Metal-Ion-Specific Screening of Charge Effects in Protein Amide H/D Exchange and the Hofmeister Series

A method for quantifying how the activity of an enzyme is affected by the net charge of its nearest crowded neighbor

The electrostatic effects of protein crowding have not been systematically explored. Rather, protein crowding is generally studied with co‐solvents or crowders that are electrostatically neutral, with no methods to measure how the net charge (Z) of a crowder affects protein function. For example, can the activity of an enzyme be affected electrostatically by the net charge of its neighbor in crowded milieu? This paper reports a method for crowding proteins of different net charge to an enzyme via semi‐random chemical crosslinking. As a proof of concept, RNase A was crowded (at distances ≤ the Debye length) via crosslinking to different heme proteins with Z = +8.50 ± 0.04, Z = +6.39 ± 0.12, or Z = −10.30 ± 1.32. Crosslinking did not disrupt the structure of proteins, according to amide H/D exchange, and did not inhibit RNase A activity. For RNase A, we found that the electrostatic environment of each crowded neighbor had significant effects on rates of RNA hydrolysis. Crowding with cationic cytochrome c led to increases in activity, while crowding with anionic “supercharged” cytochrome c or myoglobin diminished activity. Surprisingly, electrostatic crowding effects were amplified at high ionic strength (I = 0.201 M) and attenuated at low ionic strength (I = 0.011 M). This salt dependence might be caused by a unique set of electric double layers at the dimer interspace (maximum distance of 8 Å, which cannot accommodate four layers). This new method of crowding via crosslinking can be used to search for electrostatic effects in protein crowding.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

Prediction and Validation of a Protein's Free Energy Surface Using Hydrogen Exchange and (Importantly) Its Denaturant Dependence

The denaturant dependence of hydrogen-deuterium exchange (HDX) is a powerful measurement to identify the breaking of individual H-bonds and map the free energy surface (FES) of a protein including the very rare states. Molecular dynamics (MD) can identify each partial unfolding event with atomic-level resolution. Hence, their combination provides a great opportunity to test the accuracy of simulations and to verify the interpretation of HDX data. For this comparison, we use Upside, our new and extremely fast MD package that is capable of folding proteins with an accuracy comparable to that of all-atom methods. The FESs of two naturally occurring and two designed proteins are so generated and compared to our NMR/HDX data. We find that Upside's accuracy is considerably improved upon modifying the energy function using a new machine-learning procedure that trains for proper protein behavior including realistic denatured states in addition to stable native states. The resulting increase in cooperativity is critical for replicating the HDX data and protein stability, indicating that we have properly encoded the underlying physiochemical interactions into an MD package. We did observe some mismatch, however, underscoring the ongoing challenges faced by simulations in calculating accurate FESs. Nevertheless, our ensembles can identify the properties of the fluctuations that lead to HDX, whether they be small-, medium-, or large-scale openings, and can speak to the breadth of the native ensemble that has been a matter of debate.

Measuring how two proteins affect each other's net charge in a crowded environment

Theory predicts that the net charge (Z) of a protein can be altered by the net charge of a neighboring protein as the two approach one another below the Debye length. This type of charge regulation suggests that a protein's charge and perhaps function might be affected by neighboring proteins without direct binding. Charge regulation during protein crowding has never been directly measured due to analytical challenges. Here, we show that lysine specific protein crosslinkers (NHS ester-Staudinger pairs) can be used to mimic crowding by linking two non-interacting proteins at a maximal distance of ~7.9 Å. The net charge of the regioisomeric dimers and preceding monomers can then be determined with lysine-acyl "protein charge ladders" and capillary electrophoresis. As a proof of concept, we covalently linked myoglobin (Z_monomer  = -0.43 ± 0.01) and α-lactalbumin (Z_monomer  = -4.63 ± 0.05). Amide hydrogen/deuterium exchange and circular dichroism spectroscopy demonstrated that crosslinking did not significantly alter the structure of either protein or result in direct binding (thus mimicking crowding). Ultimately, capillary electrophoretic analysis of the dimeric charge ladder detected a change in charge of ΔZ = -0.04 ± 0.09 upon crowding by this pair (Z_dimer  = -5.10 ± 0.07). These small values of ΔZ are not necessarily general to protein crowding (qualitatively or quantitatively) but will vary per protein size, charge, and solvent conditions.

Studies of the highly potent lantibiotic peptide nisin Z in aqueous solutions of salts and biological buffer components

The lantibiotic nisin, usually used as a 2.5%w/w in NaCl and milk solids, has activity against a wide range of Gram-positive bacteria, especially food-borne pathogens, and has been used as a food preservative for decades without the development of significant resistance. It has been reported that the high purity (>95%) nisin Z form has activity against the Gram-negative speciesE. coli, which is significantly reduced in the presence of NaCl. This current study examined, by¹H NMR spectroscopy, the effects of NaCl, and a range of other salts, on the observed aqueous solution¹H NMR spectra of nisin Z in the pH 3-4 range, where nisin Z has its maximum stability. Nisin's mechanism of action involves binding to the polyoxygenated pyrophosphate moiety of lipid II, and in acidic solution the positively charged C-terminus region is reported to interact with the negative sulfate groups of SDS micelles, so the study was extended to include a number of polyoxygenated anions commonly used as buffers in many biological assays. In general, the biggest changes found were in the chemical shifts of protons in the hydrophobic N-terminus region, rather than the more polar C-terminus region. The effects seen on the addition of the salts (cations and anions) were not just an overall non-specific ionic strength effect, as different salts caused different effects, in an unpredictive manner. Similarly, the polyoxygenated anions behaved differently and not predictably, and neither the cations/anions, or polyoxygenated anions, constitute a Hofmeister or inverse Hofmeister series.

What Are We Missing by Not Measuring the Net Charge of Proteins?

The net electrostatic charge (Z) of a folded protein in solution represents a bird's eye view of its surface potentials-including contributions from tightly bound metal, solvent, buffer, and cosolvent ions-and remains one of its most enigmatic properties. Few tools are available to the average biochemist to rapidly and accurately measure Z at pH≠pI. Tools that have been developed more recently seem to go unnoticed. Most scientists are content with this void and estimate the net charge of a protein from its amino acid sequence, using textbook values of pK_a . Thus, Z remains unmeasured for nearly all folded proteins at pH≠pI. When marveling at all that has been learned from accurately measuring the other fundamental property of a protein-its mass-one wonders: what are we missing by not measuring the net charge of folded, solvated proteins? A few big questions immediately emerge in bioinorganic chemistry. When a single electron is transferred to a metalloprotein, does the net charge of the protein change by approximately one elementary unit of charge or does charge regulation dominate, that is, do the pK_a values of most ionizable residues (or just a few residues) adjust in response to (or in concert with) electron transfer? Would the free energy of charge regulation (ΔΔG_z ) account for most of the outer sphere reorganization energy associated with electron transfer? Or would ΔΔG_z contribute more to the redox potential? And what about metal binding itself? When an apo-metalloprotein, bearing minimal net negative charge (e.g., Z=-2.0) binds one or more metal cations, is the net charge abolished or inverted to positive? Or do metalloproteins regulate net charge when coordinating metal ions? The author's group has recently dusted off a relatively obscure tool-the "protein charge ladder"-and used it to begin to answer these basic questions.

Direct Measurement of Charge Regulation in Metalloprotein Electron Transfer

Determining whether a protein regulates its net electrostatic charge during electron transfer (ET) will deepen our mechanistic understanding of how polypeptides tune rates and free energies of ET (e.g., by affecting reorganization energy, and/or redox potential). Charge regulation during ET has never been measured for proteins because few tools exist to measure the net charge of a folded protein in solution at different oxidation states. Herein, we used a niche analytical tool (protein charge ladders analyzed with capillary electrophoresis) to determine that the net charges of myoglobin, cytochrome c, and azurin change by 0.62±0.06, 1.19±0.02, and 0.51±0.04 units upon single ET. Computational analysis predicts that these fluctuations in charge arise from changes in the pKa  values of multiple non-coordinating residues (predominantly histidine) that involve between 0.42-0.90 eV. These results suggest that ionizable residues can tune the reactivity of redox centers by regulating the net charge of the entire protein-cofactor-solvent complex.

Arresting amyloid with coulomb's law: acetylation of ALS-linked SOD1 by aspirin impedes aggregation

Although the magnitude of a protein's net charge (Z) can control its rate of self-assembly into amyloid, and its interactions with cellular membranes, the net charge of a protein is not viewed as a druggable parameter. This article demonstrates that aspirin (the quintessential acylating pharmacon) can inhibit the amyloidogenesis of superoxide dismutase (SOD1) by increasing the intrinsic net negative charge of the polypeptide, i.e., by acetylation (neutralization) of multiple lysines. The protective effects of acetylation were diminished (but not abolished) in 100 mM NaCl and were statistically significant: a total of 432 thioflavin-T amyloid assays were performed for all studied proteins. The acetylation of as few as three lysines by aspirin in A4V apo-SOD1-a variant that causes familial amyotrophic lateral sclerosis (ALS)-delayed amyloid nucleation by 38% and slowed amyloid propagation by twofold. Lysines in wild-type- and ALS-variant apo-SOD1 could also be peracetylated with aspirin after fibrillization, resulting in supercharged fibrils, with increases in formal net charge of ∼2 million units. Peracetylated SOD1 amyloid defibrillized at temperatures below unacetylated fibrils, and below the melting temperature of native Cu2,Zn2-SOD1 (e.g., fibril Tm = 84.49°C for acetylated D90A apo-SOD1 fibrils). Targeting the net charge of native or misfolded proteins with small molecules-analogous to how an enzyme's Km or Vmax are medicinally targeted-holds promise as a strategy in the design of therapies for diseases linked to protein self-assembly.

Voltage-Induced Misfolding of Zinc-Replete ALS Mutant Superoxide Dismutase-1

The monomerization of Cu, Zn superoxide dismutase (SOD1) is an early step along pathways of misfolding linked to amyotrophic lateral sclerosis (ALS). Monomerization requires the reversal of two post-translational modifications that are thermodynamically favorable: (i) dissociation of active-site metal ions and (ii) reduction of intramolecular disulfide bonds. This study found, using amide hydrogen/deuterium (H/D) exchange, capillary electrophoresis, and lysine-acetyl protein charge ladders, that ALS-linked A4V SOD1 rapidly monomerizes and partially unfolds in an external electric field (of physiological strength), without loss of metal ions, exposure to disulfide-reducing agents, or Joule heating. Voltage-induced monomerization was not observed for metal-free A4V SOD1, metal-free WT SOD1, or metal-loaded WT SOD1. Computational modeling suggested a mechanism for this counterintuitive effect: subunit macrodipoles of dimeric SOD1 are antiparallel and amplified 2-fold by metal coordination, which increases torque at the dimer interface as subunits rotate to align with the electric field.