Large-scale identification of potential drug targets based on the topological features of human protein–protein interaction network

Zou, Xiaoyong; Zhong, Wenqian; Liu, Zhiqing; Huang, Menghua; Xie, Yuanfu; Dai, Zong; Zou, Xiaoyong

doi:10.1016/j.aca.2015.02.032

Cited by 35 publications

(27 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples include proteome scale comparative modeling of Corynebacterium pseudotuberculosis responsible for ulcerative lymphangitis, mastitis in ruminants (Hassan et al, 2014); RNA-seq profiling based target identification for non-small cell lung cancer (Riccardo et al, 2014); identification of markers for early prediction of preeclampsia using metabolic profiling (Kuc et al, 2014); computational systems biology approach for drug prioritization in Clostridium botulinum (Muhammad et al, 2014); and using protein-protein interaction network for drug target identification (Li et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Novel Drug Targets for Food-Borne PathogenCampylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study

Mehla

Ramana

2015

OMICS: A Journal of Integrative Biology

View full text Add to dashboard Cite

Campylobacters are a major global health burden and a cause of food-borne diarrheal illness and economic loss worldwide. In developing countries, Campylobacter infections are frequent in children under age two and may be associated with mortality. In developed countries, they are a common cause of bacterial diarrhea in early adulthood. In the United States, antibiotic resistance against Campylobacter is notably increased from 13% in 1997 to nearly 25% in 2011. Novel drug targets are urgently needed but remain a daunting task to accomplish. We suggest that omics-guided drug discovery is timely and worth considering in this context. The present study employed an integrated subtractive genomics and comparative metabolic pathway analysis approach. We identified 16 unique pathways from Campylobacter when compared against H. sapiens with 326 non-redundant proteins; 115 of these were found to be essential in the Database of Essential Genes. Sixty-six proteins among these were non-homologous to the human proteome. Six membrane proteins, of which four are transporters, have been proposed as potential vaccine candidates. Screening of 66 essential non-homologous proteins against DrugBank resulted in identification of 34 proteins with drug-ability potential, many of which play critical roles in bacterial growth and survival. Out of these, eight proteins had approved drug targets available in DrugBank, the majority serving crucial roles in cell wall synthesis and energy metabolism and therefore having the potential to be utilized as drug targets. We conclude by underscoring that screening against these proteins with inhibitors may aid in future discovery of novel therapeutics against campylobacteriosis in ways that will be pathogen specific, and thus have minimal toxic effect on host. Omics-guided drug discovery and bioinformatics analyses offer the broad potential for veritable advances in global health relevant novel therapeutics.

show abstract

Section: Discussionmentioning

confidence: 99%

Novel Drug Targets for Food-Borne PathogenCampylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study

Mehla

Ramana

2015

OMICS: A Journal of Integrative Biology

View full text Add to dashboard Cite

show abstract

“…As the comparison to our strategies, random sampling method for the negative construction was performed in our research, which was a prevailing practice [26]. In other words, 441 proteins were randomly picked up to form the set of most likely NDTPs in the training dataset.…”

Section: Sensitivity Analysis To Data Partitionmentioning

confidence: 99%

Efficient Data Mining Algorithms for Screening Potential Proteins of Drug Target

Wang¹,

Huang²,

Feng³

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The past few decades have witnessed the boom in pharmacology as well as the dilemma of drug development. Playing a crucial role in drug design, the screening of potential human proteins of drug targets from open access database with well-measured physical and chemical properties is a task of challenge but significance. In this paper, the screening of potential drug target proteins (DTPs) from a fine collected dataset containing 5376 unlabeled proteins and 517 known DTPs was researched. Our objective is to screen potential DTPs from the 5376 proteins. Here we proposed two strategies assisting the construction of dataset of reliable nondrug target proteins (NDTPs) and then bagging of decision trees method was employed in the final prediction. Such two-stage algorithms have shown their effectiveness and superior performance on the testing set. Both of the algorithms maintained higher recall ratios of DTPs, respectively, 93.5% and 97.4%. In one turn of experiments, strategy1-based bagging of decision trees algorithm screened about 558 possible DTPs while 1782 potential DTPs were predicted in the second algorithm. Besides, two strategy-based algorithms showed the consensus of the predictions in the results, with approximately 442 potential DTPs in common. These selected DTPs provide reliable choices for further verification based on biomedical experiments.

show abstract

“…Because of the breadth of data 487 we interrogated, the effort and expertise necessary to hand engineer features equally well across 488 all datasets exceeded our resources. Others have had success hand engineering features for 489 similar applications in the past, particularly with respect to computing topological properties of 490 targets in protein-protein interaction networks (18,20,21). This analysis could benefit from such 491 efforts, potentially changing a dataset or feature type from yielding no target features correlated 492 with phase III outcomes to yielding one or several useful features (22).…”

Section: Gene Expression Predicts Phase III Outcome 401 402mentioning

confidence: 99%

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Rouillard¹,

Hurle²,

Agarwal³

2017

Preprint

View full text Add to dashboard Cite

Target selection is the first and pivotal step in drug discovery. An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent. We collected a set of 332 targets that succeeded or failed in phase III clinical trials, and explored whether Omic features describing the target genes could predict clinical success. We obtained features from the recently published comprehensive resource: Harmonizome. Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes, but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias. We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features (AUROC=0.57 and AUPR=0.81). The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues, where successful targets tended to have lower mean expression and higher expression variance than failed targets. This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Overall, our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection. We anticipate deeper insights and better models in the future, as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features. Code, documentation, and data for this study have been deposited on GitHub athttps://github.com/arouillard/omic-features-successful-targets.AUTHOR SUMMARYDrug discovery often begins with a hypothesis that changing the abundance or activity of a target—a biological molecule, usually a protein—will cure a disease or ameliorate its symptoms. Whether a target hypothesis translates into a successful therapy depends in part on the characteristics of the target, but it is not completely understood which target characteristics are important for success. We sought to answer this question with a supervised machine learning approach. We obtained outcomes of target hypotheses tested in clinical trials, scoring targets as successful or failed, and then obtained thousands of features (i.e. properties or characteristics) of targets from dozens of biological datasets. We statistically tested which features differed between successful and failed targets, and built a computational model that used these features to predict success or failure of targets in clinical trials. We found that successful targets tended to have more variable mRNA abundance from tissue to tissue and lower average abundance across tissues than failed targets. Thus, it is probably favorable for a target to be present in the tissue(s) affected by a disease and absent from other tissues. Our work demonstrates the feasibility of predicting clinical trial outcomes from target features.

show abstract

Large-scale identification of potential drug targets based on the topological features of human protein–protein interaction network

Cited by 35 publications

References 44 publications

Novel Drug Targets for Food-Borne PathogenCampylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study

Novel Drug Targets for Food-Borne PathogenCampylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study

Efficient Data Mining Algorithms for Screening Potential Proteins of Drug Target

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

Contact Info

Product

Resources

About