceptors, or other associated receptors and enzymes, might hinder its therapeutic benefits. Considering that human α-adrenoceptors belong to the rhodopsin-like class A of G-protein-coupled receptors (GPCRs) it is reasonable to assume that imidazolines might have the affinity for other receptors from the same class. Therefore, to investigate possible off-target effects of imidazoline ligands we have prepared a reverse docking protocol on class A GPCRs, using imidazoline ligands and their decoys. To verify our in silico results, three ligands with high scores and three ligands with low scores were tested for antagonistic activity on α2 - adrenoceptors.

Identification of hemagglutinin structural domain and polymorphisms which may modulate swine H1N1 interactions with human receptor.

BACKGROUND: The novel A/H1N1 influenza virus, which recently emerged in North America is most closely related to North American H1N1/N2 swine viruses. Until the beginning of 2009, North American swine H1N1/N2 viruses have only sporadically infected humans as dead-end hosts. In 2009 the A/H1N1 virus acquired the capacity to spread efficiently by human to human transmission. The novel A/H1N1 influenza virus has struck thousands of people in more than 70 countries and killed more than 140, representing a public health emergency of international concern. Here we have studied properties of hemagglutinin of A/H1N1 which may modulate virus/receptor interaction. RESULTS: Analyses by ISM bioinformatics platform of the HA1 protein of North American swine H1N1/N2 viruses and the new A/H1N1 showed that both groups of viruses differed in conserved characteristics that reflect a distinct propensity of these viruses to undergo a specific interaction with swine or human host proteins or receptors. Swine H1N1/N2 viruses that sporadically infected humans featured both the swine and the human interaction pattern. Substitutions F71S, T128S, E302K, M314L in HA1 of swine H1N1 viruses from North America are identified as critical for the human interaction pattern of A/H1N1 and residues D94, D196 and D274 are predicted to be "hot-spots" for polymorphisms which could increase infectivity of A/H1N1 virus. At least one of these residues has already emerged in the A/H1N1 isolates from Spain, Italy and USA. The domain 286-326 was identified to be involved in virus/receptor interaction. CONCLUSION: Our results (i) contribute to better understanding of the origin of the novel A/H1N1 influenza virus, (ii) provide a tool for monitoring its molecular evolution (iii) predicts hotspots associated with enhanced infectivity in humans and (iv) identify therapeutic and diagnostic targets for prevention and treatment of A/H1N1 infection.

Characterization of conserved properties of hemagglutinin of H5N1 and human influenza viruses: possible consequences for therapy and infection control.

BACKGROUND: Epidemics caused by highly pathogenic avian influenza virus (HPAIV) are a continuing threat to human health and to the world's economy. The development of approaches, which help to understand the significance of structural changes resulting from the alarming mutational propensity for human-to-human transmission of HPAIV, is of particularly interest. Here we compare informational and structural properties of the hemagglutinin (HA) of H5N1 virus and human influenza virus subtypes, which are important for the receptor/virus interaction. RESULTS: Presented results revealed that HA proteins encode highly conserved information that differ between influenza virus subtypes H5N1, H1N1, H3N2, H7N7 and defined an HA domain which may modulate interaction with receptor. We also found that about one third of H5N1 viruses which are isolated during the 2006/07 influenza outbreak in Egypt possibly evolve towards receptor usage similar to that of seasonal H1N1. CONCLUSION: The presented results may help to better understand the interaction of influenza virus with its receptor(s) and to identify new therapeutic targets for drug development.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes.

BACKGROUND: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.