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1.  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
2.  Confronting the Ethical Challenges of Big Data in Public Health 
PLoS Computational Biology  2015;11(2):e1004073.
PMCID: PMC4321831  PMID: 25664660
3.  Let's Make Those Book Chapters Open Too! 
PLoS Computational Biology  2013;9(2):e1002941.
PMCID: PMC3578764
4.  Ten Simple Rules for Writing a PLOS Ten Simple Rules Article 
PLoS Computational Biology  2014;10(10):e1003858.
PMCID: PMC4207461  PMID: 25340653
5.  Seven Years; It's Time for a Change 
PLoS Computational Biology  2012;8(10):e1002728.
PMCID: PMC3464190
6.  Ten Simple Rules for Approaching a New Job 
PLoS Computational Biology  2014;10(6):e1003660.
PMCID: PMC4072506  PMID: 24967974
7.  Ten Simple Rules for Making Good Oral Presentations 
PLoS Computational Biology  2007;3(4):e77.
PMCID: PMC1857815  PMID: 17500596
8.  Ten Simple Rules for Reviewers 
PLoS Computational Biology  2006;2(9):e110.
PMCID: PMC1584310  PMID: 17009861
9.  One Year of PLoS Computational Biology 
PLoS Computational Biology  2006;2(8):e111.
PMCID: PMC1553486  PMID: 17523253
10.  Ten Simple Rules for Getting Published  
PLoS Computational Biology  2005;1(5):e57.
PMCID: PMC1274296  PMID: 16261197
11.  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 ( 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.
PMCID: PMC3531086  PMID: 23193259
12.  Ten Simple Rules to Protect Your Intellectual Property 
PLoS Computational Biology  2012;8(11):e1002766.
PMCID: PMC3493459  PMID: 23144604
13.  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.
PMCID: PMC3993922  PMID: 24437954
small ubiquitin-like modification (SUMOylation); posttranslational modification (PTM); combinatorial peptide library; peptide fragmentation patterns; algorithms; database search method; linked peptides
14.  Ten Simple Rules To Commercialize Scientific Research 
PLoS Computational Biology  2012;8(9):e1002712.
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.
PMCID: PMC3090642  PMID: 21572587
Electrostatic potential; Coarse-graining; Molecular modeling; Multipole moments; Algorithms; Distributed multipole analysis
16.  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.
PMCID: PMC4271151  PMID: 25488929
17.  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.
PMCID: PMC3070778  PMID: 21292475
18.  Ten Simple Rules for Starting a Company 
PLoS Computational Biology  2012;8(3):e1002439.
PMCID: PMC3315446  PMID: 22479171
20.  Ten Simple Rules for Building and Maintaining a Scientific Reputation 
PLoS Computational Biology  2011;7(6):e1002108.
PMCID: PMC3127799  PMID: 21738465
21.  Ten Simple Rules for Better Figures 
PLoS Computational Biology  2014;10(9):e1003833.
PMCID: PMC4161295  PMID: 25210732
22.  Drug Discovery Using Chemical Systems Biology: Weak Inhibition of Multiple Kinases May Contribute to the Anti-Cancer Effect of Nelfinavir 
PLoS Computational Biology  2011;7(4):e1002037.
Nelfinavir is a potent HIV-protease inhibitor with pleiotropic effects in cancer cells. Experimental studies connect its anti-cancer effects to the suppression of the Akt signaling pathway, but the actual molecular targets remain unknown. Using a structural proteome-wide off-target pipeline, which integrates molecular dynamics simulation and MM/GBSA free energy calculations with ligand binding site comparison and biological network analysis, we identified putative human off-targets of Nelfinavir and analyzed the impact on the associated biological processes. Our results suggest that Nelfinavir is able to inhibit multiple members of the protein kinase-like superfamily, which are involved in the regulation of cellular processes vital for carcinogenesis and metastasis. The computational predictions are supported by kinase activity assays and are consistent with existing experimental and clinical evidence. This finding provides a molecular basis to explain the broad-spectrum anti-cancer effect of Nelfinavir and presents opportunities to optimize the drug as a targeted polypharmacology agent.
Author Summary
The traditional approach to drug discovery of “one drug – one target – one disease” is insufficient, especially for complex diseases, like cancer. This inadequacy is partially addressed by accepting the notion of polypharmacology – one drug is likely to bind to multiple targets with varying affinity. However, to identify multiple targets for a drug is a complex and challenging task. We have developed a structural proteome-wide off-target determination pipeline by integrating computational methods for high-throughput ligand binding site comparison and binding free energy calculations to predict potential off-targets for known drugs. Here this method is applied to identify human off-targets for Nelfinavir, an antiretroviral drug with anti-cancer behavior. We propose inhibition by Nelfinavir of multiple protein kinase targets. We suggest that broad-spectrum low affinity binding by a drug or drugs to multiple targets may lead to a collective effect important in treating complex diseases such as cancer. The challenge is to understand enough about such processes so as to control them.
PMCID: PMC3084228  PMID: 21552547
23.  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 (
PMCID: PMC4271143  PMID: 25183487
24.  Ten Simple Rules for Getting Ahead as a Computational Biologist in Academia 
PLoS Computational Biology  2011;7(1):e1002001.
PMCID: PMC3017106  PMID: 21253560
25.  The Mycobacterium tuberculosis Drugome and Its Polypharmacological Implications 
PLoS Computational Biology  2010;6(11):e1000976.
We report a computational approach that integrates structural bioinformatics, molecular modelling and systems biology to construct a drug-target network on a structural proteome-wide scale. The approach has been applied to the genome of Mycobacterium tuberculosis (M.tb), the causative agent of one of today's most widely spread infectious diseases. The resulting drug-target interaction network for all structurally characterized approved drugs bound to putative M.tb receptors, we refer to as the ‘TB-drugome’. The TB-drugome reveals that approximately one-third of the drugs examined have the potential to be repositioned to treat tuberculosis and that many currently unexploited M.tb receptors may be chemically druggable and could serve as novel anti-tubercular targets. Furthermore, a detailed analysis of the TB-drugome has shed new light on the controversial issues surrounding drug-target networks [1]–[3]. Indeed, our results support the idea that drug-target networks are inherently modular, and further that any observed randomness is mainly caused by biased target coverage. The TB-drugome ( has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally the methodology may be applied to other pathogens of interest with results improving as more of their structural proteomes are determined through the continued efforts of structural biology/genomics.
Author Summary
The worldwide increase in multi-drug resistant TB poses a great threat to human health and highlights the need to identify new anti-tubercular agents. We have developed a computational strategy to link the structural proteome of Mycobacterium tuberculosis, the causative agent of tuberculosis, to all structurally characterized approved drugs, and hence construct a proteome-wide drug-target network – the TB-drugome. The TB-drugome has the potential to be a valuable resource in the development of safe and efficient anti-tubercular drugs. More generally, the proteome-wide and multi-scale view of target and drug space may facilitate a systematic drug discovery process, which concurrently takes into account the disease mechanism and druggability of targets, the drug-likeness and ADMET properties of chemical compounds, and the genetic dispositions of individuals. Ultimately it may help to reduce the high attrition rate in drug development through a better understanding of drug-receptor interactions on a large scale.
PMCID: PMC2973814  PMID: 21079673

Results 1-25 (113)