Dataset Information

Arg302 governs the pKa of Glu325 in LacY.

ABSTRACT: Lactose permease is a paradigm for the major facilitator superfamily, the largest family of ion-coupled membrane transport proteins known at present. LacY carries out the coupled stoichiometric symport of a galactoside with an H+, using the free energy released from downhill translocation of H+ to drive accumulation of galactosides against a concentration gradient. In neutrophilic Escherichia coli, internal pH is kept at ?7.6 over the physiological range, but the apparent pK (pKapp) for galactoside binding is 10.5. Surface-enhanced infrared absorption spectroscopy (SEIRAS) demonstrates that the high pKa is due to Glu325 (helix X), which must be protonated for LacY to bind galactoside effectively. Deprotonation is also obligatory for turnover, however. Here, we utilize SEIRAS to study the effect of mutating residues in the immediate vicinity of Glu325 on its pKa The results are consistent with the idea that Arg302 (helix IX) is important for deprotonation of Glu325.

SUBMITTER: Grytsyk N

PROVIDER: S-EPMC6421430 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Arg302 governs the pKa of Glu325 in LacY.

Grytsyk Natalia N Santos Seiça Ana Filipa AF Sugihara Junichi J Kaback H Ronald HR Hellwig Petra P

Proceedings of the National Academy of Sciences of the United States of America 20190221 11

Lactose permease is a paradigm for the major facilitator superfamily, the largest family of ion-coupled membrane transport proteins known at present. LacY carries out the coupled stoichiometric symport of a galactoside with an H+, using the free energy released from downhill translocation of H+ to drive accumulation of galactosides against a concentration gradient. In neutrophilic Escherichia coli, internal pH is kept at ∼7.6 over the physiological range, but the app ...[more]

PMID: 30792352

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Project description:The prediction of acid dissociation constants (pKa) is a prerequisite for predicting many other properties of a small molecule, such as its protein-ligand binding affinity, distribution coefficient (log D), membrane permeability, and solubility. The prediction of each of these properties requires knowledge of the relevant protonation states and solution free energy penalties of each state. The SAMPL6 pKa Challenge was the first time that a separate challenge was conducted for evaluating pKa predictions as part of the Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) exercises. This challenge was motivated by significant inaccuracies observed in prior physical property prediction challenges, such as the SAMPL5 log D Challenge, caused by protonation state and pKa prediction issues. The goal of the pKa challenge was to assess the performance of contemporary pKa prediction methods for drug-like molecules. The challenge set was composed of 24 small molecules that resembled fragments of kinase inhibitors, a number of which were multiprotic. Eleven research groups contributed blind predictions for a total of 37 pKa distinct prediction methods. In addition to blinded submissions, four widely used pKa prediction methods were included in the analysis as reference methods. Collecting both microscopic and macroscopic pKa predictions allowed in-depth evaluation of pKa prediction performance. This article highlights deficiencies of typical pKa prediction evaluation approaches when the distinction between microscopic and macroscopic pKas is ignored; in particular, we suggest more stringent evaluation criteria for microscopic and macroscopic pKa predictions guided by the available experimental data. Top-performing submissions for macroscopic pKa predictions achieved RMSE of 0.7-1.0 pKa units and included both quantum chemical and empirical approaches, where the total number of extra or missing macroscopic pKas predicted by these submissions were fewer than 8 for 24 molecules. A large number of submissions had RMSE spanning 1-3 pKa units. Molecules with sulfur-containing heterocycles or iodo and bromo groups were less accurately predicted on average considering all methods evaluated. For a subset of molecules, we utilized experimentally-determined microstates based on NMR to evaluate the dominant tautomer predictions for each macroscopic state. Prediction of dominant tautomers was a major source of error for microscopic pKa predictions, especially errors in charged tautomers. The degree of inaccuracy in pKa predictions observed in this challenge is detrimental to the protein-ligand binding affinity predictions due to errors in dominant protonation state predictions and the calculation of free energy corrections for multiple protonation states. Underestimation of ligand pKa by 1 unit can lead to errors in binding free energy errors up to 1.2 kcal/mol. The SAMPL6 pKa Challenge demonstrated the need for improving pKa prediction methods for drug-like molecules, especially for challenging moieties and multiprotic molecules.

Dataset Information

Arg302 governs the pKa of Glu325 in LacY.

Publications

Arg302 governs the pK<sub>a</sub> of Glu325 in LacY.

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