3D Interaction Homology: Computational Titration of Aspartic Acid, Glutamic Acid and Histidine Can Create pH-Tunable Hydropathic Environment Maps

Aspartic acid, glutamic acid and histidine are ionizable residues occupying various protein environments and perform many different functions in structures. Their roles are tied to their acid/base equilibria, solvent exposure, and backbone conformations. We propose that the number of unique environments for ASP, GLU and HIS is quite limited. We generated maps of these residue's environments using a hydropathic scoring function to record the type and magnitude of interactions for each residue in a 2703-protein structural dataset. These maps are backbone-dependent and suggest the existence of new structural motifs for each residue type. Additionally, we developed an algorithm for tuning these maps to any pH, a potentially useful element for protein design and structure building. Here, we elucidate the complex interplay between secondary structure, relative solvent accessibility, and residue ionization states: the degree of protonation for ionizable residues increases with solvent accessibility, which in turn is notably dependent on backbone structure.

pKAI: a fast and interpretable deep learning approach for accurate electrostatics-driven pKa predictions

The pKa values of ionizable residues influence protein folding, stability and biological function. The pKa in bulk water is known for all residues, however, in a protein environment, it can significantly be affected by confinement and electrostatics. Existing computational methods to estimate pKa shifts rely on theoretical approximations and lengthy computations. Furthermore, the amount of experimentally determined pKa values is still very limited, hindering the development of faster machine learning-based methods. In this work, we use a data set of 6 million pKa shifts — determined by PypKa, a continuum electrostatics method — to train deep learning models that are shown to rival the physics-based predictor. On ~750 experimentally determined data points, our model displays the best accuracy and it is the only one that breaks the 1 pK unit RMSE barrier of this considerably difficult test set. Although trained using a very simplified view of the surroundings of the titratable group (namely, atom types and distances to other titratable groups within a given radius), the models are shown to assign proper electrostatic charges to chemical groups, to keep the known correlation between solvent exposure and pKa shift magnitude, and to grasp the importance of close interactions, including hydrogen bonds. Inference times allow speedups of more than 1000 times faster than physics-based methods, especially for large proteins. By combining speed, accuracy and a reasonable understanding of the theoretical basis for pKa shifts, our models provide a game-changing solution for fast estimations of macroscopic pKa from ensembles of microscopic (pKhalf) values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations.

Quantifying charge state heterogeneity for proteins with multiple ionizable residues

For proteins with multiple ionizable residues, the canonical assumption is that ionization states of residues are fixed by their intrinsic pKa values. However, several studies have shown that protonation / deprotonation of acidic vs. basic sidechains is realizable even when the solution pH is kept fixed at values that are far away from the intrinsic pKa values. Indeed, protein solutions are best described as ensembles of charge microstates, with each member of the ensemble being a distinct charge microstate defined by differences in charge states for ionizable residues. Accordingly, for a given set of solution conditions, the true partition function is sum over all charge microstates and all the Boltzmann weights of all conformations associated with each of the charge microstates. Here, we leverage the advantages afforded by potentiometric titrations to measure global net charge as a function of pH, independent of considerations of conformational preferences. The systems studied are fragments of proteins with repetitive patterns of Lys and Glu. We analyze the potentiometry data using the recently introduced formalism of the q-canonical ensemble. In this ensemble, charge microstates can be grouped into mesostates. Each mesostate is a collection of microstates of the same net charge. We analyze data for global charge vs. pH to extract mesostate populations as a function of pH. Our findings reveal that the heterogeneity of charge states makes significant contributions to measured charge profiles. This has significant implications for the types of species that are present in solution, even for a fixed pH. Measurements of net charge, decoupled from measurements of conformational equilibria, and analyzed to extract the pH-dependent populations of different mesostates, will be significant for accurate understanding of how charge state heterogeneity contributes to conformational, binding, and phase equilibria of proteins, especially those that are intrinsically disordered.

Unusual zwitterionic catalytic site of SARS–CoV-2 main protease revealed by neutron crystallography

The main protease (3CL Mpro) from SARS–CoV-2, the etiological agent of COVID-19, is an essential enzyme for viral replication. 3CL Mpro possesses an unusual catalytic dyad composed of Cys145 and His41 residues. A critical question in the field has been what the protonation states of the ionizable residues in the substrate-binding active-site cavity are; resolving this point would help understand the catalytic details of the enzyme and inform rational drug development against this pernicious virus. Here, we present the room-temperature neutron structure of 3CL Mpro, which allowed direct determination of hydrogen atom positions and, hence, protonation states in the protease. We observe that the catalytic site natively adopts a zwitterionic reactive form in which Cys145 is in the negatively charged thiolate state and His41 is doubly protonated and positively charged, instead of the neutral unreactive state usually envisaged. The neutron structure also identified the protonation states, and thus electrical charges, of all other amino acid residues and revealed intricate hydrogen-bonding networks in the active-site cavity and at the dimer interface. The fine atomic details present in this structure were made possible by the unique scattering properties of the neutron, which is an ideal probe for locating hydrogen positions and experimentally determining protonation states at near-physiological temperature. Our observations provide critical information for structure-assisted and computational drug design, allowing precise tailoring of inhibitors to the enzyme's electrostatic environment.

Protonation states in SARS-CoV-2 main protease mapped by neutron crystallography

The main protease (3CL Mpro) from SARS-CoV-2, the etiological agent of COVID-19, is an essential enzyme for viral replication, possessing an unusual catalytic dyad composed of His41 and Cys145. A long-standing question in the field has been what the protonation states of the ionizable residues in the substrate-binding active site cavity are. Here, we present the room-temperature neutron structure of 3CL Mpro from SARS-CoV-2, which allows direct determination of hydrogen atom positions and, hence, protonation states. The catalytic site natively adopts a zwitterionic reactive state where His41 is doubly protonated and positively charged, and Cys145 is in the negatively charged thiolate state. The neutron structure also identified the protonation states of other amino acid residues, mapping electrical charges and intricate hydrogen bonding networks in the SARS-CoV-2 3CL Mpro active site cavity and dimer interface. This structure highlights the ability of neutron protein crystallography for experimentally determining protonation states at near-physiological temperature – the critical information for structure-assisted and computational drug design.

An acidic residue buried in the dimer interface of isocitrate dehydrogenase 1 (IDH1) helps regulate catalysis and pH sensitivity

Isocitrate dehydrogenase 1 (IDH1) catalyzes the reversible NADP+-dependent conversion of isocitrate to α-ketoglutarate (αKG) to provide critical cytosolic substrates and drive NADPH-dependent reactions like lipid biosynthesis and glutathione regeneration. In biochemical studies, the forward reaction is studied at neutral pH, while the reverse reaction is typically characterized in more acidic buffers. This led us to question whether IDH1 catalysis is pH-regulated, which would have functional implications under conditions that alter cellular pH, like apoptosis, hypoxia, cancer, and neurodegenerative diseases. Here, we show evidence of catalytic regulation of IDH1 by pH, identifying a trend of increasing kcat values for αKG production upon increasing pH in the buffers we tested. To understand the molecular determinants of IDH1 pH sensitivity, we used the pHinder algorithm to identify buried ionizable residues predicted to have shifted pKa values. Such residues can serve as pH sensors, with changes in protonation states leading to conformational changes that regulate catalysis. We identified an acidic residue buried at the IDH1 dimer interface, D273, with a predicted pKa value upshifted into the physiological range. D273 point mutations had decreased catalytic efficiency and, importantly, loss of pH-regulated catalysis. Based on these findings, we conclude that IDH1 activity is regulated, at least in part, by pH. We show this regulation is mediated by at least one buried acidic residue ∼12 Å from the IDH1 active site. By establishing mechanisms of regulation of this well-conserved enzyme, we highlight catalytic features that may be susceptible to pH changes caused by cell stress and disease.

Conserved conformational hierarchy across functionally divergent glycosyltransferases of the GT-B structural superfamily as determined from microsecond molecular dynamics

It has long been understood that some proteins to undergo conformational transitions enroute to the Michaelis Complex to allow chemistry. Examination of crystal structures of glycosyltransferase enzymes in the GT-B structural class reveals that the presence of ligand in the active site is necessary for the protein to crystalize in the closed conformation. Herein we describe microsecond molecular dynamics simulations of two evolutionarily unrelated glycosytransferases, HepI and GtfA. Simulations were performed using these proteins in the open and closed conformations, (respectively,) and we sought to identify the major dynamical modes and communication networks which allow conformational transition between the open and closed structures. We provide the first reported evidence (within the scope of our experimental parameters) that conformational hierarchy/directionality of the interconversion between open and closed conformations is a conserved feature of enzymes of the same structural superfamily. Additionally, residues previously identified to be important for substrate binding in HepI were shown to have strong negative correlations with non-ionizable residues distal to the active site. Mutagenesis of these residues produced mutants with altered enzymatic efficiency exhibiting lower Km values, while the kcat is effectively unchanged. The negatively correlated motions of these residues are important for substrate binding and forming the Michaelis complex, without impacting the activation barrier for catalysis. This suggests that in the bi-domain HepI, the enzyme dynamics did not impact the transition state stabilization or chemistry, but rather earlier steps along the reaction coordinate, leading to the reorganization of the active site electrostatic environment required for catalysis.

An acidic residue buried in the dimer interface of isocitrate dehydrogenase 1 (IDH1) helps regulate catalysis and pH sensitivity

ABSTRACTIsocitrate dehydrogenase 1 (IDH1) catalyzes the reversible NADP+-dependent conversion of isocitrate to α-ketoglutarate (α-KG) to provide critical cytosolic substrates and drive NADPH-dependent reactions like lipid biosynthesis and glutathione regeneration. In biochemical studies, the forward reaction is studied at neutral pH, while the reverse reaction is typically characterized in more acidic buffers. This led us to question whether IDH1 catalysis is pH-regulated, which would have functional implications under conditions that alter cellular pH, like apoptosis, hypoxia, cancer, and neurodegenerative diseases. Here, we show evidence of catalytic regulation of IDH1 by pH, identifying a trend of increasing kcat values for α-KG production upon increasing pH in the buffers we tested. To understand the molecular determinants of IDH1 pH sensitivity, we used the pHinder algorithm to identify buried ionizable residues predicted to have shifted pKa values. Such residues can serve as pH sensors, with changes in protonation states leading to conformational changes that regulate catalysis. We identified an acidic residue buried at the IDH1 dimer interface, D273, with a predicted pKa value upshifted into the physiological range. D273 point mutations had decreased catalytic efficiency and, importantly, loss of pH-regulated catalysis. Based on these findings, we conclude that IDH1 activity is regulated, at least in part, by pH. We show this regulation is mediated by at least one buried acidic residue ∼12 Å from the IDH1 active site. By establishing mechanisms of regulation of this well-conserved enzyme, we highlight catalytic features that may be susceptible to pH changes caused by cell stress and disease.

Electrostatic effects in proteins are governed by pH-redistribution of the conformational ensemble

The imperative for charges to be hydrated is one of the most important organizing principles in biology, responsible for the general architecture of biological macromolecules and for energy storage in the form of electrochemical gradients. Paradoxically, many functional sites in proteins have buried ionizable groups1. These groups are tolerated because they are usually buried in the neutral state2. However, when they become charged they can drive structural transitions to open states in which the charge can be stabilized, mostly through interactions with water3. This coupling between the ionization of a buried group and conformational reorganization is precisely the mechanism used by proteins to perform energy transduction4,5,6. By applying this principle to a family of 25 variants of staphylococcal nuclease with internal Lys residues, it was possible to characterize in detail the range of localized partial unfolding events that even a highly stable protein that unfolds cooperatively can undergo in response to H+-binding. Conformational states that constitute vanishingly small populations of the equilibrium native state ensemble of this protein were identified by correlation of structural and thermodynamic data, providing a map of the conformational landscape of this protein with unprecedented detail. The data demonstrate that the apparent pKa values of buried ionizable residues are not determined by the properties of their microenvironment but by the intrinsic propensity of the protein to populate open states in which internal charged residues can be hydrated. The role of buried residues in functional sites in proteins relies on their ability to tune the conformational ensemble for redistribution in response to small changes in pH. These results provide the physical framework necessary for understanding the role of pH-driven conformational changes in driving biological energy transduction4, the identification of pH-sensing proteins in nature7, and for the engineering of pH-sensitive dynamics and function in de novo designed proteins8.