Dataset Information

Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses.

ABSTRACT:

Motivation

Evolution couples differences in ambient pH to biological function through protonatable groups, in particular, those that switch from buried to exposed and alter protonation state in doing so. We present a tool focusing on structure-based discovery and display of these groups.

Results

Since prediction of buried group pKas is computationally intensive, solvent accessibility of ionizable groups is displayed, from which the user can iteratively select pKa calculation centers. Results are color-coded, with emphasis on buried groups. Utility is demonstrated with benchmarking against known pH sensing sites in influenza virus hemagglutinin and in variants of murine hepatitis virus, a coronavirus. A pair of histidine residues, which are conserved in coronavirus spike proteins, are predicted to be electrostatically frustrated at acidic pH in both pre- and post-fusion conformations. We suggest that an intermediate expanded conformation at endosomal pH could relax the frustration, allowing histidine protonation and facilitating conformational conversion of coronavirus spike protein.

Availability and implementation

This tool is available at http://www.protein-sol.manchester.ac.uk/pka/.

SUBMITTER: Hebditch M

PROVIDER: S-EPMC7454282 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses.

Hebditch Max M Warwicker Jim J

Bioinformatics (Oxford, England) 20201201 20

<h4>Motivation</h4>Evolution couples differences in ambient pH to biological function through protonatable groups, in particular, those that switch from buried to exposed and alter protonation state in doing so. We present a tool focusing on structure-based discovery and display of these groups.<h4>Results</h4>Since prediction of buried group pKas is computationally intensive, solvent accessibility of ionizable groups is displayed, from which the user can iteratively select pKa calculation cente ...[more]

PMID: 32683439

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Project description:BackgroundPrediction and analysis of protein-protein interactions (PPI) and specifically types of PPIs is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. In addition, electrostatic interactions are important in understanding inter-molecular interactions, since they are long-range, and because of their influence in charged molecules. This is the main motivation for using electrostatic energy for prediction of PPI types.ResultsWe propose a prediction model to analyze protein interaction types, namely obligate and non-obligate, using electrostatic energy values as properties. The prediction approach uses electrostatic energy values for pairs of atoms and amino acids present in interfaces where the interaction occurs. The main features of the complexes are found and then the prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). For an in-depth analysis of classification results, some other experiments were performed by varying the distance cutoffs between atom pairs of interacting chains, ranging from 5Å to 13Å. Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative pairs of atom types and amino acid types as features for prediction.ConclusionsOur results on two well-known datasets of obligate and non-obligate complexes confirm that electrostatic energy is an important property to predict obligate and non-obligate protein interaction types on the basis of all the experimental results, achieving accuracies of over 98%. Furthermore, a comparison performed by changing the distance cutoff demonstrates that the best values for prediction of PPI types using electrostatic energy range from 9Å to 12Å, which show that electrostatic interactions are long-range and cover a broader area in the interface. In addition, the results on using feature selection before prediction confirm that (a) a few pairs of atoms and amino acids are appropriate for prediction, and (b) prediction performance can be improved by eliminating irrelevant and noisy features and selecting the most discriminative ones.

Dataset Information

Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses.

Motivation

Results

Availability and implementation

Publications

Protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses.

Similar Datasets

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