The pKa-cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pKa values and protein electrostatics in general. The first round of the pKa-cooperative, which challenged computational labs to carry out blind predictions against pKas experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6–10, 2009). This paper serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here we briefly outline existing approaches for pKa calculations, emphasizing methods that were used by the participants in calculating the blind pKa values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pKa calculations.

Site-specific pK(a) values measured by NMR spectroscopy provide essential information on protein electrostatics, the pH-dependence of protein structure, dynamics and function, and constitute an important benchmark for protein pK(a) calculation algorithms. Titration curves can be measured by tracking the NMR chemical shifts of several reporter nuclei versus sample pH. However, careful analysis of these curves is needed to extract residue-specific pK(a) values since pH-dependent chemical shift changes can arise from many sources, including through-bond inductive effects, through-space electric field effects, and conformational changes. We have re-measured titration curves for all carboxylates and His 15 in Hen Egg White Lysozyme (HEWL) by recording the pH-dependent chemical shifts of all backbone amide nitrogens and protons, Asp/Glu side chain protons and carboxyl carbons, and imidazole protonated carbons and protons in this protein. We extracted pK(a) values from the resulting titration curves using standard fitting methods, and compared these values to each other, and with those measured previously by ¹H NMR (Bartik et al., Biophys J 1994;66:1180–1184). This analysis gives insights into the true accuracy associated with experimentally measured pK(a) values. We find that apparent pK(a) values frequently differ by 0.5–1.0 units depending upon the nuclei monitored, and that larger differences occasionally can be observed. The variation in measured pK(a) values, which reflects the difficulty in fitting and assigning pH-dependent chemical shifts to specific ionization equilibria, has significant implications for the experimental procedures used for measuring protein pK(a) values, for the benchmarking of protein pK(a) calculation algorithms, and for the understanding of protein electrostatics in general.

NMR-monitored pH titration experiments are routinely used to measure site-specific protein pKa values. Accurate experimental pKa values are essential in dissecting enzyme catalysis, in studying the pH-dependence of protein stability and ligand binding, in benchmarking pKa prediction algorithms, and ultimately in understanding electrostatic effects in proteins. However, due to the complex ways in which pH-dependent electrostatic and structural changes manifest themselves in NMR spectra, reported apparent pKa values are often dependent on the way that NMR pH-titration curves are analyzed. It is therefore important to retain the raw NMR spectroscopic data to allow for documentation and possible re-interpretation. We have constructed a database of primary NMR pH-titration data, which is accessible via a web interface. Here, we report statistics of the database contents and analyze the data with a global perspective to provide guidelines on best practice for fitting NMR titration curves. Titration_DB is available at http://enzyme.ucd.ie/Titration_DB. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

Chaperones are protein complexes that help to fold and disaggregate a cell's proteins. It is not understood how four major chaperone systems of Escherichia coli work together in proteostasis: the recognition, sorting, folding, and disaggregating of the cell's many different proteins. Here, we model this machine. We combine extensive data on chaperoning, folding, and aggregation rates with expression levels of proteins and chaperones measured at different growth rates. We find that the proteostasis machine recognizes and sorts a client protein based on two biophysical properties of the client's misfolded state (M state): its stability and its kinetic accessibility from its unfolded state (U state). The machine is energy-efficient (the sickest proteins use the most ATP-expensive chaperones), comprehensive (it can handle any type of protein), and economical (the chaperone concentrations are just high enough to keep the whole proteome folded and disaggregated but no higher). The cell needs higher chaperone levels in two situations: fast growth (when protein production rates are high) and very slow growth (to mitigate the effects of protein degradation). This type of model complements experimental knowledge by showing how the various chaperones work together to achieve the broad folding and disaggregation needs of the cell.proteostasis | chaperone | protein folding | shields up | shields down A major action of cells is proteostasis (1-4). A cell's proteostasis "machine" is the collection of chaperones and synthesis and degradation processes that maintain the homeostatic balance of the folding and disaggregation of the cell's proteins. It is a machine in the sense that it is an energy-driven cyclic device that has component parts that work together to create its action. Proteostasis can become unbalanced under stresses, such as temperature, osmotic shock, oxidation, or drugs, or different growth conditions. Proteome health can fail if the machine is pushed beyond its tipping point (for example, in cell aging, cancer, or neurodegenerative diseases, such as Alzheimer's and Parkinson's) (1, 2, 4, 5).Much is now understood about the component parts (i.e., the structures of some chaperones, the folding equilibria and kinetics of isolated proteins in vitro, and the rates at which particular chaperones help fold and disaggregate particular proteins). The organism in which this is best understood is arguably Escherichia coli. What is not yet known is how the component chaperones act together as a machine on the many different proteins to meet the cell's needs. It is not known how "decisions" are made for trafficking different proteins through different chaperones.Cells have multiple types of chaperones. Also, different classes of proteins have different relationships with each chaperone (6). E. coli has four major chaperone systems: GroEL/GroES (GroE), DnaK/DnaJ/GrpE (KJE), Trigger Factor (TF), and ClpB (B) (7). Complex cells have more (8). E. coli proteins fall into three classes of interaction with GroEL (7): class I protei...

Understanding the connection between protein structure and function requires a quantitative understanding of electrostatic effects. Structure-based electrostatics calculations are essential for this purpose, but their use have been limited by a long-standing discussion on which value to use for the dielectric constants (εeff and εp) required in Coulombic models and Poisson-Boltzmann models. The currently used values for εeff and εp are essentially empirical parameters calibrated against thermodynamic properties that are indirect measurements of protein electric fields. We determine optimal values for εeff and εp by measuring protein electric fields in solution using direct detection of NMR chemical shift perturbations (CSPs). We measured CSPs in fourteen proteins to get a broad and general characterization of electric fields. Coulomb's law reproduces the measured CSPs optimally with a protein dielectric constant (εeff) from 3 to 13, with an optimal value across all proteins of 6.5. However, when the water-protein interface is treated with finite difference Poisson-Boltzmann calculations, the optimal protein dielectric constant (εp) rangedsfrom 2-5 with an optimum of 3. It is striking how similar this value is to the dielectric constant of 2-4 measured for protein powders, and how different it is from the εp of 6-20 used in models based on the Poisson-Boltzmann equation when calculating thermodynamic parameters. Because the value of εp = 3 is obtained by analysis of NMR chemical shift perturbations instead of thermodynamic parameters such as pKa values, it is likely to describe only the electric field and thus represent a more general, intrinsic, and transferable εp common to most folded proteins.