Dataset Information

Exponential consensus ranking improves the outcome in docking and receptor ensemble docking.

ABSTRACT: Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.

SUBMITTER: Palacio-Rodriguez K

PROVIDER: S-EPMC6435795 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Exponential consensus ranking improves the outcome in docking and receptor ensemble docking.

Palacio-Rodríguez Karen K Lans Isaias I Cavasotto Claudio N CN Cossio Pilar P

Scientific reports 20190326 1

Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this ...[more]

PMID: 30914702

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Project description:AimConservation planning exercises increasingly rely on species distributions predicted either from one particular statistical model or, more recently, from an ensemble of models (i.e. ensemble forecasting). However, it has not yet been explored how different ways of summarizing ensemble predictions affect conservation planning outcomes. We evaluate these effects and compare commonplace consensus methods, applied before the conservation prioritization phase, to a novel method that applies consensus after reserve selection.LocationEurope.MethodsWe used an ensemble of predicted distributions of 146 Western Palaearctic bird species in alternative ways: four different consensus methods, as well as distributions discounted with variability, were used to produce inputs for spatial conservation prioritization. In addition, we developed and tested a novel method, in which we built 100 datasets by sampling the ensemble of predicted distributions, ran a conservation prioritization analysis on each of them and averaged the resulting priority ranks. We evaluated the conservation outcome against three controls: (i) a null control, based on random ranking of cells; (2) the reference solution, based on an expert-refined dataset; and (3) the independent solution, based on an independent dataset.ResultsNetworks based on predicted distributions were more representative of rare species than randomly selected networks. Alternative methods to summarize ensemble predictions differed in representativeness of resulting reserve networks. Our novel method resulted in better representation of rare species than pre-selection consensus methods.Main conclusionsRetaining information about the variation in the predicted distributions throughout the conservation prioritization seems to provide better results than summarizing the predictions before conservation prioritization. Our results highlight the need to understand and consider model-based uncertainty when using predicted distribution data in conservation prioritization.

Dataset Information

Exponential consensus ranking improves the outcome in docking and receptor ensemble docking.

Publications

Exponential consensus ranking improves the outcome in docking and receptor ensemble docking.

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