PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-25 (113)
 

Clipboard (0)
None

Select a Filter Below

Journals
more »
Year of Publication
more »
1.  Confronting the Ethical Challenges of Big Data in Public Health 
PLoS Computational Biology  2015;11(2):e1004073.
doi:10.1371/journal.pcbi.1004073
PMCID: PMC4321831  PMID: 25664660
2.  Ten Simple Rules for Writing a PLOS Ten Simple Rules Article 
PLoS Computational Biology  2014;10(10):e1003858.
doi:10.1371/journal.pcbi.1003858
PMCID: PMC4207461  PMID: 25340653
3.  A Turn-Key Approach for Large-Scale Identification of Complex Posttranslational Modifications 
Journal of Proteome Research  2014;13(3):1190-1199.
The conjugation of complex post-translational modifications (PTMs) such as glycosylation and Small Ubiquitin-like Modification (SUMOylation) to a substrate protein can substantially change the resulting peptide fragmentation pattern compared to its unmodified counterpart, making current database search methods inappropriate for the identification of tandem mass (MS/MS) spectra from such modified peptides. Traditionally it has been difficult to develop new algorithms to identify these atypical peptides because of the lack of a large set of annotated spectra from which to learn the altered fragmentation pattern. Using SUMOylation as an example, we propose a novel approach to generate large MS/MS training data from modified peptides and derive an algorithm that learns properties of PTM-specific fragmentation from such training data. Benchmark tests on data sets of varying complexity show that our method is 80–300% more sensitive than current state-of-the-art approaches. The core concepts of our method are readily applicable to developing algorithms for the identifications of peptides with other complex PTMs.
doi:10.1021/pr400368u
PMCID: PMC3993922  PMID: 24437954
small ubiquitin-like modification (SUMOylation); posttranslational modification (PTM); combinatorial peptide library; peptide fragmentation patterns; algorithms; database search method; linked peptides
4.  Ten Simple Rules for Approaching a New Job 
PLoS Computational Biology  2014;10(6):e1003660.
doi:10.1371/journal.pcbi.1003660
PMCID: PMC4072506  PMID: 24967974
5.  Achievements and challenges in structural bioinformatics and computational biophysics 
Bioinformatics  2014;31(1):146-150.
Motivation: The field of structural bioinformatics and computational biophysics has undergone a revolution in the last 10 years. Developments that are captured annually through the 3DSIG meeting, upon which this article reflects.
Results: An increase in the accessible data, computational resources and methodology has resulted in an increase in the size and resolution of studied systems and the complexity of the questions amenable to research. Concomitantly, the parameterization and efficiency of the methods have markedly improved along with their cross-validation with other computational and experimental results.
Conclusion: The field exhibits an ever-increasing integration with biochemistry, biophysics and other disciplines. In this article, we discuss recent achievements along with current challenges within the field.
Contact: Rafael.Najmanovich@USherbrooke.ca
doi:10.1093/bioinformatics/btu769
PMCID: PMC4271151  PMID: 25488929
6.  Let's Make Those Book Chapters Open Too! 
PLoS Computational Biology  2013;9(2):e1002941.
doi:10.1371/journal.pcbi.1002941
PMCID: PMC3578764
7.  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.
doi:10.1371/journal.pcbi.1000938
PMCID: PMC2950675  PMID: 20957118
8.  Seven Years; It's Time for a Change 
PLoS Computational Biology  2012;8(10):e1002728.
doi:10.1371/journal.pcbi.1002728
PMCID: PMC3464190
9.  Ten Simple Rules for Better Figures 
PLoS Computational Biology  2014;10(9):e1003833.
doi:10.1371/journal.pcbi.1003833
PMCID: PMC4161295  PMID: 25210732
10.  RCSB PDB Mobile: iOS and Android mobile apps to provide data access and visualization to the RCSB Protein Data Bank 
Bioinformatics  2014;31(1):126-127.
Summary: The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) resource provides tools for query, analysis and visualization of the 3D structures in the PDB archive. As the mobile Web is starting to surpass desktop and laptop usage, scientists and educators are beginning to integrate mobile devices into their research and teaching. In response, we have developed the RCSB PDB Mobile app for the iOS and Android mobile platforms to enable fast and convenient access to RCSB PDB data and services. Using the app, users from the general public to expert researchers can quickly search and visualize biomolecules, and add personal annotations via the RCSB PDB’s integrated MyPDB service.
Availability and implementation: RCSB PDB Mobile is freely available from the Apple App Store and Google Play (http://www.rcsb.org).
Contact: pwrose@ucsd.edu
doi:10.1093/bioinformatics/btu596
PMCID: PMC4271143  PMID: 25183487
11.  Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine 
PLoS Computational Biology  2014;10(5):e1003554.
Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
doi:10.1371/journal.pcbi.1003554
PMCID: PMC4022462  PMID: 24830652
12.  The RCSB Protein Data Bank: new resources for research and education 
Nucleic Acids Research  2012;41(Database issue):D475-D482.
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.
doi:10.1093/nar/gks1200
PMCID: PMC3531086  PMID: 23193259
13.  Ten Simple Rules to Protect Your Intellectual Property 
PLoS Computational Biology  2012;8(11):e1002766.
doi:10.1371/journal.pcbi.1002766
PMCID: PMC3493459  PMID: 23144604
14.  Ten Simple Rules To Commercialize Scientific Research 
PLoS Computational Biology  2012;8(9):e1002712.
doi:10.1371/journal.pcbi.1002712
PMCID: PMC3459878  PMID: 23028299
15.  Coarse-graining the electrostatic potential via distributed multipole expansions 
Computer physics communications  2011;182(7):1455-1462.
Multipole expansions offer a natural path to coarse-graining the electrostatic potential. However, the validity of the expansion is restricted to regions outside a spherical enclosure of the distribution of charge and, therefore, not suitable for most applications that demand accurate representation at arbitrary positions around the molecule. We propose and demonstrate a distributed multipole expansion approach that resolves this limitation. We also provide a practical algorithm for the computational implementation of this approach. The method allows the partitioning of the charge distribution into subsystems so that the multipole expansion of each component of the partition, and therefore of their superposition, is valid outside an enclosing surface of the molecule of arbitrary shape. The complexity of the resulting coarse-grained model of electrostatic potential is dictated by the area of the molecular surface and therefore, for a typical three-dimensional molecule, it scale as N2/3 with N, the number of charges in the system. This makes the method especially useful for coarse-grained studies of biological systems consisting of many large macromolecules provided that the configuration of the individual molecules can be approximated as fixed.
doi:10.1016/j.cpc.2011.03.014
PMCID: PMC3090642  PMID: 21572587
Electrostatic potential; Coarse-graining; Molecular modeling; Multipole moments; Algorithms; Distributed multipole analysis
16.  Ten Simple Rules for Making Good Oral Presentations 
PLoS Computational Biology  2007;3(4):e77.
doi:10.1371/journal.pcbi.0030077
PMCID: PMC1857815  PMID: 17500596
17.  Ten Simple Rules for Reviewers 
PLoS Computational Biology  2006;2(9):e110.
doi:10.1371/journal.pcbi.0020110
PMCID: PMC1584310  PMID: 17009861
18.  One Year of PLoS Computational Biology 
PLoS Computational Biology  2006;2(8):e111.
doi:10.1371/journal.pcbi.0020111
PMCID: PMC1553486  PMID: 17523253
19.  Structure-based Systems Biology for Analyzing Off-target Binding 
Here off-target binding implies the binding of a small molecule of therapeutic interest to a protein target other than the primary target for which it was intended. Increasingly such off-targeting appears to be the norm rather than the exception, rational drug design notwithstanding, and can lead to detrimental side-effects, or opportunities to reposition a therapeutic agent to treat a different condition. Not surprisingly, there is significant interest in determining a priori what off-targets exist on a proteome-wide scale. Beyond determining putative off-targets is the need to understand the impact of such binding on the complete biological system, with the ultimate goal of being able to predict the phenotypic outcome. While a very ambitious goal, some progress is being made.
doi:10.1016/j.sbi.2011.01.004
PMCID: PMC3070778  PMID: 21292475
20.  Ten Simple Rules for Starting a Company 
PLoS Computational Biology  2012;8(3):e1002439.
doi:10.1371/journal.pcbi.1002439
PMCID: PMC3315446  PMID: 22479171
21.  Ten Simple Rules for Getting Published  
PLoS Computational Biology  2005;1(5):e57.
doi:10.1371/journal.pcbi.0010057
PMCID: PMC1274296  PMID: 16261197
23.  Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome 
PLoS ONE  2013;8(11):e80278.
How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published paper for different classes of users (ranging from users with little expertise to domain experts) and suggest ways in which the situation might be improved. Quantification is achieved by estimating the time required to reproduce each of the steps in the method described in the original paper and make them part of an explicit workflow that reproduces the original results. Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results. The quantification leads to “reproducibility maps” that reveal that novice researchers would only be able to reproduce a few of the steps in the method, and that only expert researchers with advance knowledge of the domain would be able to reproduce the method in its entirety. The workflow itself is published as an online resource together with supporting software and data. The paper concludes with a brief discussion of the complexities of requiring reproducibility in terms of cost versus benefit, and a desiderata with our observations and guidelines for improving reproducibility. This has implications not only in reproducing the work of others from published papers, but reproducing work from one’s own laboratory.
doi:10.1371/journal.pone.0080278
PMCID: PMC3842296  PMID: 24312207
24.  Ten Simple Rules for Building and Maintaining a Scientific Reputation 
PLoS Computational Biology  2011;7(6):e1002108.
doi:10.1371/journal.pcbi.1002108
PMCID: PMC3127799  PMID: 21738465
25.  Learning How to Run a Lab: Interviews with Principal Investigators 
PLoS Computational Biology  2013;9(11):e1003349.
doi:10.1371/journal.pcbi.1003349
PMCID: PMC3820502  PMID: 24244147

Results 1-25 (113)