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Prediction of peptides binding to the PKA RIIalpha subunit using a hierarchical strategy.

ABSTRACT: Favorable interaction between the regulatory subunit of the cAMP-dependent protein kinase (PKA) and a peptide in A-kinase anchoring proteins (AKAPs) is critical for translocating PKA to the subcellular sites where the enzyme phosphorylates its substrates. It is very hard to identify AKAPs peptides binding to PKA due to the high sequence diversity of AKAPs.We propose a hierarchical and efficient approach, which combines molecular dynamics (MD) simulations, free energy calculations, virtual mutagenesis (VM) and bioinformatics analyses, to predict peptides binding to the PKA RII? regulatory subunit in the human proteome systematically. Our approach successfully retrieved 15 out of 18 documented RII?-binding peptides. Literature curation supported that many newly predicted peptides might be true AKAPs. Here, we present the first systematic search for AKAP peptides in the human proteome, which is useful to further experimental identification of AKAPs and functional analysis of their biological roles.

SUBMITTER: Hou T

PROVIDER: S-EPMC3117377 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Prediction of peptides binding to the PKA RIIalpha subunit using a hierarchical strategy.

Hou Tingjun T Li Youyong Y Wang Wei W

Bioinformatics (Oxford, England) 20110517 13

<h4>Motivation</h4>Favorable interaction between the regulatory subunit of the cAMP-dependent protein kinase (PKA) and a peptide in A-kinase anchoring proteins (AKAPs) is critical for translocating PKA to the subcellular sites where the enzyme phosphorylates its substrates. It is very hard to identify AKAPs peptides binding to PKA due to the high sequence diversity of AKAPs.<h4>Results</h4>We propose a hierarchical and efficient approach, which combines molecular dynamics (MD) simulations, free ...[more]

PMID: 21586518

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Project description:BackgroundThe logarithmic acid dissociation constant pKa reflects the ionization of a chemical, which affects lipophilicity, solubility, protein binding, and ability to pass through the plasma membrane. Thus, pKa affects chemical absorption, distribution, metabolism, excretion, and toxicity properties. Multiple proprietary software packages exist for the prediction of pKa, but to the best of our knowledge no free and open-source programs exist for this purpose. Using a freely available data set and three machine learning approaches, we developed open-source models for pKa prediction.MethodsThe experimental strongest acidic and strongest basic pKa values in water for 7912 chemicals were obtained from DataWarrior, a freely available software package. Chemical structures were curated and standardized for quantitative structure-activity relationship (QSAR) modeling using KNIME, and a subset comprising 79% of the initial set was used for modeling. To evaluate different approaches to modeling, several datasets were constructed based on different processing of chemical structures with acidic and/or basic pKas. Continuous molecular descriptors, binary fingerprints, and fragment counts were generated using PaDEL, and pKa prediction models were created using three machine learning methods, (1) support vector machines (SVM) combined with k-nearest neighbors (kNN), (2) extreme gradient boosting (XGB) and (3) deep neural networks (DNN).ResultsThe three methods delivered comparable performances on the training and test sets with a root-mean-squared error (RMSE) around 1.5 and a coefficient of determination (R2) around 0.80. Two commercial pKa predictors from ACD/Labs and ChemAxon were used to benchmark the three best models developed in this work, and performance of our models compared favorably to the commercial products.ConclusionsThis work provides multiple QSAR models to predict the strongest acidic and strongest basic pKas of chemicals, built using publicly available data, and provided as free and open-source software on GitHub.

Dataset Information

Prediction of peptides binding to the PKA RIIalpha subunit using a hierarchical strategy.

Publications

Prediction of peptides binding to the PKA RIIalpha subunit using a hierarchical strategy.

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