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 .

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.

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.

Biochemical changes in cancer cells induced by photoactive nanosystem based on carbon dots loaded with Ru-complex.

Carbon dots (CDs) and N-carbon dots (N-CDs) loaded with Ru-complex (CDs@RuCN, N-CDs@RuCN, respectively) were investigated as media imposing biochemical changes induced by UV illumination of ovarian cancer, A2780, and osteosarcoma, CAL72, cells. Synchrotron radiation-based Fourier Transform Infrared Spectroscopy was performed, and the spectra were subjected to a Principal Component Analysis. The CDs@RuCN and N-CDs@RuCN effects on cancer cells were analyzed by the theoretical modelling of the stability of the composite systems and a protein database search. Moreover, a detailed evaluation of surface and optical properties of CDs@RuCN and N-CDs@RuCN was carried out. Results demonstrated selective action of the CDs@RuCN and N-CDs@RuCN-based photodynamic therapy, with N-CDs@RuCN being the most active in inducing changes in A2780 and CDs@RuCN in CAL72 cells. We assume that different surface charges of nanoparticles led to direct interactions of N-CDs@RuCN with a Wnt signalling pathway in A2780 and those of CDs@RuCN with PI3-K/Akt in CAL72 cells and that further biochemical changes occurred upon light illumination.

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.

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.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.

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.

Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells.

Imidazoline I1 receptor signaling is associated with pathways that regulate cell viability leading to varied cell-type specific phenotypes. We demonstrated that the antihypertensive drug rilmenidine, a selective imidazoline I1 receptor agonist, modulates proliferation and stimulates the proapoptotic protein Bax thus inducing the perturbation of the mitochondrial pathway and apoptosis in human leukemic K562 cells. Rilmenidine acts through a mechanism which involves deactivation of Ras/MAP kinases ERK, p38 and JNK. Moreover, rilmenidine renders K562 cells, which are particularly resistant to chemotherapeutic agents, susceptible to the DNA damaging drug doxorubicin. The rilmenidine co-treatment with doxorubicin reverses G2/M arrest and triggers apoptotic response to DNA damage. Our data offer new insights into the pathways associated with imidazoline I1 receptor activation in K562 cells suggesting rilmenidine as a valuable tool to deepen our understanding of imidazoline I1 receptor signaling in hematologic malignancies and to search for medicinally active agents.

Natural Products as Promising Therapeutics for Treatment of Influenza Disease.

The influenza virus represents a permanent global health threat because it circulates not only within but also between numerous host populations, thereby frequently causing unexpected outbreaks in animals and humans with a generally unpredictable course of disease and epidemiology. Conventional influenza therapy is directed against the viral neuraminidase protein, which promotes virus release from infected cells, and the viral ion channel M2, which facilitates viral uncoating. However, these drugs, albeit effective, have a major drawback: their targets are of a highly variable sequence. As a consequence, the virus can readily acquire resistance by mutating the drug targets. Indeed, most seasonal A/H1N1 viruses and the 2009 H1N1 virus are resistant to M2 inhibitors, and a significant proportion of the seasonal A/H1N1 viruses are resistant to the neuraminidase inhibitor oseltamivir. Development of new effective drugs for treatment of disease during the regular influenza seasons and the possible influenza pandemic represents an important goal. The results presented here point out natural products as a promising source of low toxic and widely accessible drug candidates for treatment of the influenza disease. Natural products combined with new therapeutic targets and drug repurposing techniques, which accelerate development of new drugs, serve as an important platform for development of new influenza therapeutics.

Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120.

BACKGROUND/AIM: High sera reactivity with a peptide derived from human immunodeficiency virus HIV-1 envelope protein gpl20, NTM1, correlate with non-progressive HIV-1 infection and also may have protective role in breast and prostate cancer. We also detected a low NTM1 reactive antibodies titer in healthy HIV negative sera and showed that antibody levels can be significantly increased with vigorous physical activity. However, the immune system seems to be unresponsive or tolerant to this peptide, implicating that the NTM1 sequence encompasses or overlaps a certain innate immune epitope. The aim of this study was to present evidences that NTM1 - binding antibodies - are components of innate immune humoral response, by confirming their presence in sera of newborn babies. For this purpose we collected a set of 225 innate antigen sequences reported in the literature and screened it for candidate antigens with the highest sequence and spectral similarity to NTM1 derived from HIV-1 gp120. METHODS: Sera from 18 newborns were tested using ELISA, with peptide NTM1. Sequences from innate antigen database were aligned by an EMBOSS Water bioinformatics tool. RESULTS: We identified NTM1 reactive antibodies in sera of HIV negative newborn babies. Further, in order to identify which of already known innate antigens are the most similar to NTM1 peptide we screened innate immune antigen sequence database collected from the literature. This screening revealed that the most similar sequence are ribonucleoproteins RO60, in addition to previously identified N-terminus of vasoactive intestinal peptide. CONCLUSION: The results of this study confirm the hypothesis that NTM1 recognizing antibodies are a part of humoral innate immune response. Further, computational similarity screening revealed a vasoactive intestinal peptide and RO60 as the most similar sequences and the strongest candidate antigens. In the light of the presented results, it is appealing that testing blood reactivity at birth, with specific innate antigens, particularly a vasoactive intestinal peptide, can reveal the potential to develop or boost protective immune response in breast and prostate cancer and HIV infection later in life.

Imidazoline-1 receptor ligands as apoptotic agents: pharmacophore modeling and virtual docking study.

The group of imidazoline-1 receptors (I(1)-IR) agonists encompasses drugs are currently used in treatment of high blood pressure and hyperglycemia. The I(1)-IR protein structures have not been determined yet, but Nischarin protein that binds numerous imidazoline ligands inducing initiation of various cell-signaling cascades, including apoptosis, is identified as strong I(1)-IR candidate. In this study we examined apoptotic activity of rilmenidine (potent I(1)-IR agonist), moxonidine (moderate I(1)-IR agonist), and efaroxan (I(1)-IR partial agonist) on cancer cell line (K562) expressing Nischarin. The Nischarine domains mapping was performed by use of the Informational Spectrum Method (ISM). The 3D-Quantitative Structure-Activity Relationship (3D-QSAR) and virtual docking studies of 29 I(1)-IR ligands (agonists, partial agonists, and antagonists) were carried out on I(1)-IR receptors binding affinities. The 3D-QSAR study defined 3D-pharmacophore models for I(1)-IR agonistic and I(1)-IR antagonistic activity and created regression model for prediction of I(1)-IR activity of novel compounds. The 3D-QSAR models were applied for design and evaluation of novel I(1)-IR agonists and I(1)-IR antagonists. The most promising I(1)-IR ligands with enhanced activities than parent compounds were proposed for synthesis. The results of 3D-QSAR, ISM, and virtual docking studies were in perfect agreement and allowed precise definition of binding mode of I(1)-IR agonists (Arg 758, Arg 866, Val 981, and Glu 1057) and significantly different binding modes of I(1)-IR antagonists or partial I(1)-IR agonists. The performed theoretical study provides reliable system for evaluation of I(1)-IR agonistic and I(1)-IR antagonistic activity of novel I(1)-IR ligands, as drug candidates with anticancer activities.

Assessment of hepatitis C virus protein sequences with regard to interferon/ribavirin combination therapy response in patients with HCV genotype 1b.

Hepatitis C virus (HCV) infection is a major and rising global health problem, affecting about 170 million people worldwide. The current standard of care treatment with interferon alpha and ribavirin in patients with the genotype 1 infection, the most frequent genotype in the USA and Western Europe, leads to a successful outcome in only about 50% of individuals. Accurate prediction of hepatitis C treatment response is of great benefit to patients and clinicians. The informational spectrum method, a virtual spectroscopy method for structure/function analysis of nucleotide and protein sequences, is applied here for the identification of the conserved information of the HCV proteins that correlate with the combination therapy outcome. Among the HCV proteins that we have analyzed the informational property of the p7 of HCV genotype 1b was best related to the therapy outcome. On the basis of these results, a simple bioinformatics criterion that could be useful in assessment of the response of HCV-infected patients to the combination therapy has been proposed.