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1.  Ten Simple Rules for Writing a PLOS Ten Simple Rules Article 
PLoS Computational Biology  2014;10(10):e1003858.
PMCID: PMC4207461  PMID: 25340653
3.  DOIs for DICOM Raw Images: Enabling Science Reproducibility 
Radiology  2015;275(1):3-4.
By being a front-runner, the imaging community has everything to gain, because original DICOM raw data exposure to the wider science audience is likely to speed standardized image acquisition as well as engender greater confidence in the clinical imaging literature.
PMCID: PMC4455617  PMID: 25799330
4.  How open science helps researchers succeed 
eLife  null;5:e16800.
Open access, open data, open source and other open scholarship practices are growing in popularity and necessity. However, widespread adoption of these practices has not yet been achieved. One reason is that researchers are uncertain about how sharing their work will affect their careers. We review literature demonstrating that open research is associated with increases in citations, media attention, potential collaborators, job opportunities and funding opportunities. These findings are evidence that open research practices bring significant benefits to researchers relative to more traditional closed practices.
PMCID: PMC4973366  PMID: 27387362
open access; open data; open science; open source; research; None
5.  Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach 
Journal of medicinal chemistry  2016;59(9):4326-4341.
Targeted polypharmacology of kinases has emerged as a promising strategy to design efficient and safe therapeutics. Here, we perform a systematic study of kinase–ligand binding modes for the human structural kinome at scale (208 kinases, 1777 unique ligands, and their complexes) by integrating chemical genomics and structural genomics data and by introducing a functional site interaction fingerprint (Fs-IFP) method. New insights into kinase–ligand binding modes were obtained. We establish relationships between the features of binding modes, the ligands, and the binding pockets, respectively. We also drive the intrinsic binding specificity and which correlation with amino acid conservation. Third, we explore the landscape of the binding modes and highlight the regions of “selectivity pocket” and “selectivity entrance”. Finally, we demonstrate that Fs-IFP similarity is directly correlated to the experimentally determined profile. These improve our understanding of kinase–ligand interactions and contribute to the design of novel polypharmacological therapies targeting kinases.
PMCID: PMC4865454  PMID: 26929980
6.  Detection of circular permutations within protein structures using CE-CP 
Bioinformatics  2014;31(8):1316-1318.
Motivation: Circular permutation is an important type of protein rearrangement. Natural circular permutations have implications for protein function, stability and evolution. Artificial circular permutations have also been used for protein studies. However, such relationships are difficult to detect for many sequence and structure comparison algorithms and require special consideration.
Results: We developed a new algorithm, called Combinatorial Extension for Circular Permutations (CE-CP), which allows the structural comparison of circularly permuted proteins. CE-CP was designed to be user friendly and is integrated into the RCSB Protein Data Bank. It was tested on two collections of circularly permuted proteins. Pairwise alignments can be visualized both in a desktop application or on the web using Jmol and exported to other programs in a variety of formats.
Availability and implementation: The CE-CP algorithm can be accessed through the RCSB website at Source code is available under the LGPL 2.1 as part of BioJava 3 (;
Contact: or
PMCID: PMC4393524  PMID: 25505094
7.  Let's Make Gender Diversity in Data Science a Priority Right from the Start 
PLoS Biology  2015;13(7):e1002206.
The emergent field of data science is a critical driver for innovation in all sectors, a focus of tremendous workforce development, and an area of increasing importance within science, technology, engineering, and math (STEM). In all of its aspects, data science has the potential to narrow the gender gap and set a new bar for inclusion. To evolve data science in a way that promotes gender diversity, we must address two challenges: (1) how to increase the number of women acquiring skills and working in data science and (2) how to evolve organizations and professional cultures to better retain and advance women in data science. Everyone can contribute.
Data science is accelerating innovation, and the best and the brightest from both genders are needed to achieve its potential. This Perspective discusses what you can do to advance the field.
PMCID: PMC4516301  PMID: 26213996
8.  The FAIR Guiding Principles for scientific data management and stewardship 
Scientific Data  2016;3:160018.
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
PMCID: PMC4792175  PMID: 26978244
Research data; Publication characteristics
9.  Systems biology of the structural proteome 
BMC Systems Biology  2016;10:26.
The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology.
Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository (
Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism’s genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.
Electronic supplementary material
The online version of this article (doi:10.1186/s12918-016-0271-6) contains supplementary material, which is available to authorized users.
PMCID: PMC4787049  PMID: 26969117
11.  Systematic detection of internal symmetry in proteins using CE-Symm 
Journal of molecular biology  2014;426(11):2255-2268.
Symmetry is an important feature of protein tertiary and quaternary structure that has been associated with protein folding, function, evolution and stability. Its emergence and ensuing prevalence has been attributed to gene duplications, fusion events, and subsequent evolutionary drift in sequence. This process maintains structural similarity and is further supported by this study. To further investigate the question of how internal symmetry evolved, how symmetry and function are related, and the overall frequency of internal symmetry, we developed an algorithm, CE-Symm, to detect pseudosymmetry within the tertiary structure of protein chains. Using a large manually curated benchmark of 1007 protein domains, we show that CE-Symm performs significantly better than previous approaches. We use CE-Symm to build a census of symmetry among domain superfamilies in SCOP and note that 18% of all superfamilies are pseudo-symmetric. Our results indicate that more domains are pseudo-symmetric than previously estimated. We establish a number of recurring types of symmetry–function relationships and describe several characteristic cases in detail. Using the Enzyme Commission classification, symmetry was found to be enriched in some enzyme classes but depleted in others. CE-Symm thus provides a methodology for a more complete and detailed study of the role of symmetry in tertiary protein structure.
CE-Symm can be run from the web at Source code and software binaries are also available under the GNU Lesser General Public License (v. 2.1) at An interactive census of domains identified as symmetric by CE-Symm is available from:
PMCID: PMC4456030  PMID: 24681267
structural biology; protein evolution; pseudo-symmetry; tertiary structure; algorithm; structural alignment; symmetry detection; evolution; protein function
12.  Drug repurposing to target Ebola virus replication and virulence using structural systems pharmacology 
BMC Bioinformatics  2016;17:90.
The recent outbreak of Ebola has been cited as the largest in history. Despite this global health crisis, few drugs are available to efficiently treat Ebola infections. Drug repurposing provides a potentially efficient solution to accelerating the development of therapeutic approaches in response to Ebola outbreak. To identify such candidates, we use an integrated structural systems pharmacology pipeline which combines proteome-scale ligand binding site comparison, protein-ligand docking, and Molecular Dynamics (MD) simulation.
One thousand seven hundred and sixty-six FDA-approved drugs and 259 experimental drugs were screened to identify those with the potential to inhibit the replication and virulence of Ebola, and to determine the binding modes with their respective targets. Initial screening has identified a number of promising hits. Notably, Indinavir; an HIV protease inhibitor, may be effective in reducing the virulence of Ebola. Additionally, an antifungal (Sinefungin) and several anti-viral drugs (e.g. Maraviroc, Abacavir, Telbivudine, and Cidofovir) may inhibit Ebola RNA-directed RNA polymerase through targeting the MTase domain.
Identification of safe drug candidates is a crucial first step toward the determination of timely and effective therapeutic approaches to address and mitigate the impact of the Ebola global crisis and future outbreaks of pathogenic diseases. Further in vitro and in vivo testing to evaluate the anti-Ebola activity of these drugs is warranted.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-016-0941-9) contains supplementary material, which is available to authorized users.
PMCID: PMC4757998  PMID: 26887654
Drug repositioning; Infectious disease; Indinavir; Sinefungin; Binding site similarity; RNA-directed RNA polymerase; VP24
13.  Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model 
PLoS Computational Biology  2010;6(9):e1000938.
Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine.
Author Summary
Pharmaceutical science is only beginning to scratch the surface on the exact mechanisms of drug action that lead to a drug's breadth of patient responses, both intended and side effects. Decades of clinical trials, molecular studies, and more recent computational analysis have sought to characterize the interactions between a drug and the cell's molecular machinery. We have devised an integrated computational approach to assess how a drug may affect a particular system, in our study the metabolism of the human kidney, and its capacity for filtration of the contents of the blood. We applied this approach to retrospectively investigate potential causal drug targets leading to increased blood pressure in participants of clinical trials for the drug torcetrapib in an effort to display how our approach could be directly useful in the drug development process. Our results suggest specific metabolic enzymes that may be directly responsible for the side effect. The drug screening framework we have developed could be used to link adverse side effects to particular drug targets, discover new uses for old drugs, identify biomarkers for metabolic disease and drug response, and suggest genetic or dietary risk factors to help guide personalized patient care.
PMCID: PMC2950675  PMID: 20957118
14.  What Big Data means to me 
PMCID: PMC3932474  PMID: 24509599
15.  Ten Simple Rules for Lifelong Learning, According to Hamming 
PLoS Computational Biology  2015;11(2):e1004020.
PMCID: PMC4342007  PMID: 25719205
16.  Confronting the Ethical Challenges of Big Data in Public Health 
PLoS Computational Biology  2015;11(2):e1004073.
PMCID: PMC4321831  PMID: 25664660
17.  Let's Make Those Book Chapters Open Too! 
PLoS Computational Biology  2013;9(2):e1002941.
PMCID: PMC3578764
18.  Seven Years; It's Time for a Change 
PLoS Computational Biology  2012;8(10):e1002728.
PMCID: PMC3464190
19.  Ten Simple Rules To Commercialize Scientific Research 
PLoS Computational Biology  2012;8(9):e1002712.
PMCID: PMC3459878  PMID: 23028299
20.  Ten Simple Rules for Starting a Company 
PLoS Computational Biology  2012;8(3):e1002439.
PMCID: PMC3315446  PMID: 22479171
21.  Developing multi-target therapeutics to fine-tune the evolutionary dynamics of the cancer ecosystem 
PMCID: PMC4585080  PMID: 26441664
multi-target drug; cancer evolution; non-linear dynamic system; polypharmacology; cell-cell communication
22.  Ten Simple Rules for Approaching a New Job 
PLoS Computational Biology  2014;10(6):e1003660.
PMCID: PMC4072506  PMID: 24967974
23.  Ten Simple Rules for Building and Maintaining a Scientific Reputation 
PLoS Computational Biology  2011;7(6):e1002108.
PMCID: PMC3127799  PMID: 21738465
24.  A Review of 2010 for PLoS Computational Biology 
PLoS Computational Biology  2011;7(1):e1002003.
PMCID: PMC3029231  PMID: 21298077

Results 1-25 (130)