Ernest L. Mehler

Progress in the prediction of pKa values in proteins

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 article 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. Proteins 2011;

Ligand-dependent conformations and dynamics of the serotonin 5-HT(2A) receptor determine its activation and membrane-driven oligomerization properties.

From computational simulations of a serotonin 2A receptor (5-HT2AR) model complexed with pharmacologically and structurally diverse ligands we identify different conformational states and dynamics adopted by the receptor bound to the full agonist 5-HT, the partial agonist LSD, and the inverse agonist Ketanserin. The results from the unbiased all-atom molecular dynamics (MD) simulations show that the three ligands affect differently the known GPCR activation elements including the toggle switch at W6.48, the changes in the ionic lock between E6.30 and R3.50 of the DRY motif in TM3, and the dynamics of the NPxxY motif in TM7. The computational results uncover a sequence of steps connecting these experimentally-identified elements of GPCR activation. The differences among the properties of the receptor molecule interacting with the ligands correlate with their distinct pharmacological properties. Combining these results with quantitative analysis of membrane deformation obtained with our new method (Mondal et al, Biophysical Journal 2011), we show that distinct conformational rearrangements produced by the three ligands also elicit different responses in the surrounding membrane. The differential reorganization of the receptor environment is reflected in (i)-the involvement of cholesterol in the activation of the 5-HT2AR, and (ii)-different extents and patterns of membrane deformations. These findings are discussed in the context of their likely functional consequences and a predicted mechanism of ligand-specific GPCR oligomerization.

Long Dynamics Simulations of Proteins Using Atomistic Force Fields and a Continuum Representation of Solvent Effects: Calculation of Structural and Dynamic Properties

Long dynamics simulations were carried out on the B1 immunoglobulin‐binding domain of streptococcal protein G (ProtG) and bovine pancreatic trypsin inhibitor (BPTI) using atomistic descriptions of the proteins and a continuum representation of solvent effects. To mimic frictional and random collision effects, Langevin dynamics (LD) were used. The main goal of the calculations was to explore the stability of tens‐of‐nanosecond trajectories as generated by this molecular mechanics approximation and to analyze in detail structural and dynamical properties. Conformational fluctuations, order parameters, cross correlation matrices, residue solvent accessibilities, pKa values of titratable groups, and hydrogen‐bonding (HB) patterns were calculated from all of the trajectories and compared with available experimental data. The simulations comprised over 40 ns per trajectory for ProtG and over 30 ns per trajectory for BPTI. For comparison, explicit water molecular dynamics simulations (EW/MD) of 3 ns and 4 ns, respectively, were also carried out. Two continuum simulations were performed on each protein using the CHARMM program, one with the all‐atom PAR22 representation of the protein force field (here referred to as PAR22/LD simulations) and the other with the modifications introduced by the recently developed CMAP potential (CMAP/LD simulations). The explicit solvent simulations were performed with PAR22 only. Solvent effects are described by a continuum model based on screened Coulomb potentials (SCP) reported earlier, i.e., the SCP‐based implicit solvent model (SCP–ISM). For ProtG, both the PAR22/LD and the CMAP/LD 40‐ns trajectories were stable, yielding Cα root mean square deviations (RMSD) of about 1.0 and 0.8 Å respectively along the entire simulation time, compared to 0.8 Å for the EW/MD simulation. For BPTI, only the CMAP/LD trajectory was stable for the entire 30‐ns simulation, with a Cα RMSD of ≈1.4 Å, while the PAR22/LD trajectory became unstable early in the simulation, reaching a Cα RMSD of about 2.7 Å and remaining at this value until the end of the simulation; the Cα RMSD of the EW/MD simulation was about 1.5 Å. The source of the instabilities of the BPTI trajectories in the PAR22/LD simulations was explored by an analysis of the backbone torsion angles. To further validate the findings from this analysis of BPTI, a 35‐ns SCP–ISM simulation of Ubiquitin (Ubq) was carried out. For this protein, the CMAP/LD simulation was stable for the entire simulation time (Cα RMSD of ≈1.0 Å), while the PAR22/LD trajectory showed a trend similar to that in BPTI, reaching a Cα RMSD of ≈1.5 Å at 7 ns. All the calculated properties were found to be in agreement with the corresponding experimental values, although local deviations were also observed. HB patterns were also well reproduced by all the continuum solvent simulations with the exception of solvent‐exposed side chain–side chain (sc–sc) HB in ProtG, where several of the HB interactions observed in the crystal structure and in the EW/MD simulation were lost. The overall analysis reported in this work suggests that the combination of an atomistic representation of a protein with a CMAP/CHARMM force field and a continuum representation of solvent effects such as the SCP–ISM provides a good description of structural and dynamic properties obtained from long computer simulations. Although the SCP–ISM simulations (CMAP/LD) reported here were shown to be stable and the properties well reproduced, further refinement is needed to attain a level of accuracy suitable for more challenging biological applications, particularly the study of protein–protein interactions. Proteins 2005.

Substrate-dependent proton antiport in neurotransmitter:sodium symporters

SUMMARY Neurotransmitter:sodium symporters (NSS), targets for psychostimulants and therapeutic drugs, play a critical role in neurotransmission. Whereas eukaryotic NSS exhibit Cl−-dependent transport, bacterial NSS feature Cl−-independent substrate transport. Recently we showed in LeuT and Tyt1 that mutation of an acidic side chain near one of the Na+-binding sites renders substrate binding and/or transport Cl− dependent. We reasoned that the negative charge - provided either by Cl− or by the transporter itself - is required for substrate translocation. Here we show that Tyt1 reconstituted in proteoliposomes is strictly dependent on the Na+ gradient and is stimulated by an inside negative membrane potential and by an inversely-oriented H+ gradient. Remarkably, Na+/substrate symport elicited H+ efflux, indicative of Na+/substrate symport-coupled H+ antiport. Mutations that render the transport phenotype Cl−-dependent essentially abolish the pH dependence. We propose unifying features of charge balance by all NSS members with similar mechanistic features but with different molecular solutions.

Ab Initio Computational Modeling of Loops in G-Protein- Coupled Receptors: Lessons from the Crystal Structure of Rhodopsin

With the help of the crystal structure of rhodopsin an ab initio method has been developed to calculate the three‐dimensional structure of the loops that connect the transmembrane helices (TMHs). The goal of this procedure is to calculate the loop structures in other G‐protein coupled receptors (GPCRs) for which only model coordinates of the TMHs are available. To mimic this situation a construct of rhodopsin was used that only includes the experimental coordinates of the TMHs while the rest of the structure, including the terminal domains, has been removed. To calculate the structure of the loops a method was designed based on Monte Carlo (MC) simulations which use a temperature annealing protocol, and a scaled collective variables (SCV) technique with proper structural constraints. Because only part of the protein is used in the calculations the usual approach of modeling loops, which consists of finding a single, lowest energy conformation of the system, is abandoned because such a single structure may not be a representative member of the native ensemble. Instead, the method was designed to generate structural ensembles from which the single lowest free energy ensemble is identified as representative of the native folding of the loop. To find the native ensemble a successive series of SCV‐MC simulations are carried out to allow the loops to undergo structural changes in a controlled manner. To increase the chances of finding the native funnel for the loop, some of the SCV‐MC simulations are carried out at elevated temperatures. The native ensemble can be identified by an MC search starting from any conformation already in the native funnel. The hypothesis is that native structures are trapped in the conformational space because of the high‐energy barriers that surround the native funnel. The existence of such ensembles is demonstrated by generating multiple copies of the loops from their crystal structures in rhodopsin and carrying out an extended SCV‐MC search. For the extracellular loops e1 and e3, and the intracellular loop i1 that were used in this work, the procedure resulted in dense clusters of structures with Cα‐RMSD ∼0.5 Å. To test the predictive power of the method the crystal structure of each loop was replaced by its extended conformations. For e1 and i1 the procedure identifies native clusters with Cα‐RMSD ∼0.5 Å and good structural overlap of the side chains; for e3, two clusters were found with Cα‐RMSD ∼1.1 Å each, but with poor overlap of the side chains. Further searching led to a single cluster with lower Cα‐RMSD but higher energy than the two previous clusters. This discrepancy was found to be due to the missing elements in the constructs available from experiment for use in the calculations. Because this problem will likely appear whenever parts of the structural information are missing, possible solutions are discussed. Proteins 2006.

Ab initio computational modeling of long loops in G-protein coupled receptors

A newly developed approach for predicting the structure of segments that connect known elements of secondary structure in proteins has been applied to some of the longer loops in the G-protein coupled receptors (GPCRs) rhodopsin and the dopamine receptor D2R. The algorithm uses Monte Carlo (MC) simulation in a temperature annealing protocol combined with a scaled collective variables (SCV) technique to search conformation space for loop structures that could belong to the native ensemble. Except for rhodopsin, structural information is only available for the transmembrane helices (TMHs), and therefore the usual approach of finding a single conformation of lowest energy has to be abandoned. Instead the MC search aims to find the ensemble located at the absolute minimum free energy, i.e., the native ensemble. It is assumed that structures in the native ensemble can be found by an MC search starting from any conformation in the native funnel. The hypothesis is that native structures are trapped in this part of conformational space because of the high-energy barriers that surround the native funnel. In this work it is shown that the crystal structure of the second extracellular loop (e2) of rhodopsin is a member of this loop’s native ensemble. In contrast, the crystal structure of the third intracellular loop is quite different in the different crystal structures that have been reported. Our calculations indicate, that of three crystal structures examined, two show features characteristic of native ensembles while the other one does not. Finally the protocol is used to calculate the structure of the e2 loop in D2R. Here, the crystal structure is not known, but it is shown that several side chains that are involved in interaction with a class of substituted benzamides assume conformations that point into the active site. Thus, they are poised to interact with the incoming ligand.

Simulation of molecular crowding effects on an Alzheimer's α-amyloid peptide

Fibril formation by the Alzheimers β-amyloid (Aβ) peptide in brain tissue is integral to the Alzheimers disease pathology. Understanding the conformational properties and the mechanisms triggering aggregation of the Aβ peptides, at an atomic level of detail, is of crucial importance for the design of effective therapeutic agents against this disease. In this work, the conformational transitions and dynamic properties of an amyloidogenic peptide fragment (Aβ10-35) were studied by molecular dynamics simulations in systems modeling infinite dilution and the presence of macromolecular crowding agents (CA). The model system consists of the peptide described with an atomistic force field, the CA represented by inert, quasi-hard spheres and a continuum solvent model. This combined model allowed the simulations to be extended to 100 ns each. Simulations were carried out starting from a completely extended structure, a β-strand structure, and four nuclear magnetic resonance structures in dilute aqueous solution. For all structures, two additional simulations were performed that included the inert CA in the solution and occupied approx 30 and 40% of the volume, respectively. For two of the nuclear magnetic resonance structures, additional simulations were carried out with 35% volume fraction of CA to further examine the diffusive behavior of the peptide. The peptide adopted a collapsed coil conformation in all simulations. The results of the simulations in dilute solution showed reasonable qualitative agreement with experimental and other simulation results, whereas the presence of volume excluding agents resulted in some distinct changes in properties (e.g., an increase in the appearance of transient β-structure or decreases in diffusivity with increasing CA concentration). At the same time, internal motion such as order parameter or atomic root mean square fluctuations showed less systematic responses to volume exclusion.

Calculation of pKa in proteins with the microenvironment modulated-screened coulomb potential†

The MM‐SCP has been applied to predict pKa values of titratable residues in wild type and mutants of staphylococcal nuclease (SNase). The calculations were based on crystal structures made available by the Garcia‐Moreno Laboratory. In the mutants, mostly deeply buried hydrophobic residues were replaced with ionizable residues, and thus their pKa values could be measured and calculated using various methods. The data set used here consisted of a set of WT SNase for which His pKa at several ionic strengths had been measured, a set of mutants for which measured pKa were available and a set of 11 mutants for which the measured pKa were not known at the time of calculation. For this latter set, blind predictions were submitted to the protein pKa cooperative, 2009 workshop at Telluride, where the results of the blind predictions were discussed (the RMSD of the submitted set was 1.10 pH units). The calculations on the structures with known pKa indicated that in addition to weaknesses of the method, structural issues were observed that led to larger errors (>1) in pKa predictions. For example, different crystallographic conditions or steric clashes can lead to differences in the local environment around the titratable residue, which can produce large differences in the calculated pKa. To gain further insight into the reliability of the MM‐SCP, pKa of an extended set of 54 proteins belonging to several structural classes were carried out. Here some initial results from this study are reported to help place the SNase results in the appropriate context. Proteins 2011.

Quantitative expression of protein heterogeneity: Response of amino acid side chains to their local environment

A general method has been developed to characterize the hydrophobicity or hydrophilicity of the microenvironment (MENV), in which a given amino acid side chain is immersed, by calculating a quantitative property descriptor (QPD) based on the relative (to water) hydrophobicity of the MENV. Values of the QPD were calculated for a test set of 733 proteins to analyze the modulating effects on amino acid residue properties by the MENV in which they are imbedded. The QPD values and solvent accessibility were used to derive a partitioning of residues based on the MENV hydrophobicities. From this partitioning, a new hydrophobicity scale was developed, entirely in the context of protein structure, where amino acid residues are immersed in one or more “MENVpockets.” Thus, the partitioning is based on the residues “sampling” a large number of “solvents” (MENVs) that represent a very large range of hydrophobicity values. It was found that the hydrophobicity of around 80% of amino acid side chains and their MENV are complementary to each other, but for about 20%, the MENV and their imbedded residue can be considered as mismatched. Many of these mismatches could be rationalized in terms of the structural stability of the protein and/or the involvement of the imbedded residue in function. The analysis also indicated a remarkable conservation of local environments around highly conserved active site residues that have similar functions across protein families, but where members have relatively low sequence homology. Thus, quantitative evaluation of this QPD is suggested, here, as a tool for structure–function prediction, analysis, and parameter development for the calculation of properties in proteins. Proteins 2008.