Dataset Information

Single mutations in the ? subunit from thermophilic Bacillus PS3 generate a high binding affinity site for ATP.

ABSTRACT: The ? subunit from ATP synthases acts as an ATP sensor in the bacterial cell to prevent ATP hydrolysis and thus the waste of ATP under conditions of low ATP concentration. However, the ATP binding affinities from various bacterial organisms differ markedly, over several orders of magnitude. For example, the ATP synthases from thermophilic Bacillus PS3 and Escherichia coli exhibit affinities of 4 µM and 22 mM, respectively. The recently reported R103A/R115A double mutant of Bacillus PS3 ATP synthase demonstrated an increased binding affinity by two orders of magnitude with respect to the wild type. Here, we used atomic-resolution molecular dynamics simulations to determine the role of the R103A and R115A single mutations. These lead us to predict that both single mutations also cause an increased ATP binding affinity. Evolutionary analysis reveals R103 and R115 substitutions in the ? subunit from other bacillic organisms, leading us to predict they likely have a higher ATP binding affinity than previously expected.

SUBMITTER: Krah A

PROVIDER: S-EPMC6129141 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Single mutations in the ε subunit from thermophilic Bacillus PS3 generate a high binding affinity site for ATP.

Krah Alexander A Bond Peter J PJ

PeerJ 20180905

The ε subunit from ATP synthases acts as an ATP sensor in the bacterial cell to prevent ATP hydrolysis and thus the waste of ATP under conditions of low ATP concentration. However, the ATP binding affinities from various bacterial organisms differ markedly, over several orders of magnitude. For example, the ATP synthases from thermophilic Bacillus PS3 and Escherichia coli exhibit affinities of 4 µM and 22 mM, respectively. The recently reported R103A/R115A double mutant of Bacil ...[more]

PMID: 30202650

Similar Datasets

Project description:Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Dataset Information

Single mutations in the ? subunit from thermophilic Bacillus PS3 generate a high binding affinity site for ATP.

Publications

Single mutations in the ε subunit from thermophilic <i>Bacillus</i> PS3 generate a high binding affinity site for ATP.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets