Escherichia coli NhaA is a prototypical sodium–proton antiporter responsible for maintaining cellular ion and volume homeostasis by exchanging two protons for one sodium ion; despite two decades of ...research, the transport mechanism of NhaA remains poorly understood. Recent crystal structure and computational studies suggested Lys300 as a second proton-binding site; however, functional measurements of several K300 mutants demonstrated electrogenic transport, thereby casting doubt on the role of Lys300. To address the controversy, we carried out state-of-the-art continuous constant pH molecular dynamics simulations of NhaA mutants K300A, K300R, K300Q/D163N, and K300Q/D163N/D133A. Simulations suggested that K300 mutants maintain the electrogenic transport by utilizing an alternative proton-binding residue Asp133. Surprisingly, while Asp133 is solely responsible for binding the second proton in K300R, Asp133 and Asp163 jointly bind the second proton in K300A, and Asp133 and Asp164 jointly bind two protons in K300Q/D163N. Intriguingly, the coupling between Asp133 and Asp163 or Asp164 is enabled through the proton-coupled hydrogen-bonding network at the flexible intersection of two disrupted helices. These data resolve the controversy and highlight the intricacy of the compensatory transport mechanism of NhaA mutants. Alternative proton-binding site and proton sharing between distant aspartates may represent important general mechanisms of proton-coupled transport in secondary active transporters.
DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web ...server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent.
DeepKa is a deep-learning-based protein p
predictor proposed in our previous work. In this study, a web server was developed that enables online protein p
prediction driven by DeepKa. The web server ...provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how p
's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.
DeepKa is a deep-learning-based protein pK a predictor proposed in our previous work. In this study, a web server was developed that enables online protein pK a prediction driven by DeepKa. The web ...server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pK a’s calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.
pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by ...pKa's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pKa prediction is crucial. Here we present a theoretical pKa data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pKa orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pKa prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pKa predictor and thus can be applied immediately to, for example, pKa database construction, protein design, drug discovery, and so on.
pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by ...pK a’s. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pK a prediction is crucial. Here we present a theoretical pK a data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pK a orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pK a prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pK a predictor and thus can be applied immediately to, for example, pK a database construction, protein design, drug discovery, and so on.
pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by p
...'s. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate p
prediction is crucial. Here we present a theoretical p
data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental p
orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein p
prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein p
predictor and thus can be applied immediately to, for example, p
database construction, protein design, drug discovery, and so on.
We have studied the effects of different 3d orbitals in divalent transition-metal ions G2+ = Mn2+ (d5), Fe2+ (d6), Co2+ (d7), Ni2+ (d8), Cu2+ (d9), or Zn2+ (d10) on the conformations of leucine ...encephalin (LE) and methionine encephalin (ME) in the gas phase using hydrogen/deuterium exchange mass spectrometry (HDX-MS) and theoretical calculations at the molecular level. The HDX-MS reveals a 1:1 stoichiometric monovalent complex of LE/ME + G – H+ and observed that the different HDX reactivities follow the trend Fe2+ < Co2+ < Ni2+ < Mn2+ < Cu2+ ≈ Zn2+ and that ME + Mn/Cu/Zn – H+ > LE + Mn/Cu/Zn – H+, while LE + Fe/Co/Ni – H+ > ME + Fe/Co/Ni – H+. We cross-correlated the collision-induced dissociation energies of the complexes with the HDX results and found that the more stable the complex, the harder it is for it to undergo HDX. Furthermore, we used theoretical calculations to optimize the favorable conformations of the complexes and found the same interaction structure of G2+ coordination with the five carbonyl oxygens of LE/ME that have different bond lengths. Finally, we calculated the proton affinity (PA) values of the optimized complexes in order to interpret the HDX observations that the higher the PA values, the more difficult it is for the complex to undergo HDX. Overall, both the experiments and the theoretical calculations show that the six metal ions have different effects on the LE/ME conformation, with the low-energy stability of the G2+ 3d orbitals corresponding to more dramatic effects on the LE/ME conformation. In addition, the hardness of the ionic acid corresponding to the fully filled Mn2+ and half-filled Zn2+ orbitals also contributes strongly to the coordination effect; the conformation effect of Fe2+/Co2+/Ni2+ on LE is greater than that on ME, whereas the conformation effect of Mn2+/Cu2+/Zn2+ on ME is greater than that on LE.
Protein pK a prediction is essential for the investigation of the pH-associated relationship between protein structure and function. In this work, we introduce a deep learning-based protein pK a ...predictor DeepKa, which is trained and validated with the pK a values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD implemented in the Amber molecular dynamics package has been employed ( Huang, Y. J. Chem. Inf. Model. 2018, 58, 1372−1383 ). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pK a predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein pK a predictors in the future. Finally, the grid charge representation is general and applicable to other topics, such as the protein–ligand binding affinity prediction.
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