TarO : a target optimisation system for structural biology

This work was funded by the UK Biotechnology and Biological Sciences Research Council (BBSRC) Structural Proteomics of Rational Targets (SPoRT) initiative, (Grant BBS/B/14434). Funding to pay the Open Access publication charges for this article was provided by BBSRC.TarO (http://www.compbio.dundee.ac.uk/taro) offers a single point of reference for key bioinformatics analyses relevant to selecting proteins or domains for study by structural biology techniques. The protein sequence is analysed by 17 algorithms and compared to 8 databases. TarO gathers putative homologues, including orthologues, and then obtains predictions of properties for these sequences including crystallisation propensity, protein disorder and post-translational modifications. Analyses are run on a high-performance computing cluster, the results integrated, stored in a database and accessed through a web-based user interface. Output is in tabulated format and in the form of an annotated multiple sequence alignment (MSA) that may be edited interactively in the program Jalview. TarO also simplifies the gathering of additional annotations via the Distributed Annotation System, both from the MSA in Jalview and through links to Dasty2. Routes to other information gateways are included, for example to relevant pages from UniProt, COG and the Conserved Domains Database. Open access to TarO is available from a guest account with private accounts for academic use available on request. Future development of TarO will include further analysis steps and integration with the Protein Information Management System (PIMS), a sister project in the BBSRC Structural Proteomics of Rational Targets initiative.Publisher PDFPeer reviewe

Molecular diagnosis in recessive pediatric neurogenetic disease can help reduce disease recurrence in families.

BackgroundThe causes for thousands of individually rare recessive diseases have been discovered since the adoption of next generation sequencing (NGS). Following the molecular diagnosis in older children in a family, parents could use this information to opt for fetal genotyping in subsequent pregnancies, which could inform decisions about elective termination of pregnancy. The use of NGS diagnostic sequencing in families has not been demonstrated to yield benefit in subsequent pregnancies to reduce recurrence. Here we evaluated whether genetic diagnosis in older children in families supports reduction in recurrence of recessive neurogenetic disease.MethodsRetrospective study involving families with a child with a recessive pediatric brain disease (rPBD) that underwent NGS-based molecular diagnosis. Prenatal molecular testing was offered to couples in which a molecular diagnosis was made, to help couples seeking to prevent recurrence. With this information, families made decisions about elective termination. Pregnancies that were carried to term were assessed for the health of child and mother, and compared with historic recurrence risk of recessive disease.ResultsBetween 2010 and 2016, 1172 families presented with a child a likely rPBD, 526 families received a molecular diagnosis, 91 families returned to the clinic with 101 subsequent pregnancies, and 84 opted for fetal genotyping. Sixty tested negative for recurrence for the biallelic mutation in the fetus, and all, except for one spontaneous abortion, carried to term, and were unaffected at follow-up. Of 24 that genotyped positive for the biallelic mutation, 16 were electively terminated, and 8 were carried to term and showed features of disease similar to that of the older affected sibling(s). Among the 101 pregnancies, disease recurrence in living offspring deviated from the expected 25% to the observed 12% ([95% CI 0·04 to 0·20], p = 0·011).ConclusionsMolecular diagnosis in an older child, coupled with prenatal fetal genotyping in subsequent pregnancies and genetic counselling, allows families to make informed decisions to reduce recessive neurogenetic disease recurrence

Multi-criteria assessment of ethical aspects in fresh tomato systems: Plant genomics technology innovation and food policy uses

Product assessment for imperceptible characteristics like environmental impact, healthfulness, naturalness, and fairness is a helpful tool in product innovation and for enhancing socially responsible conduct. In this study we apply multiple criteria analysis for the assessment of fresh tomatoes in terms of consumer perceptions regarding the above characteristics. The generated indices provide an explicit and comprehensive representation of consumer perceptions. Existing tomato products from the Dutch market are ranked alongside (reasonable conjectures of) potential products to be developed with the use of plant genomics technology. The results are interpreted to provide insights into the socially optimal use of (plant genomics) technology for fresh tomato production. Policy uses are highlighted.Ethical assessment, corporate societal responsibility, multiple criteria., Demand and Price Analysis, Research and Development/Tech Change/Emerging Technologies,

XML in Motion from Genome to Drug

Information technology (IT) has emerged as a central to the solution of contemporary genomics and drug discovery problems. Researchers involved in genomics, proteomics, transcriptional profiling, high throughput structure determination, and in other sub-disciplines of bioinformatics have direct impact on this IT revolution. As the full genome sequences of many species, data from structural genomics, micro-arrays, and proteomics became available, integration of these data to a common platform require sophisticated bioinformatics tools. Organizing these data into knowledgeable databases and developing appropriate software tools for analyzing the same are going to be major challenges. XML (eXtensible Markup Language) forms the backbone of biological data representation and exchange over the internet, enabling researchers to aggregate data from various heterogeneous data resources. The present article covers a comprehensive idea of the integration of XML on particular type of biological databases mainly dealing with sequence-structure-function relationship and its application towards drug discovery. This e-medical science approach should be applied to other scientific domains and the latest trend in semantic web applications is also highlighted

fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

Background: Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope. Results: We introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/ fDETECT/. Conclusions: fDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-ofthe-art tools and is especially suitable for the analysis of large protein sets

Multi-class protein fold classification using a new ensemble machine learning approach.

Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recent structural genomics initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. The amount of structural data has made traditional methods such as manual inspection of the protein structure become impossible. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. This work proposes a novel ensemble machine learning method that improves the coverage of the classifiers under the multi-class imbalanced sample sets by integrating knowledge induced from different base classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have compared our approach with PART and show that our method improves the sensitivity of the classifier in protein fold classification. Furthermore, we have extended this method to learning over multiple data types, preserving the independence of their corresponding data sources, and show that our new approach performs at least as well as the traditional technique over a single joined data source. These experimental results are encouraging, and can be applied to other bioinformatics problems similarly characterised by multi-class imbalanced data sets held in multiple data sources

Science and Society in Dialogue About Marker Assisted Selection

Analysis of a European Union funded biotechnology project on plant genomics and marker assisted selection in Solanaceous crops shows that the organization of a dialogue between science and society to accompany technological innovations in plant breeding faces practical challenges. Semi-structured interviews with project participants and a survey among representatives of consumer and other non-governmental organizations show that the professed commitment to dialogue on science and biotechnology is rather shallow and has had limited application for all involved. Ultimately, other priorities tend to prevail because of high workload. The paper recommends including results from previous debates and input from societal groups in the research design phase (prior to communication), to use appropriate media to disseminate information and to make explicit how societal feedback is used in research, in order to facilitate true dialogue between science and society on biotechnology

A Scientific Roadmap for Antibiotic Discovery: A Sustained and Robust Pipeline of New Antibacterial Drugs and Therapies is Critical to Preserve Public Health

In recent decades, the discovery and development of new antibiotics have slowed dramatically as scientific barriers to drug discovery, regulatory challenges, and diminishing returns on investment have led major drug companies to scale back or abandon their antibiotic research. Consequently, antibiotic discovery—which peaked in the 1950s—has dropped precipitously. Of greater concern is the fact that nearly all antibiotics brought to market over the past 30 years have been variations on existing drugs. Every currently available antibiotic is a derivative of a class discovered between the early 1900s and 1984.At the same time, the emergence of antibiotic-resistant pathogens has accelerated, giving rise to life-threatening infections that will not respond to available antibiotic treatment. Inevitably, the more that antibiotics are used, the more that bacteria develop resistance—rendering the drugs less effective and leading public health authorities worldwide to flag antibiotic resistance as an urgent and growing public health threat

Just how difficult can it be counting up R&D funding for emerging technologies (and is tech mining with proxy measures going to be any better?)

Decision makers considering policy or strategy related to the development of emerging technologies expect high quality data on the support for different technological options. A natural starting point would be R&D funding statistics. This paper explores the limitations of such aggregated data in relation to the substance and quantification of funding for emerging technologies. Using biotechnology as an illustrative case, we test the utility of a novel taxonomy to demonstrate the endemic weaknesses in the availability and quality of data from public and private sources. Using the same taxonomy, we consider the extent to which tech-mining presents an alternative, or potentially complementary, way to determine support for emerging technologies using proxy measures such as patents and scientific publications