Showing papers by "Philip E. Bourne published in 2012"

The RCSB Protein Data Bank: new resources for research and education

Abstract: The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) develops tools and resources that provide a structural view of biology for research and education. The RCSB PDB web site (http://www.rcsb.org) uses the curated 3D macromolecular data contained in the PDB archive to offer unique methods to access, report and visualize data. Recent activities have focused on improving methods for simple and complex searches of PDB data, creating specialized access to chemical component data and providing domain-based structural alignments. New educational resources are offered at the PDB-101 educational view of the main web site such as Author Profiles that display a researcher’s PDB entries in a timeline. To promote different kinds of access to the RCSB PDB, Web Services have been expanded, and an RCSB PDB Mobile application for the iPhone/iPad has been released. These improvements enable new opportunities for analyzing and understanding structure data.

Immune epitope database analysis resource

Abstract: The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.

Novel computational approaches to polypharmacology as a means to define responses to individual drugs.

Abstract: Polypharmacology, which focuses on designing therapeutics to target multiple receptors, has emerged as a new paradigm in drug discovery. Polypharmacological effects are an attribute of most, if not all, drug molecules. The efficacy and toxicity of drugs, whether designed as single- or multitarget therapeutics, result from complex interactions between pharmacodynamic, pharmacokinetic, genetic, epigenetic, and environmental factors. Ultimately, to predict a drug response phenotype, it is necessary to understand the change in information flow through cellular networks resulting from dynamic drug-target interactions and the impact that this has on the complete biological system. Although such is a future objective, we review recent progress and challenges in computational techniques that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes.

BioJava: an open-source framework for bioinformatics in 2012

Abstract: Motivation: BioJava is an open-source project for processing of biological data in the Java programming language. We have recently released a new version (3.0.5), which is a major update to the code base that greatly extends its functionality. Results: BioJava now consists of several independent modules that provide state-of-the-art tools for protein structure comparison, pairwise and multiple sequence alignments, working with DNA and protein sequences, analysis of amino acid properties, detection of protein modifications and prediction of disordered regions in proteins as well as parsers for common file formats using a biologically meaningful data model. Availability: BioJava is an open-source project distributed under the Lesser GPL (LGPL). BioJava can be downloaded from the BioJava

SuperTarget goes quantitative: update on drug–target interactions

Abstract: There are at least two good reasons for the ongoing interest in drug–target interactions: first, drug-effects can only be fully understood by considering a complex network of interactions to multiple targets (so-called off-target effects) including metabolic and signaling pathways; second, it is crucial to consider drug-target-pathway relations for the identification of novel targets for drug development. To address this on-going need, we have developed a web-based data warehouse named SuperTarget, which integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present, the updated database contains >6000 target proteins, which are annotated with >330 000 relations to 196 000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range. SuperTarget is available at http://bioinformatics.charite.de/supertarget.

Topic Pages: PLoS Computational Biology Meets Wikipedia

Abstract: While there has been much debate about the coverage and quality of Wikipedia (starting with an article in 2005 [1]), there is no doubt about its value (and increasing role) as a reference source and starting point for in-depth research. For example, within the biomedical sciences, there have been recent articles about the accuracy and completeness of drug information in Wikipedia [2], Wikipedia as a source of information in nursing care [3] and mental disorders [4], and making biological databases available through Wikipedia [5].

Improving The Future of Research Communications and e-Scholarship (Dagstuhl Perspectives Workshop 11331)

Abstract: The dissemination of knowledge derived from research and scholarship has a fundamental impact on the ways in which society develops and progresses, and at the same time it feeds back to improve subsequent research and scholarship. Here, as in so many other areas of human activity, the internet is changing the way things work; two decades of emergent and increasingly pervasive information technology have demonstrated the potential for far more effective scholarly communication. But the use of this technology remains limited. Force11 is a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing. This document highlights the findings of the Force11 workshop on the Future of Research Communication held at Schloss Dagstuhl, Germany, in August 2011: it summarizes a number of key problems facing scholarly publishing today, and presents a vision that addresses these problems, proposing concrete steps that key stakeholders can take to improve the state of scholarly publishing.

Ten simple rules to commercialize scientific research.

Abstract: Commercializing scientific research or a breakthrough idea is really no different, in principle, from commercializing anything, except perhaps that it's more difficult in practice because of the steps required to turn basic research into something practical and because you are looking for a market for a product, rather than designing a product to fit an established, or obvious market. Commercialization is different to starting and running a company, a broader endeavour and the subject of a previous Ten Simple Rules article [1]. Even so, commercialization can be a broad endeavour. For example, at one extreme, you could hand over your monoclonal antibody to Sigma to supply it on your behalf to other researchers who might find it useful while the company pays you a small royalty; on the other, you could be involved in developing Herceptin (anti-HER2 monoclonal antibody) from its origins as a mouse-specific antibody through to its use as an effective anti-breast cancer drug, in a process that took more than decade. Here we assume the former—others are carrying out that commercialization, which has its pluses and minuses—less work for you, but typically less control of the commercialization process. Commercialization is a much studied subject, both by academics [2] and the business community [3]. All larger academic institutions generally have offices to promote and help scientists get research to market. Consequently, in this Ten Simple Rules article we won't deal with the details, but instead will concentrate on some of the key issues to consider when working with, or before and after working with, a specialized office.

Ten Simple Rules to Protect Your Intellectual Property

Abstract: The concepts that underpin the protection of ideas and inventions are not new; such laws have been around for several hundred years and are discussed under the broad heading of intellectual property (IP). IP is easily misunderstood, but at the same time most scientists encounter it at some point in their career, as it is a necessary feature in the commercialization of research. The term intellectual property includes such concepts and rights as copyright, trademarks, industrial design rights, and patents. It is important to remember that IP is a tool to help your endeavours, and not a goal in itself. Having IP for its own sake is pointless. IP can be crucial in commercializing research and running a successful science-based business, but having a patent and having a successful patented product are two very different things. Above all, IP can only work for you if you understand what it is, why you want it, and what you are going to do with it. These ten simple rules are intended to provide an overview of these issues; however, we must start with a warning. Laws relating to IP change all the time, they are complex, sometimes rather obscure, and are very different from country to country. For example, research surrounding methods of treatment by surgery and therapy and diagnostic methods are patentable in the United States, but specifically excluded from patentability in Europe [1]. However, these boundaries seem to be shifting in both the US and Europe. In short, we are dealing with a complex and changing subject and restrict ourselves here to the guiding principles.

Receptor Databases and Computational Websites for Ligand Binding

Abstract: Ligand binding to receptors is a key step in the regulation of cellular function by neurotransmitters, hormones, and many drugs. Not surprisingly then, genome projects have found that families of receptor genes form the largest groups of functional genes in mammalian genomes. A large body of experimental data have thus been generated on receptor-ligand interactions, and in turn, numerous computational tools for the in silico prediction of receptor-ligand interactions have been developed. Websites containing ligand binding data and tools to assess and manipulate such data are available in the public domain. Such Websites provide a resource for experimentalists studying receptor binding and for scientists interested in utilizing large data sets for other purposes, which include modeling structure-function relationships, defining patterns of interactions of drugs with different receptors, and computational comparisons among receptors. The Websites include databases of receptor protein and nucleotide sequences for particular classes of receptors (such as G-protein-coupled receptors and nuclear receptors) and of experimental results from receptor-ligand binding assays, as well as computational tools for modeling the interactions between ligands and receptors and predicting the function of orphan receptors. In this chapter, we provide information and Uniform Resource Locators (URLs) for Websites that facilitate computational and experimental studies of receptor-ligand interactions. This list will be periodically updated at https://sites.google.com/site/receptorligandbinding/.

An integrated workflow for proteome-wide off-target identification and polypharmacology drug design

Abstract: Polypharmacology, which focuses on designing drugs to target multiple receptors, has emerged as a new paradigm in drug discovery. To rationally design multi-target drugs, it is fundamental to understand protein-ligand interactions on a proteome scale. We have developed a Proteome-wide Off-target Pipeline POP that integrates ligand binding site analysis, protein-ligand docking, the statistic analysis of docking scores, and electrostatics potential calculation. The utility of POP is demonstrated by a case study, in which the molecular mechanism of anti-cancer effect of Nelfinavir is hypothesized. By combining structural proteome-wide off-target identification and systems biology, it is possible for us to correlate drug perturbations with clinical outcomes.

Ten Simple Rules for Starting a Company

Abstract: Many faculty, staff, and students at academic institutions think about starting companies at some point in their careers. As academic funding models change, and how academia views entrepreneurial activity changes, starting companies is likely to happen more frequently. Hence, it is worth considering Ten Simple Rules to contemplate when starting a company while in academia. There is a wealth of general information out there to help you, but that information is not aimed specifically towards computational biologists. What follows is a hybrid that is intended as a general quick review for anyone, intermingled with some specific advice for computational biologists. By way of experience, we should say we have been involved in starting several companies: both in the biomedical sciences, dealing with biological software, computational biology services, and currently SciVee Inc. (http://www.scivee.tv) distributing scientific rich media, and outside it, ranging from the distribution of independent films, to a socially oriented dining club aimed at supporting local businesses, to a business supplying art quality photographic prints. None have been a great financial success, but all have been immense fun, an opportunity to meet interesting people, and an opportunity to think quite differently than when doing scientific research. Read that as a personal endorsement to go for it, even if starting a company is not yet a well-formed idea, and even though, as you will see below, the rules themselves might be cautionary and off-putting.

Seven Years; It's Time for a Change

Abstract: After seven years as the Editor-in-Chief of PLOS Computational Biology, I have decided to step to the side It's time to bring in new leadership and a new vision As scientists we generally do not learn a lot of management skills (a mistake in my opinion), but if I have learnt two management skills it is the value of enablement and to start planning for your successor on day one Well, I did not start on day one, but Ruth Nussinov has been the Deputy Editor-in-Chief since October 2008, and she is an outstanding scientist and editor ideally suited to take over as editor-in-chief So please welcome Ruth to this leadership role The journal is in very safe hands The journal staff, editors, and Ruth have not seen the last of me, however—this is just too much fun As Founding Editor-in-Chief, I will continue to be involved with the journal, with a focus on special projects, helping Ruth and the team where I can, and, of course, continue as an author of both research articles and front matter, such as the Ten Simple Rules series In changing roles, I would like to make a few personal comments about the evolution of the journal and where it might go next When Steven Brenner, Michael Eisen, and I founded the journal, we had a vision for how it would fill a gap between journals supporting purely computational methods and the array of experimental journals with the odd, token computational paper [1] Thanks to you, the readers and authors, that vision has been realized beyond what we imagined, and I am very proud of how the journal is a voice for our broad and important community and at the same time helps build that community The appearance of the journal in June 2005 was timely since it both propelled our field of science at a time when it was being recognized as a critical part of the life sciences, and made a strong statement about the importance of open access I must confess that when we started planning for the journal in 2004, support for open access seemed like the right thing to do, but it was not a major driver for me That changed when, early on in my tenure, I realized open access is critical to maximizing the rate of scientific discovery Supporting open access, and the new forms of scholarly communication it fosters, enriched my career and brought me into contact with many amazing people I would not otherwise have met Emphasizing that enrichment, of the 22 invited lectures I gave in 2011, 18 were evangelizing about the importance of open access and open science, and four were directly related to my science In my opinion, we have yet to see open access reach its full potential, but we will [2], and our journal will be poised to play an ever-increasing role as an exemplar for what is possible In short, PLOS and this journal will continue to foster change, which maximizes the accessibility and comprehension of science I am proud to continue to be a part of that It only remains for me to thank and acknowledge a variety of people who have all been so critically important in the past seven years First and foremost are the approximately 150 editors who have worked tirelessly to shape and ensure a high-quality product over the years There is no journal without community-driven efforts, which offer limited reward beyond a job well done I can't mention everyone, but I must call out Karl Friston, who between 2005 and 2010 worked tirelessly to make computational neuroscience such a rich part of the journal, and Steven Brenner, Simon Levin, and Sebastian Bonhoeffer, who, since the journal's inception, have always responded with good advice Thanks are also due to Mark Patterson, who, until late 2011, was Director of Publishing at PLOS Mark was critical to the success of the journal and to PLOS as a whole We first began talking about the journal in May 2004, and he listened to, refined, and contributed to a lot of crazy ideas that define what the journal is today To Catherine Nancarrow, who managed the journal from 2005 to 2010 A nicer and more dedicated person will not be found In the current era of 140 character snapshots, her emails conferred a quality, beauty, and caring that you just don't see anymore To Evie Browne, who contributed so much as Publications Assistant and Publications Manager between 2006 and 2009 Her spreadsheet analyses of how the journal was doing were amazing To Andy Collings, who in various roles from Publications Assistant to Editorial Manager from 2005 to 2012 just made all ideas work, however outside the box they were To Fran Lewitter, who, as Education Editor since the beginning, has created an important community jewel To Scott Markel, who has been a voice of reason and an interface to the International Society of Computational Biology (ISCB) over the years Finally, to all the other staff who have contributed over the past seven years: Emily Stevenson, Johanna Dehlinger, Helen Budd, Sheran Basra, and Cecy Marden, and the amazing current staff of Laura Taylor, Clare Weaver, and Chris Hall, all so well led by Rosemary Dickin and Theo Bloom PLOS is a family of journals and a family of people; in short, a family organization whose goal is to disseminate science in the most open and useful way possible It is a successful organization able to recruit the best and most dedicated staff, and to adjust to its growing success and the success of open access itself The world of scientific publishing will never be the same again because of PLOS and the people who drive it I am proud to continue to be a member of this amazing family There is no other publisher like PLOS and no other journal like PLOS Computational Biology

A Review of 2011 for PLoS Computational Biology

Abstract: In 2011, during discussions at various conferences, as well as informally with authors, readers, reviewers, and editors, we were struck by one resonating theme: the view that PLoS Computational Biology has helped to create a sense of community amongst a broad group of scientists and educators. While the journal labels itself as a PLoS “community” journal, if that label has any true meaning, it must come from the community itself. We feel that, after six years, we are indeed serving the community well, but as always you can disagree at any time, either publicly with a comment in response to this article or by email (gro.solp@loibpmocsolp). That service comes first and foremost from the research we publish, but also from our desire to educate, report on open-source software, provide a history of the field, capture the vision of our editors, and move beyond the boundaries of traditional publishing to inform people within and outside of our community. Before we take a look at developments in each of these areas, and what is to come in 2012, let us first review how we served the community in 2011. According to Google Analytics, 2011 saw over 553,000 unique visitors to our website and more than two million article views (not including access statistics from PubMed Central). Visitors came from 211 countries/territories, which was undoubtedly helped by the fact that the journal is open access. India, Spain, Russia, and Iran each showed over a 40% increase in visitors from the previous year. From the journal website, the most accessed Research Article was “Effect of Promoter Architecture on the Cell-to-Cell Variability in Gene Expression” by Sanchez et al. [1], published in March 2011 (8,954 views at the time of writing); the most accessed article overall was “Ten Simple Rules for Building and Maintaining a Scientific Reputation” by Bourne and Barbour [2], published in June 2011 (15,255 views at the time of writing). Also in 2011, 1,623 research articles from 57 countries were submitted, up 16% from 2010, and 384 were published (down 2% from 2010). Receiving more but publishing about the same number in real terms should reflect the increasing quality of our content. We are very grateful to our Associate Editors, Guest Editors, reviewers (a list of Guest Editors and reviewers from 2011 is available in Table S1), and, of course, our Deputy Editors – Patricia Babbitt, Joel Bader, Sebastian Bonhoeffer, Lyle J. Graham, Konrad Kording, Douglas Lauffenburger, Uwe Ohler, Nathan Price, Burkhard Rost, Olaf Sporns, Wyeth Wasserman, and Weixiong Zhang – for helping us to handle this growth. With this growth, we have not met our goal of reducing the times to first decision, even with the addition of new editors, but we will continue to work on this in 2012. Our median decision before review time in 2011 was 8 days, and our median decision after review time was 47 days. A number of our Research Articles were featured in blogs and the popular press. Notably, Mitra Hartman's paper on the morphology of the rat vibrissal array [3] was covered extensively, including two videos, by National Public Radio and Science Bytes. Our Software section was launched in August 2011, and we have so far published one article, with six more either accepted or under review. Uptake has been relatively slow, based on, we believe, the open source and stringent documentation requirements we have imposed. We believe it is better to publish only a few, but high-quality, software articles, and that this will highlight the lack of rigor of software otherwise in the field. Our Education section has continued to flourish, in part because of the journal's relationship with the International Society for Computational Biology (ISCB). This year we introduced a collection, Bioinformatics: Starting Early, which takes the notion of biology as a computational science into secondary schools. We are hoping for more articles from those involved in secondary teaching in 2012. Open science removes all boundaries not only to reading the latest science, but also to contributing to that science. We have even seen secondary school students as authors and expect to see more in the future. In July 2011 we began the Editors Outlook series, with five published [4]–[8] and more on the way. These mini-reviews already broach subjects from ontologies to genome organization, and from evolution to data and privacy. They speak to the breadth of our field and editorial board, and collectively will form a vision from our many expert editors of what is being, and will be, accomplished in the coming years. That our journal is fully open access provides opportunities for maximizing the use and reuse of our scholarship; we intend to explore this further in 2012. Early in 2012 we will launch our first Topic Page on circular permutations in proteins. Wikipedia is a valuable resource for knowledge dissemination, yet Wikipedia pages are lacking in coverage of computational biology. In part this is because authors gain little career-based reward for creating Wikipedia pages. We aim to bridge the gap. Topic Page articles, which will be published in the journal and will each receive a PubMed identifier and DOI, will become the copy of record, thereby crediting the author(s). At the same time the Topic Page will be used to seed a Wikipedia article and become a living version of the same material–a viable option thanks to our Creative Commons license. Look for an announcement of this development in the new year, but in the interim if you have ideas for Topic Pages you would like to contribute, please do get in touch for further information (gro.solp@loibpmocsolp). We are also contemplating a new article type: Data Pages. Data Pages would be brief publications about datasets, in which the data are not already well described in other papers yet are considered of great value to the community. Such brief publications would bring a traditional reward to the producers of these shared datasets. Which is more valuable: a dataset downloaded and used by 100 investigators, who in turn publish research based on these data, or a paper that is cited only by the authors who wrote it? Data Pages would, from our point of view, help to answer this question. If you want to provide feedback on our plans for Data Pages later in 2012, please do so by commenting on this article. Feel free to comment in public or to us privately on anything we are doing, or ideas that you have for the future of the journal. After all, PLoS Computational Biology is a community journal, and if you have read this far, you should consider yourself an important part of our ever-broadening community.