Constraint-based knowledge discovery from SAGE data

Current analyses of co-expressed genes are often based on global approaches such as clustering or bi-clustering. An alternative way is to employ local methods and search for patterns--sets of genes displaying specific expression properties in a set of situations. The main bottleneck of this type of analysis is twofold--computational costs and an overwhelming number of candidate patterns which can hardly be further exploited. A timely application of background knowledge available in literature databases, biological ontologies and other sources can help to focus on the most plausible patterns only. The paper proposes, implements and tests a flexible constraint-based framework that enables the effective mining and representation of meaningful over-expression patterns representing intrinsic associations among genes and biological situations. The framework can be simultaneously applied to a wide spectrum of genomic data and we demonstrate that it allows to generate new biological hypotheses with clinical implications.     

Drug databases and their contributions to drug repurposing

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.     

Organisation of biotechnological information into knowledge

The success of biotechnological research, development and marketing depends to a large extent on the international transfer of information and on the ability to organise biotechnology information into knowledge. To increase the efficiency of information-based approaches, an information strategy has been developed and consists of the following stages: definition of the problem, its structure and sub-problems; acquisition of data by targeted processing of computer-supported bibliographic, numeric, textual and graphic databases; analysis of data and building of specialized in-house information systems; information processing for structuring data into systems, recognition of trends and patterns of knowledge, particularly by information synthesis using the concept of information density; design of research hypotheses; testing hypotheses in the laboratory and/or pilot plant; repeated evaluation and optimization of hypotheses by information methods and testing them by further laboratory work. The information approaches are illustrated by examples from the university-industry joint projects in biotechnology, biochemistry and agriculture.The author is with the International Center for Chemical Studies, Faculty of Science and Technology, University of Ljubljana FNT-Kü Vegova 4. P.O. Box 18/1, 61001 Ljubljana, Slovenia     

Integrated bio-entity network: a system for biological knowledge discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein-protein interactions, protein/gene regulations, protein-small molecule interactions, protein-GO relationships, protein-pathway relationships, and pathway-disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses--the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs.     

Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.     

Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation

MOTIVATION: Many bioinformatics data resources not only hold data in the form of sequences, but also as annotation. In the majority of cases, annotation is written as scientific natural language: this is suitable for humans, but not particularly useful for machine processing. Ontologies offer a mechanism by which knowledge can be represented in a form capable of such processing. In this paper we investigate the use of ontological annotation to measure the similarities in knowledge content or 'semantic similarity' between entries in a data resource. These allow a bioinformatician to perform a similarity measure over annotation in an analogous manner to those performed over sequences. A measure of semantic similarity for the knowledge component of bioinformatics resources should afford a biologist a new tool in their repertoire of analyses. RESULTS: We present the results from experiments that investigate the validity of using semantic similarity by comparison with sequence similarity. We show a simple extension that enables a semantic search of the knowledge held within sequence databases. AVAILABILITY: Software available from http://www.russet.org.uk.     

The complexity of comparing reaction systems

MOTIVATION: As more genomic data becomes available there is increased attention on understanding the mechanisms encoded in the genome. New XML dialects like CellML and Systems Biology Markup Language (SBML) are being developed to describe biological networks of all types. In the absence of detailed kinetic information for these networks, stoichiometric data is an especially valuable source of information. Network databases are the next logical step beyond storing purely genomic information. Just as comparison of entries in genomic databases has been a vital algorithmic problem through the course of the sequencing project, comparison of networks in network databases will be a crucial problem as we seek to integrate higher-order network knowledge. RESULTS: We show that comparing the stoichiometric structure of two reactions systems is equivalent to the graph isomorphism problem. This is encouraging because graph isomorphism is, in practice, a tractable problem using heuristics. The analogous problem of searching for a subsystem of a reaction system is NP-complete. We also discuss heuristic issues in implementations for practical comparison of stoichiometric matrices.     

RNAcentral: A vision for an international database of RNA sequences

During the last decade there has been a great increase in the number of noncoding RNA genes identified, including new classes such as microRNAs and piRNAs. There is also a large growth in the amount of experimental characterization of these RNA components. Despite this growth in information, it is still difficult for researchers to access RNA data, because key data resources for noncoding RNAs have not yet been created. The most pressing omission is the lack of a comprehensive RNA sequence database, much like UniProt, which provides a comprehensive set of protein knowledge. In this article we propose the creation of a new open public resource that we term RNAcentral, which will contain a comprehensive collection of RNA sequences and fill an important gap in the provision of biomedical databases. We envision RNA researchers from all over the world joining a federated RNAcentral network, contributing specialized knowledge and databases. RNAcentral would centralize key data that are currently held across a variety of databases, allowing researchers instant access to a single, unified resource. This resource would facilitate the next generation of RNA research and help drive further discoveries, including those that improve food production and human and animal health. We encourage additional RNA database resources and research groups to join this effort. We aim to obtain international network funding to further this endeavor.     

Tetrameric structure of a serine integrase catalytic domain

The serine integrases have recently emerged as powerful new chromosome engineering tools in various organisms and show promise for therapeutic use in human cells. The serine integrases are structurally and mechanistically unrelated to the bacteriophage lambda integrase but share a similar catalytic domain with the resolvase/invertase enzymes typified by the resolvase proteins from transposons Tn3 and gammadelta. Here we report the crystal structure and solution properties of the catalytic domain from bacteriophage TP901-1 integrase. The protein is a dimer in solution but crystallizes as a tetramer that is closely related in overall architecture to structures of activated gammadelta-resolvase mutants. The ability of the integrase tetramer to explain biochemical experiments performed in the resolvase and invertase systems suggests that the TP901 integrase tetramer represents a unique intermediate on the recombination pathway that is shared within the serine recombinase superfamily.     

EcoProDB: the Escherichia coli protein database

EcoProDB is a web-based database for comparative proteomics of Escherichia coli. The database contains information on E. coli proteins identified on 2D gels along with other resources collected from various databases and published literature, with a special feature of showing the expression levels of E. coli proteins under different genetic and environmental conditions. It also provides comparative information of subcellular localization, theoretical 2D map, experimental 2D map and integrated protein information via an interactive web interface and application such as the Map Browser. Users can also upload their own 2D gels, extract core information associated with the proteins and 2D gel results from different experiments and consequently generate new knowledge and hypotheses for further studies. Availability: EcoProDB database system is accessible at http://eecoli.kaist.ac.kr.     

Structural dynamics of full-length retroviral integrase: a molecular dynamics analysis

HIV integrase catalyzes the integration between host and viral DNA and is considered as an interesting target for treating HIV. Knowledge of the complete structure of integrase is inevitable to describe the communicative inter-domain interactions affecting the HIV integration and disintegration process and hence the study on full-length integrase turns out to be an essential task. In this investigation, a structure of full-length integrase is designed to analyze the global dynamics of integrase dimer and monomers (with and without the C-terminal, 270-288 amino acids) for a period of 20?ns. The molecular dynamics analysis and the subsequent DynDom analysis reveal (i) a stable dynamics of dimeric CCD and NTD domains and (ii) CCD-α11-mediated rotational-cum-translational CTD motion as the functional dynamics of IN dimer. This observation supports that (i) aggregation enhances the integrase activity and (ii) flexible CTD for its cis and trans coordination with CCD. The role of C-loop over the dynamics of integrase is also explored, which unveils that the spatial arrangement of integrase domains is changed during dynamics in the absence of C-loop. In essence, here we report a C-loop-dependent structural dynamics of integrase and the active dynamics of integrase in dimer. Further studies on C-loop sensing mechanism and the multimerization of integrase would provide insight into HIV integration and disintegration processes. Supplementary material. Movies generated from molecular dynamics trajectory showing the CTD dynamics of IN structures (monomers with & without C-loop and dimer) are linked online to this article. The remaining supplementary data can be downloaded from the author's server at the URL http://ramutha.bicpu.edu.in .     

Substrate features important for recognition and catalysis by human immunodeficiency virus type 1 integrase identified by using novel DNA substrates.

The integrase encoded by human immunodeficiency virus type 1 (HIV-1) is required for integration of viral DNA into the host cell chromosome. In vitro, integrase mediates a concerted cleavage-ligation reaction (strand transfer) that results in covalent attachment of viral DNA to target DNA. With a substrate that mimics the strand transfer product, integrase carries out disintegration, the reverse of the strand transfer reaction, resolving this integration intermediate into its viral and target DNA parts. We used a set of disintegration substrates to study the catalytic mechanism of HIV-1 integrase and the interaction between the protein and the viral and target DNA sequence. One substrate termed dumbbell consists of a single oligonucleotide that can fold to form a structure that mimics the integration intermediate. Kinetic analysis using the dumbbell substrate showed that integrase turned over, establishing that HIV-1 integrase is an enzyme. Analysis of the disintegration activity on the dumbbell substrate and its derivatives showed that both the viral and target DNA parts of the molecule were required for integrase recognition. Integrase recognized target DNA asymmetrically: the target DNA upstream of the viral DNA joining site played a much more important role than the downstream target DNA in protein-DNA interaction. The site of transesterification was determined by both the DNA sequence of the viral DNA end and the structure of the branched substrate. Using a series of disintegration substrates with various base modifications, we found that integrase had relaxed structural specificity for the hydroxyl group used in transesterification and could tolerate distortion of the double-helical structure of these DNA substrates.     

Stability-activity relationships of a family of G-tetrad forming oligonucleotides as potent HIV inhibitors. A basis for anti-HIV drug design

Recently, we have demonstrated that T30695, a G-tetrad-forming oligonucleotide, is a potent inhibitor of human immunodeficiency virus, type I (HIV-1) integrase and the K(+)-induced loop folding of T30695 plays a key role in the inhibition of HIV-1 integrase (Jing, N., and Hogan, M. E. (1998) J. Biol. Chem. 273, 34992-34999). Here we have modified T30695 by introducing a hydrophobic bulky group, propynyl dU, or a positively charged group, 5-amino dU, into the bases of T residues of the loops, and by substitution of the T-G loops by T-T loops. Physical measurements have demonstrated that the substitution of propynyl dU or 5-amino dU for T in the T residues of the loops did not alter the structure of T30695, and these derivatives also formed an intramolecular G-quartet structure, which is an essential requirement for anti-HIV activity. Measured IC(50) and EC(50) values show that these substitutions did not induce an apparent decrease in the ability to inhibit HIV-1 integrase activity and in the inhibition of HIV-1 replication in cell culture. However, the substitution of T-T loops for T-G loops induced a substantial decrease in both thermal stability and anti-HIV activity. The data analysis of T30695 and the 21 derivatives shows a significant, functional correlation between thermal stability of the G-tetrad structure and the capacity to inhibit HIV-1 integrase activity and between thermal stability of the G-tetrad structure and the capacity to inhibit HIV-1 replication, as assessed with the virus strains HIV-1 RF, IIIB, and MN in cell culture. This relationship between thermostability and activity provides a basis for improving the efficacy of these compounds to inhibit HIV-1 integrase activity and HIV-1 replication in cell culture.     

Connecting environment and genome plasticity in the characterization of transformation-induced SOS regulation and carbon catabolite control of the Vibrio cholerae integron integrase

The human pathogen Vibrio cholerae carries a chromosomal superintegron (SI). The SI contains an array of hundreds of gene cassettes organized in tandem which are stable under conditions when no particular stress is applied to bacteria (such as during laboratory growth). Rearrangements of these cassettes are catalyzed by the activity of the associated integron integrase. Understanding the regulation of integrase expression is pivotal to fully comprehending the role played by this genetic reservoir for bacterial adaptation and its connection with the development of antibiotic resistance. Our previous work established that the integrase is regulated by the bacterial SOS response and that it is induced during bacterial conjugation. Here, we show that transformation, another horizontal gene transfer (HGT) mechanism, also triggers integrase expression through SOS induction, underlining the importance of HGT in genome plasticity. Moreover, we report a new cyclic AMP (cAMP)-cAMP receptor protein (CRP)-dependent regulation mechanism of the integrase, highlighting the influence of the extracellular environment on chromosomal gene content. Altogether, our data suggest an interplay between different stress responses and regulatory pathways for the modulation of the recombinase expression, thus showing how the SI remodeling mechanism is merged into bacterial physiology.     

COVID19 Disease Map,a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.