Dataset Information

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.

ABSTRACT: Ensemble docking is a widely applied concept in structure-based virtual screening-to at least partly account for protein flexibility-usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases- and in this study as well-this is the minimum score). Nonetheless, there are a number of other fusion rules that can be applied. We report here the results of a detailed statistical comparison of seven fusion rules for ensemble docking, on five case studies of current drug targets, based on four performance metrics. Sevenfold cross-validation and variance analysis (ANOVA) allowed us to highlight the best fusion rules. The results are presented in bubble plots, to unite the four performance metrics into a single, comprehensive image. Notably, we suggest the use of the geometric and harmonic means as better alternatives to the generally applied minimum fusion rule.

SUBMITTER: Bajusz D

PROVIDER: S-EPMC6695709 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.

Bajusz Dávid D Rácz Anita A Héberger Károly K

Molecules (Basel, Switzerland) 20190724 15

Ensemble docking is a widely applied concept in structure-based virtual screening-to at least partly account for protein flexibility-usually granting a significant performance gain at a modest cost of speed. From the individual, single-structure docking scores, a consensus score needs to be produced by data fusion: this is usually done by taking the best docking score from the available pool (in most cases- and in this study as well-this is the minimum score). Nonetheless, there are a number of ...[more]

PMID: 31344902

Similar Datasets

Project description:BackgroundmicroRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.ResultsIn this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.

Project description:BACKGROUND:Mycobacterium tuberculosis encodes 11 putative serine-threonine proteins Kinases (STPK) which regulates transcription, cell development and interaction with the host cells. From the 11 STPKs three kinases namely PknA, PknB and PknG have been related to the mycobacterial growth. From previous studies it has been observed that PknB is essential for mycobacterial growth and expressed during log phase of the growth and phosphorylates substrates involved in peptidoglycan biosynthesis. In recent years many high affinity inhibitors are reported for PknB. Previously implementation of data fusion has shown effective enrichment of active compounds in both structure and ligand based approaches .In this study we have used three types of data fusion ranking algorithms on the PknB dataset namely, sum rank, sum score and reciprocal rank. We have identified reciprocal rank algorithm is capable enough to select compounds earlier in a virtual screening process. We have also screened the Asinex database with reciprocal rank algorithm to identify possible inhibitors for PknB. RESULTS:In our work we have used both structure-based and ligand-based approaches for virtual screening, and have combined their results using a variety of data fusion methods. We found that data fusion increases the chance of actives being ranked highly. Specifically, we found that the ranking of Pharmacophore search, ROCS and Glide XP fused with a reciprocal ranking algorithm not only outperforms structure and ligand based approaches but also capable of ranking actives better than the other two data fusion methods using the BEDROC, robust initial enhancement (RIE) and AUC metrics. These fused results were used to identify 45 candidate compounds for further experimental validation. CONCLUSION:We show that very different structure and ligand based methods for predicting drug-target interactions can be combined effectively using data fusion, outperforming any single method in ranking of actives. Such fused results show promise for a coherent selection of candidates for biological screening.

Dataset Information

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.

Publications

Comparison of Data Fusion Methods as Consensus Scores for Ensemble Docking.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets