Drug Promiscuity in PDB: Protein Binding Site Similarity Is Key

Drug repositioning applies established drugs to new disease indications with increasing success. A pre-requisite for drug repurposing is drug promiscuity (polypharmacology) – a drug’s ability to bind to several targets. There is a long standing debate on the reasons for drug promiscuity. Based on large compound screens, hydrophobicity and molecular weight have been suggested as key reasons. However, the results are sometimes contradictory and leave space for further analysis. Protein structures offer a structural dimension to explain promiscuity: Can a drug bind multiple targets because the drug is flexible or because the targets are structurally similar or even share similar binding sites? We present a systematic study of drug promiscuity based on structural data of PDB target proteins with a set of 164 promiscuous drugs. We show that there is no correlation between the degree of promiscuity and ligand properties such as hydrophobicity or molecular weight but a weak correlation to conformational flexibility. However, we do find a correlation between promiscuity and structural similarity as well as binding site similarity of protein targets. In particular, 71% of the drugs have at least two targets with similar binding sites. In order to overcome issues in detection of remotely similar binding sites, we employed a score for binding site similarity: LigandRMSD measures the similarity of the aligned ligands and uncovers remote local similarities in proteins. It can be applied to arbitrary structural binding site alignments. Three representative examples, namely the anti-cancer drug methotrexate, the natural product quercetin and the anti-diabetic drug acarbose are discussed in detail. Our findings suggest that global structural and binding site similarity play a more important role to explain the observed drug promiscuity in the PDB than physicochemical drug properties like hydrophobicity or molecular weight. Additionally, we find ligand flexibility to have a minor influence.     

A Linear Mixed Model Spline Framework for Analysing Time Course ‘Omics’ Data

Time course ‘omics’ experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. ‘Omics’ technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course ‘omics’ experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course ‘omics’ data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course ‘omics’ studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.     

Pediatric Drug Safety Surveillance in FDA-AERS: A Description of Adverse Events from GRiP Project

Individual case safety reports (ICSRs) are a cornerstone in drug safety surveillance. The knowledge on using these data specifically for children is limited. We studied characteristics of pediatric ICSRs reported to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Public available ICSRs reported in children (0–18 years) to FAERS were downloaded from the FDA-website for the period Jan 2004-Dec 2011. Characteristics of these ICSRs, including the reported drugs and events, were described and stratified by age-groups. We included 106,122 pediatric ICSRs (55% boys and 58% from United States) with a median of 1 drug [range 1–3] and 1 event [1–2] per ICSR. Mean age was 9.1 years. 90% was submitted through expedited (15-days) (65%) or periodic reporting (25%) and 10% by non-manufacturers. The proportion and type of pediatric ICSRs reported were relatively stable over time. Most commonly reported drug classes by decreasing frequency were ‘nervous system drugs’ (58%), ‘antineoplastics’ (32%) and ‘anti-infectives’ (25%). Most commonly reported system organ classes were ‘general’ (13%), ‘nervous system’ (12%) and ‘psychiatric’ (11%) disorders. Duration of use could be calculated for 19.7% of the reported drugs, of which 14.5% concerned drugs being used long-term (>6 months). Knowledge on the distribution of the drug classes and events within FAERS is a key first step in developing pediatric specific methods for drug safety surveillance. Because of several differences in terms of drugs and events among age-categories, drug safety signal detection analysis in children needs to be stratified by each age group.     

IDMap: facilitating the detection of potential leads with therapeutic targets

Pharmaceutical industry has been striving to reduce the costs of drug development and increase productivity. Among the many different attempts, drug repositioning (retargeting existing drugs) comes into the spotlight because of its financial efficiency. We introduce IDMap which predicts novel relationships between targets and chemicals and thus is capable of repositioning the marketed drugs by using text mining and chemical structure information. Also capable of mapping commercial chemicals to possible drug targets and vice versa, IDMap creates convenient environments for identifying the potential lead and its targets, especially in the field of drug repositioning. AVAILABILITY: IDMap executable and its user manual including color images are freely available to non-commercial users at http://www.equispharm.com/idmap     

'Conserved hypothetical' proteins: prioritization of targets for experimental study

Comparative genomics shows that a substantial fraction of the genes in sequenced genomes encodes ‘conserved hypothetical’ proteins, i.e. those that are found in organisms from several phylogenetic lineages but have not been functionally characterized. Here, we briefly discuss recent progress in functional characterization of prokaryotic ‘conserved hypothetical’ proteins and the possible criteria for prioritizing targets for experimental study. Based on these criteria, the chief one being wide phyletic spread, we offer two ‘top 10’ lists of highly attractive targets. The first list consists of proteins for which biochemical activity could be predicted with reasonable confidence but the biological function was predicted only in general terms, if at all (‘known unknowns’). The second list includes proteins for which there is no prediction of biochemical activity, even if, for some, general biological clues exist (‘unknown unknowns’). The experimental characterization of these and other ‘conserved hypothetical’ proteins is expected to reveal new, crucial aspects of microbial biology and could also lead to better functional prediction for medically relevant human homologs.     

Reticulophagy and nucleophagy: New findings and unsolved issues

Autophagy targets various intracellular components ranging from proteins and nucleic acids to organelles for their degradation in lysosomes or vacuoles. In selective types of autophagy, receptor proteins play central roles in target selection. These proteins bind or localize to specific targets, and also interact with Atg8 family proteins on forming autophagosomal membranes, leading to the efficient sequestration of the targets by the membranes. Our recent study revealed that yeast cells actively degrade the endoplasmic reticulum (ER) and even part of the nucleus via selective autophagy under nitrogen-deprived conditions. We identified novel receptors, Atg39 and Atg40, specific to these pathways. Here, we summarize our findings on ‘reticulophagy’ (or ‘ER-phagy’) and ‘nucleophagy’, and discuss key issues that remain to be solved in future studies.     

Revisiting PPARγ as a target for the treatment of metabolic disorders

As the prevalence of obesity has increased explosively over the last several decades, associated metabolic disorders, including type 2 diabetes, dyslipidemia, hypertension, and cardiovascular diseases, have been also increased. Thus, new strategies for preventing and treating them are needed. The nuclear peroxisome proliferator-activated receptors (PPARs) are involved fundamentally in regulating energy homeostasis; thus, they have been considered attractive drug targets for addressing metabolic disorders. Among the PPARs, PPARγ is a master regulator of gene expression for metabolism, inflammation, and other pathways in many cell types, especially adipocytes. It is a physiological receptor of the potent anti-diabetic drugs of the thiazolidinediones (TZDs) class, including rosiglitazone (Avandia). However, TZDs have undesirable and severe side effects, such as weight gain, fluid retention, and cardiovascular dysfunction. Recently, many reports have suggested that PPARγ could be modulated by post-translational modifications (PTMs), and modulation of PTM has been considered as novel approaches for treating metabolic disorders with fewer side effects than the TZDs. In this review, we discuss how PTM of PPARγ may be regulated and issues to be considered in making novel anti-diabetic drugs that can modulate the PTM of PPARγ. [BMB Reports 2014; 47(11): 599-608]     

Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data

Background

Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials.

Methodology/Principal Findings

To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins.

Conclusions/Significance

Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents.     

Factors Associated with Results and Conclusions of Trials of Thiazolidinediones

Background

When a sponsor funds a study of two competing drugs in a head-to-head comparison, the results and conclusions are likely to favor the sponsor’s drug. Thiazolidinediones, oral medications used for the treatment of type 2 diabetes, are one of the most costly choices of oral anti-diabetic medications, yet they do not demonstrate clinically relevant differences in achieving lower glycosylated hemoglobin levels compared to other oral antidiabetic drugs. Our aim is to examine associations between research funding source, study design characteristics aimed at reducing bias, and other factors with the results and conclusions of randomized controlled trials (RCTs) of thiazolidinediones compared to other oral hypoglycemic agents.

Methods and Findings

This is a cross-sectional study of 61 published RCTs comparing a thiazolidinedione (glitazone) to another anti-diabetic drug or placebo for treatment of type 2 diabetes. Data on study design characteristics, funding source, author’s financial ties, results for primary outcomes, and author conclusions were extracted. Univariate logistic regression identified associations between independent variables and results and conclusions that favored the glitazone. Of the RCTs, 59% (36/61) were funded by industry, 39% (24/61) did not disclose any funding. Common study design weaknesses included inadequate blinding and lack of concealment of allocation. Trials that reported favorable glycemic control results for the glitazone were more likely to have adequate blinding (OR (95% CI)  = 5.42 (1.46, 21.19), p = 0.008) and have a corresponding author with financial ties to the glitazone manufacturer (OR (95% CI)  = 4.12 (1.05, 19.53); p = 0.04). Trials with conclusions favoring the glitazone were less likely to be funded by a comparator drug company than a glitazone company (OR (95% CI)  = 0.026 (0, 0.40), p = 0.003) and less likely to be published in journals with higher impact factors (OR (95% CI)  = 0.79 (0.62, 0.97), p = 0.011). One limitation of our study is that we categorized studies as funded by industry based on each article’s disclosure which could underestimate the number of industry sponsored studies and personal ties of investigators. Additionally, our study did not include any head-to-head comparisons of one glitazone to another.

Conclusions

Published RCT comparisons of glitazones with other anti-diabetic drugs or placebo are predominantly industry supported and this support, as well as the financial ties of study authors, appears to be associated with favorable findings.     

Plastid Proteomic Analysis in Tomato Fruit Development

To better understand the mechanism of plastid differentiation from chloroplast to chromoplast, we examined proteome and plastid changes over four distinct developmental stages of ‘Micro-Tom’ fruit. Additionally, to discover more about the relationship between fruit color and plastid differentiation, we also analyzed and compared ‘Micro-Tom’ results with those from two other varieties, ‘Black’ and ‘White Beauty’. We confirmed that proteins related to photosynthesis remain through the orange maturity stage of ‘Micro-Tom’, and also learned that thylakoids no longer exist at this stage. These results suggest that at a minimum there are changes in plastid morphology occurring before all related proteins change. We also compared ‘Micro-Tom’ fruits with ‘Black’ and ‘White Beauty’ using two-dimensional gel electrophoresis. We found a decrease of CHRC (plastid-lipid-associated protein) and HrBP1 (harpin binding protein-1) in the ‘Black’ and ‘White Beauty’ varieties. CHRC is involved in carotenoid accumulation and stabilization. HrBP1 in Arabidopsis has a sequence similar to proteins in the PAP/fibrillin family. These proteins have characteristics and functions similar to lipocalin, an example of which is the transport of hydrophobic molecules. We detected spots of TIL (temperature-induced lipocalin) in 2D-PAGE results, however the number of spots and their isoelectric points differed between ‘Micro-Tom’ and ‘Black’/‘White Beauty’. Lipocalin has various functions including those related to environmental stress response, apoptosis induction, membrane formation and fixation, regulation of immune response, cell growth, and metabolism adjustment. Lipocalin related proteins such as TIL and HrBP1 could be related to the accumulation of carotenoids, fruit color and the differentiation of chromoplast.     

A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs

The present study proposed a two-step drug repositioning method based on a protein-protein interaction (PPI) network of two diseases and the similarity of the drugs prescribed for one of the two. In the proposed method, first, lists of disease related genes were obtained from a meta-database called Genotator. Then genes shared by a pair of diseases were sought. At the first step of the method, if a drug having its target(s) in the PPI network, the drug was deemed a repositioning candidate. Because targets of many drugs are still unknown, the similarities between the prescribed drugs for a specific disease were used to infer repositioning candidates at the second step. As a first attempt, we applied the proposed method to four different types of diseases: hypertension, diabetes mellitus, Crohn disease, and autism. Some repositioning candidates were found both at the first and second steps.     

Systematic drug repositioning based on clinical side-effects

Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naïve Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this “clinical phenotypic assay”.     

aniFOUND: analysing the associated proteome and genomic landscape of the repaired nascent non-replicative chromatin

Specific capture of chromatin fractions with distinct and well-defined features has emerged as both challenging and a key strategy towards a comprehensive understanding of genome biology. In this context, we developed aniFOUND (accelerated native isolation of factors on unscheduled nascent DNA), an antibody-free method, which can label, capture, map and characterise nascent chromatin fragments that are synthesized in response to specific cues outside S-phase. We used the ‘unscheduled’ DNA synthesis (UDS) that takes place during the repair of UV-induced DNA lesions and coupled the captured chromatin to high-throughput analytical technologies. By mass-spectrometry we identified several factors with no previously known role in UVC-DNA damage response (DDR) as well as known DDR proteins. We experimentally validated the repair-dependent recruitment of the chromatin remodeller RSF1 and the cohesin-loader NIPBL at sites of UVC-induced photolesions. Developing aniFOUND-seq, a protocol for mapping UDS activity with high resolution, allowed us to monitor the landscape of UVC repair-synthesis events genome wide. We further resolved repair efficacy of the rather unexplored repeated genome, in particular rDNA and telomeres. In summary, aniFOUND delineates the proteome composition and genomic landscape of chromatin loci with specific features by integrating state-of-the-art ‘omics’ technologies to promote a comprehensive view of their function.     

Different genome stability proteins underpin primed and na?ve adaptation in E. coli CRISPR-Cas immunity

CRISPR-Cas is a prokaryotic immune system built from capture and integration of invader DNA into CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) loci, termed ‘Adaptation’, which is dependent on Cas1 and Cas2 proteins. In Escherichia coli, Cascade-Cas3 degrades invader DNA to effect immunity, termed ‘Interference’. Adaptation can interact with interference (‘primed’), or is independent of it (‘naïve’). We demonstrate that primed adaptation requires the RecG helicase and PriA protein to be present. Genetic analysis of mutant phenotypes suggests that RecG is needed to dissipate R-loops at blocked replication forks. Additionally, we identify that DNA polymerase I is important for both primed and naive adaptation, and that RecB is needed for naïve adaptation. Purified Cas1-Cas2 protein shows specificity for binding to and nicking forked DNA within single strand gaps, and collapsing forks into DNA duplexes. The data suggest that different genome stability systems interact with primed or naïve adaptation when responding to blocked or collapsed invader DNA replication. In this model, RecG and Cas3 proteins respond to invader DNA replication forks that are blocked by Cascade interference, enabling DNA capture. RecBCD targets DNA ends at collapsed forks, enabling DNA capture without interference. DNA polymerase I is proposed to fill DNA gaps during spacer integration.     

A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction

Background

Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level.

Results

We evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% ‘precision’ (accuracy of prediction), 97% ‘recall’ (sensitivity)] in predicting ‘true-positive’ targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low ‘recall’ values. Score optimizations increased the ‘recall’ to only 70% (corresponding ‘precision’: 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly ‘precise’ predictions. The large number of ‘false negative’ predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches.

Conclusion

Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-348) contains supplementary material, which is available to authorized users.     

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.     

Unhealthy Days and Quality of Life in Irish Patients with Diabetes

Objectives

To study the determinants of health-related quality of life (HRQoL) in Irish patients with diabetes using the Centres for Disease Controls'' (CDC''s) ‘Unhealthy Days’ summary measure and to assesses the agreement between this generic HRQoL measure and the disease-specific Audit of Diabetes Dependant Quality of Life (ADDQoL) measure.

Research Design and Methods

Data were analysed from the Diabetes Quality of Life Study, a cross-sectional study of 1,456 people with diabetes in Ireland (71% response rate). Unhealthy days were assessed using the CDC''s ‘Unhealthy days’ summary measure. Quality of life (QoL) was also assessed using the ADDQoL measure. Analyses were conducted primarily using logistic regression. The agreement between the two QoL instruments was measured using the kappa co-efficient.

Results

Participants reported a median of 2 unhealthy days per month. In multivariate analyses, female gender (P = 0.001), insulin use (P = 0.030), diabetes complications (P = <0.001) were significantly associated with more unhealthy days. Older patients had fewer unhealthy days per month (P = 0.003). Agreement between the two measures of QoL (unhealthy days measure and ADDQoL) was poor, Kappa = 0.234

Conclusions

The findings highlight the determinants of HRQoL in patients with diabetes using a generic HRQoL summary measure. The ‘Unhealthy Days’ and the ADDQoL have poor agreement, therefore the ‘Unhealthy Days’ summary measure may be assessing a different construct. Nonetheless, this study demonstrates that the generic ‘Unhealthy Days’ summary measure can be used to detect determinants of HRQoL in patients with diabetes.     

Revisiting the Myths of Protein Interior: Studying Proteins with Mass-Fractal Hydrophobicity-Fractal and Polarizability-Fractal Dimensions

A robust marker to describe mass, hydrophobicity and polarizability distribution holds the key to deciphering structural and folding constraints within proteins. Since each of these distributions is inhomogeneous in nature, the construct should be sensitive in describing the patterns therein. We show, for the first time, that the hydrophobicity and polarizability distributions in protein interior follow fractal scaling. It is found that (barring ‘all-α’) all the major structural classes of proteins have an amount of unused hydrophobicity left in them. This amount of untapped hydrophobicity is observed to be greater in thermophilic proteins, than that in their (structurally aligned) mesophilic counterparts. ‘All-β’(thermophilic, mesophilic alike) proteins are found to have maximum amount of unused hydrophobicity, while ‘all-α’ proteins have been found to have minimum polarizability. A non-trivial dependency is observed between dielectric constant and hydrophobicity distributions within (α+β) and ‘all-α’ proteins, whereas absolutely no dependency is found between them in the ‘all-β’ class. This study proves that proteins are not as optimally packed as they are supposed to be. It is also proved that origin of α-helices are possibly not hydrophobic but electrostatic; whereas β-sheets are predominantly hydrophobic in nature. Significance of this study lies in protein engineering studies; because it quantifies the extent of packing that ensures protein functionality. It shows that myths regarding protein interior organization might obfuscate our knowledge of actual reality. However, if the later is studied with a robust marker of strong mathematical basis, unknown correlations can still be unearthed; which help us to understand the nature of hydrophobicity, causality behind protein folding, and the importance of anisotropic electrostatics in stabilizing a highly complex structure named ‘proteins’.