Project: Predicting protonation states in proteins | Positions | Beckstein Lab

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Project: Predicting protonation states in proteins

Project: Predicting protonation states in proteins

Acids and bases exist in a chemical equilibrium between protonated and unprotonated forms. Switching of protonation states of amino acids in proteins is often at the heart of the mechanism of enzymes and transport proteins. The probability to find, say, the protonated form of a certain amino acid residue, is directly related to the free energy difference between the two forms, and is measured by the pKa. The chemical environment of a residue strongly influences the pKa. For instance, a low dielectric environment increases the probability to find the neutral form (e.g. protonated acid or de-protonated base) because the electrostatic energy of creating a charge is much higher in a low dielectric than in a high dielectric. A number of methods exist to calculate the pKa, ranging from heuristic approaches, through electrostatic Poisson-Boltzmann theory, to explicit molecular dynamics free energy calculations and quantum mechanical ab-initio calculations. However, no single method has emerged as the best one to use in all situations1. We recently used a combination of molecular dynamics simulations with the fast heuristic PROPKA algorithm2 to arrive at more robust predictions of pKa values for the NhaA transporter.3

In this project you will benchmark and extend the new prediction method. You will

  • analyze and visualize protein structures;
  • understand computational methods in the light of the underlying physics;
  • carry out pKa predictions;
  • use and write code for pKa calculation and analysis (and learning Python, a great language for scientific calculations);
  • integrate the method into our open source MDAnalysis Python library


1 E. Alexov, E. L. Mehler, N. Baker, A. M. Baptista, Y. Huang, F. Milletti, J. E. Nielsen, D. Farrell, T. Carstensen, M. H. M. Olsson, J. K. Shen, J. Warwicker, S. Williams, and J. M. Word. Progress in the prediction of pKa values in proteins. Proteins, 79(12):3260-75, 2011. 10.1002/prot.23189.

2 Mats H.M. Olsson, Chresten R. Søndergard, Michal Rostkowski, and Jan H. Jensen. PROPKA3: Consistent Treatment of Internal and Surface Residues in Empirical pKa predictions. Journal of Chemical Theory and Computation, 2011 7 (2), 525-537 doi: 10.1021/ct100578z.

3 C. Lee, S. Yashiro, D.L. Dotson, P. Uzdavinys, S. Iwata, M.S.P. Sansom, C. von Ballmoos, O. Beckstein, D. Drew, and A.D. Cameron. Crystal structure of the sodium-proton antiporter NhaA dimer and new mechanistic insights. J Gen Physiol 144 (2014), 529-544. doi: 10.1085/jgp.201411219 (ASU repository item 27919 )

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