MM/GBSA and LIE estimates of host–guest affinities: dependence on charges and solvation model

The affinities of two sets of guest–host systems were estimated using the popular end-point methods MM/GBSA (molecular-mechanics with generalised Born and surface-area solvation) and LIE (linear interaction energy). A set of six primary alcohols that bind to α-cyclodextrin (α-CD) and a set of eight guest molecules to cucurbit[8]uril (CB8) were considered. Three different charge schemes were used to obtain charges for the host and guest molecules, viz., AM1-BCC, RESP, and the recently suggested xAvESP (which average ESP charges over a number of molecular dynamics snapshots). Furthermore, both the generalised Born and Poisson–Boltzmann solvation models were used in the MM/GBSA calculations. The two solvation models perform equally well in predicting relative affinities, and hence there is no point in using the more expensive Poisson–Boltzmann model for these systems. Both the LIE and MM/GBSA estimates are shown to be robust with respect to the charge model, and therefore it is recommended to use the cheapest AM1-BCC charges. Using AM1-BCC charges, the MM/GBSA method gave a MADtr (mean absolute deviation after removal of systematic error) of 17 kJ/mol and a correlation coefficient (r ²) of 0.67 for the CB8 complexes, and a MADtr of 10 kJ/mol and an r ² of 0.96 for the α-CD complexes. The LIE method gave a MADtr of 20 kJ/mol and an r ² of 0.10 for the CB8 complexes, after optimisation of the non-polar scaling parameter. For the α-CD complexes, no optimisation was necessary and the method gave a MADtr of 2 kJ/mol and a r ² of 0.96. These results indicate that both MM/GBSA and LIE are able to estimate host–guest affinities accurately.     

Can MM/GBSA calculations be sped up by system truncation?

We have studied whether calculations of the binding free energy of small ligands to a protein by the MM/GBSA approach (molecular mechanics combined with generalized Born and surface area solvation) can be sped up by including only a restricted number of atoms close to the ligand. If the protein is truncated before the molecular dynamics (MD) simulations, quite large changes are observed for the calculated binding energies, for example, 4 kJ/mol average difference for a radius of 19 Å for the binding of nine phenol derivatives to ferritin. The results are improved if no atoms are fixed in the simulations, with average and maximum errors of 2 and 3 kJ/mol at 19 Å and 3 and 6 kJ/mol at 7 Å. Similar results are obtained for two additional proteins, p38α MAP kinase and factor Xa. On the other hand, if energies are calculated on snapshots that are truncated after the MD simulation, all residues more than 8.5 Å from the ligand can be omitted without changing the energies by more than 1 kJ/mol on average (maximum error 1.4 kJ/mol). At the molecular mechanics level, the gain in computer time for such an approach is small. However, it shows what size of system should be used if the energies instead are calculated with a more demanding method, for example, quantum‐mechanics. © 2017 Wiley Periodicals, Inc.     

Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations

The Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) methods calculate binding free energies for macromolecules by combining molecular mechanics calculations and continuum solvation models. To systematically evaluate the performance of these methods, we report here an extensive study of 59 ligands interacting with six different proteins. First, we explored the effects of the length of the molecular dynamics (MD) simulation, ranging from 400 to 4800 ps, and the solute dielectric constant (1, 2, or 4) on the binding free energies predicted by MM/PBSA. The following three important conclusions could be observed: (1) MD simulation length has an obvious impact on the predictions, and longer MD simulation is not always necessary to achieve better predictions. (2) The predictions are quite sensitive to the solute dielectric constant, and this parameter should be carefully determined according to the characteristics of the protein/ligand binding interface. (3) Conformational entropy often show large fluctuations in MD trajectories, and a large number of snapshots are necessary to achieve stable predictions. Next, we evaluated the accuracy of the binding free energies calculated by three Generalized Born (GB) models. We found that the GB model developed by Onufriev and Case was the most successful model in ranking the binding affinities of the studied inhibitors. Finally, we evaluated the performance of MM/GBSA and MM/PBSA in predicting binding free energies. Our results showed that MM/PBSA performed better in calculating absolute, but not necessarily relative, binding free energies than MM/GBSA. Considering its computational efficiency, MM/GBSA can serve as a powerful tool in drug design, where correct ranking of inhibitors is often emphasized.     

How to obtain statistically converged MM/GBSA results

The molecular mechanics/generalized Born surface area (MM/GBSA) method has been investigated with the aim of achieving a statistical precision of 1 kJ/mol for the results. We studied the binding of seven biotin analogues to avidin, taking advantage of the fact that the protein is a tetramer with four independent binding sites, which should give the same estimated binding affinities. We show that it is not enough to use a single long simulation (10 ns), because the standard error of such a calculation underestimates the difference between the four binding sites. Instead, it is better to run several independent simulations and average the results. With such an approach, we obtain the same results for the four binding sites, and any desired precision can be obtained by running a proper number of simulations. We discuss how the simulations should be performed to optimize the use of computer time. The correlation time between the MM/GBSA energies is ～5 ps and an equilibration time of 100 ps is needed. For MM/GBSA, we recommend a sampling time of 20–200 ps for each separate simulation, depending on the protein. With 200 ps production time, 5–50 separate simulations are required to reach a statistical precision of 1 kJ/mol (800–8000 energy calculations or 1.5–15 ns total simulation time per ligand) for the seven avidin ligands. This is an order of magnitude more than what is normally used, but such a number of simulations is needed to obtain statistically valid results for the MM/GBSA method. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant

We have performed a systematic study of the entropy term in the MM/GBSA (molecular mechanics combined with generalized Born and surface-area solvation) approach to calculate ligand-binding affinities. The entropies are calculated by a normal-mode analysis of harmonic frequencies from minimized snapshots of molecular dynamics simulations. For computational reasons, these calculations have normally been performed on truncated systems. We have studied the binding of eight inhibitors of blood clotting factor Xa, nine ligands of ferritin, and two ligands of HIV-1 protease and show that removing protein residues with distances larger than 8-16 ? to the ligand, including a 4 ? shell of fixed protein residues and water molecules, change the absolute entropies by 1-5 kJ/mol on average. However, the change is systematic, so relative entropies for different ligands change by only 0.7-1.6 kJ/mol on average. Consequently, entropies from truncated systems give relative binding affinities that are identical to those obtained for the whole protein within statistical uncertainty (1-2 kJ/mol). We have also tested to use a distance-dependent dielectric constant in the minimization and frequency calculation (ε = 4r), but it typically gives slightly different entropies and poorer binding affinities. Therefore, we recommend entropies calculated with the smallest truncation radius (8 ?) and ε =1. Such an approach also gives an improved precision for the calculated binding free energies.     

Binding affinities of factor Xa inhibitors estimated by thermodynamic integration and MM/GBSA

We present free energy estimates of nine 3-amidinobenzyl-1H-indole-2-carboxamide inhibitors of factor Xa. Using alchemical thermodynamic integration (TI) calculations, we estimate the difference in binding free energies with high accuracy and precision, except for mutations involving one of the amidinobenzyl rings. Crystal studies show that the inhibitors may bind in two distinct conformations, and using TI, we show that the two conformations give a similar binding affinity. Furthermore, we show that we can reduce the computational demand, while still retaining a high accuracy and precision, by using fewer integration points and shorter protein-ligand simulations. Finally, we have compared the TI results to those obtained with the simpler MM/GBSA method (molecular-mechanics with generalized Born surface-area solvation). MM/GBSA gives better results for the mutations that involve a change of net charge, but if a precision similar to that of the TI method is required, the MM/GBSA method is actually slightly more expensive. Thus, we have shown that TI could be a valuable tool in drug design.     

Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking

In molecular docking, it is challenging to develop a scoring function that is accurate to conduct high-throughput screenings. Most scoring functions implemented in popular docking software packages were developed with many approximations for computational efficiency, which sacrifices the accuracy of prediction. With advanced technology and powerful computational hardware nowadays, it is feasible to use rigorous scoring functions, such as molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) and molecular mechanics/generalized Born surface area (MM/GBSA) in molecular docking studies. Here, we systematically investigated the performance of MM/PBSA and MM/GBSA to identify the correct binding conformations and predict the binding free energies for 98 protein-ligand complexes. Comparison studies showed that MM/GBSA (69.4%) outperformed MM/PBSA (45.5%) and many popular scoring functions to identify the correct binding conformations. Moreover, we found that molecular dynamics simulations are necessary for some systems to identify the correct binding conformations. Based on our results, we proposed the guideline for MM/GBSA to predict the binding conformations. We then tested the performance of MM/GBSA and MM/PBSA to reproduce the binding free energies of the 98 protein-ligand complexes. The best prediction of MM/GBSA model with internal dielectric constant 2.0, produced a Spearman's correlation coefficient of 0.66, which is better than MM/PBSA (0.49) and almost all scoring functions used in molecular docking. In summary, MM/GBSA performs well for both binding pose predictions and binding free-energy estimations and is efficient to re-score the top-hit poses produced by other less-accurate scoring functions.     

Comparison of radii sets,entropy, QM methods,and sampling on MM‐PBSA,MM‐GBSA,and QM/MM‐GBSA ligand binding energies of F. tularensis enoyl‐ACP reductase (FabI)

To validate a method for predicting the binding affinities of FabI inhibitors, three implicit solvent methods, MM‐PBSA, MM‐GBSA, and QM/MM‐GBSA were carefully compared using 16 benzimidazole inhibitors in complex with Francisella tularensis FabI. The data suggests that the prediction results are sensitive to radii sets, GB methods, QM Hamiltonians, sampling protocols, and simulation length, if only one simulation trajectory is used for each ligand. In this case, QM/MM‐GBSA using 6 ns MD simulation trajectories together with GB^neck2, PM3, and the mbondi2 radii set, generate the closest agreement with experimental values (r² = 0.88). However, if the three implicit solvent methods are averaged from six 1 ns MD simulations for each ligand (called “multiple independent sampling”), the prediction results are relatively insensitive to all the tested parameters. Moreover, MM/GBSA together with GB^HCT and mbondi, using 600 frames extracted evenly from six 0.25 ns MD simulations, can also provide accurate prediction to experimental values (r² = 0.84). Therefore, the multiple independent sampling method can be more efficient than a single, long simulation method. Since future scaffold expansions may significantly change the benzimidazole's physiochemical properties (charges, etc.) and possibly binding modes, which may affect the sensitivities of various parameters, the relatively insensitive “multiple independent sampling method” may avoid the need of an entirely new validation study. Moreover, due to large fluctuating entropy values, (QM/)MM‐P(G)BSA were limited to inhibitors’ relative affinity prediction, but not the absolute affinity. The developed protocol will support an ongoing benzimidazole lead optimization program. © 2015 Wiley Periodicals, Inc.     

Protonation thermochemistry of alpha-aminoacids bearing a basic residue

The proton affinity, PA, and protonation entropy, Delta(p)S degree, of glycine (Gly), 1, aspartic acid (Asp), 2, asparagine (Asn), 3, histidine (His), 4, lysine (Lys), 5, glutamic acid (Glu), 6, and glutamine (Gln), 7, have been reinvestigated by the extended kinetic method, using the "isothermal point" method and the orthogonal distance regression, ODR, technique. The proton affinity values of a-aminoacids bearing a basic residue (PA = 926.8; 965.2; 996.0; 993.9; 981.8 and 988.1 kJ.mol(-1) for 2-7, respectively) show significant deviation from the tabulated values. As expected from the effect of a strong intramolecular hydrogen bond in the protonated forms of these peculiar aminoacids, negative protonation entropies are detected (Delta(p)S degree = 36; 43; 37; 29; 95 and 55 J mol(-1) K(-1) for for 27 respectively).     

Simple models for nonpolar solvation: Parameterization and testing

Implicit solvent models are important for many biomolecular simulations. The polarity of aqueous solvent is essential and qualitatively captured by continuum electrostatics methods like Generalized Born (GB). However, GB does not account for the solvent‐induced interactions between exposed hydrophobic sidechains or solute‐solvent dispersion interactions. These “nonpolar” effects are often modeled through surface area (SA) energy terms, which lack realism, create mathematical singularities, and have a many‐body character. We have explored an alternate, Lazaridis–Karplus (LK) gaussian energy density for nonpolar effects and a dispersion (DI) energy term proposed earlier, associated with GB electrostatics. We parameterized several combinations of GB, SA, LK, and DI energy terms, to reproduce 62 small molecule solvation free energies, 387 protein stability changes due to point mutations, and the structures of 8 protein loops. With optimized parameters, the models all gave similar results, with GBLK and GBDILK giving no performance loss compared to GBSA, and mean errors of 1.7 kcal/mol for the stability changes and 2 Å deviations for the loop conformations. The optimized GBLK model gave poor results in MD of the Trpcage mini‐protein, but parameters optimized specifically for MD performed well for Trpcage and three other small proteins. Overall, the LK and DI nonpolar terms are valid alternatives to SA treatments for a range of applications. © 2017 Wiley Periodicals, Inc.     

Molecular dynamics simulations of the mononuclear zinc-beta-lactamase from Bacillus cereus.

Herein, we report molecular dynamics simulations of the mononuclear form of the Bacillus cereuszinc-beta-lactamase. We studied two different configurations which differ in the presence of a zinc-bound hydroxide or a zinc-bound water and in the protonation state of the essential His210 residue. Contacts of the catalytically important residues (Asp90, His210, Cys168, etc.) with the zinc center are characterized by the MD analyses. The nature of the Zn-OH(2) --> His210 proton transfer pathway connecting the two configurations was studied by means of QM calculations on cluster models while the relative stability of the two configurations was estimated from QM/MM calculations in the enzyme. From these results, a theoretical model for the kinetically active form of the B. cereus metalloenzyme is proposed. Some mechanistic implications and the influence of mutating the Cys168 residue are also discussed.     

Binding free energies in the SAMPL6 octa-acid host–guest challenge calculated with MM and QM methods

We have estimated free energies for the binding of eight carboxylate ligands to two variants of the octa-acid deep-cavity host in the SAMPL6 blind-test challenge (with or without endo methyl groups on the four upper-rim benzoate groups, OAM and OAH, respectively). We employed free-energy perturbation (FEP) for relative binding energies at the molecular mechanics (MM) and the combined quantum mechanical (QM) and MM (QM/MM) levels, the latter obtained with the reference-potential approach with QM/MM sampling for the MM → QM/MM FEP. The semiempirical QM method PM6-DH+ was employed for the ligand in the latter calculations. Moreover, binding free energies were also estimated from QM/MM optimised structures, combined with COSMO-RS estimates of the solvation energy and thermostatistical corrections from MM frequencies. They were performed at the PM6-DH+ level of theory with the full host and guest molecule in the QM system (and also four water molecules in the geometry optimisations) for 10–20 snapshots from molecular dynamics simulations of the complex. Finally, the structure with the lowest free energy was recalculated using the dispersion-corrected density-functional theory method TPSS-D3, for both the structure and the energy. The two FEP approaches gave similar results (PM6-DH+/MM slightly better for OAM), which were among the five submissions with the best performance in the challenge and gave the best results without any fit to data from the SAMPL5 challenge, with mean absolute deviations (MAD) of 2.4–5.2 kJ/mol and a correlation coefficient (R²) of 0.77–0.93. This is the first time QM/MM approaches give binding free energies that are competitive to those obtained with MM for the octa-acid host. The QM/MM-optimised structures gave somewhat worse performance (MAD?=?3–8 kJ/mol and R²?=?0.1–0.9), but the results were improved compared to previous studies of this system with similar methods.     

Fast and accurate predictions of binding free energies using MM‐PBSA and MM‐GBSA

In the drug discovery process, accurate methods of computing the affinity of small molecules with a biological target are strongly needed. This is particularly true for molecular docking and virtual screening methods, which use approximated scoring functions and struggle in estimating binding energies in correlation with experimental values. Among the various methods, MM‐PBSA and MM‐GBSA are emerging as useful and effective approaches. Although these methods are typically applied to large collections of equilibrated structures of protein‐ligand complexes sampled during molecular dynamics in water, the possibility to reliably estimate ligand affinity using a single energy‐minimized structure and implicit solvation models has not been explored in sufficient detail. Herein, we thoroughly investigate this hypothesis by comparing different methods for the generation of protein‐ligand complexes and diverse methods for free energy prediction for their ability to correlate with experimental values. The methods were tested on a series of structurally diverse inhibitors of Plasmodium falciparum DHFR with known binding mode and measured affinities. The results showed that correlations between MM‐PBSA or MM‐GBSA binding free energies with experimental affinities were in most cases excellent. Importantly, we found that correlations obtained with the use of a single protein‐ligand minimized structure and with implicit solvation models were similar to those obtained after averaging over multiple MD snapshots with explicit water molecules, with consequent save of computing time without loss of accuracy. When applied to a virtual screening experiment, such an approach proved to discriminate between true binders and decoy molecules and yielded significantly better enrichment curves. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

A QM/MM study on the catalytic mechanism of pyruvate decarboxylase

Pyruvate decarboxylase (PDC) is a typical thiamin diphosphate (ThDP)-dependent enzyme with widespread applications in industry. Though studies regarding the reaction mechanism of PDC have been reported, they are mainly focused on the formation of ThDP ylide and some elementary steps in the catalytic cycle, studies about the whole catalytic cycle of PDC are still not completed. In these previous studies, a major controversy is whether the key active residues (Glu473, Glu50′, Asp27′, His113′, His114′) are protonated or ionized during the reaction. To explore the catalytic mechanism and the role of key residues in the active site, three whole-enzyme models were considered, and the combined QM/MM calculations on the nonoxidative decarboxylation of pyruvate to acetaldehyde catalyzed by PDC were performed. According to our computational results, the fundamental reaction pathways, the complete energy profiles of the whole catalytic cycle, and the specific role of key residues in the common steps were obtained. It is also found that the same residue with different protonation states will lead to different reaction pathways and energy profiles. The mechanism derived from the model in which the residues (Glu473, Glu50′, Asp27′, His113′, His114′) are in their protonated states is most consistent with experimental observations. Therefore, extreme care must be taken when assigning the protonation states in the mechanism study. Because the experimental determination of protonation state is currently difficult, the combined QM/MM method provides an indirect means for determining the active-site protonation state.     

A semiempirical approach to ligand‐binding affinities: Dependence on the Hamiltonian and corrections

We present a combination of semiempirical quantum‐mechanical (SQM) calculations in the conductor‐like screening model with the MM/GBSA (molecular‐mechanics with generalized Born and surface‐area solvation) method for ligand‐binding affinity calculations. We test three SQM Hamiltonians, AM1, RM1, and PM6, as well as hydrogen‐bond corrections and two different dispersion corrections. As test cases, we use the binding of seven biotin analogues to avidin, nine inhibitors to factor Xa, and nine phenol‐derivatives to ferritin. The results vary somewhat for the three test cases, but a dispersion correction is mandatory to reproduce experimental estimates. On average, AM1 with the DH2 hydrogen‐bond and dispersion corrections gives the best results, which are similar to those of standard MM/GBSA calculations for the same systems. The total time consumption is only 1.3–1.6 times larger than for MM/GBSA. © 2012 Wiley Periodicals, Inc.     

The treatment of solvation by a generalized Born model and a self-consistent charge-density functional theory-based tight-binding method

We present a model to calculate the free energies of solvation of small organic compounds as well as large biomolecules. This model is based on a generalized Born (GB) model and a self-consistent charge-density functional theory-based tight-binding (SCC-DFTB) method with the nonelectrostatic contributions to the free energy of solvation modeled in terms of solvent-accessible surface areas (SA). The parametrization of the SCC-DFTB/GBSA model has been based on 60 neutral and six ionic molecules composed of H, C, N, O, and S, and spanning a wide range of chemical groups. Effective atomic radii as parameters have been obtained through Monte Carlo Simulated Annealing optimization in the parameter space to minimize the differences between the calculated and experimental free energies of solvation. The standard error in the free energies of solvation calculated by the final model is 1.11 kcal mol(-1). We also calculated the free energies of solvation for these molecules using a conductor-like screening model (COSMO) in combination with different levels of theory (AM1, SCC-DFTB, and B3LYP/6-31G*) and compared the results with SCC-DFTB/GBSA. To assess the efficiency of our model for large biomolecules, we calculated the free energy of solvation for a HIV protease-inhibitor complex containing 3,204 atoms using the SCC-DFTB/GBSA and the SCC-DFTB/COSMO models, separately. The computed relative free energies of solvation are comparable, while the SCC-DFTB/GBSA model is three to four times more efficient, in terms of computational cost.     

Calculations of the free energy of interaction of the c-Fos-c-Jun coiled coil: effects of the solvation model and the inclusion of polarization effects

The leucine zipper region of activator protein-1 (AP-1) comprises the c-Jun and c-Fos proteins and constitutes a well-known coiled coil protein-protein interaction motif. We have used molecular dynamics (MD) simulations in conjunction with the molecular mechanics/Poisson-Boltzmann generalized-Born surface area [MM/PB(GB)SA] methods to predict the free energy of interaction of these proteins. In particular, the influence of the choice of solvation model, protein force field, and water potential on the stability and dynamic properties of the c-Fos-c-Jun complex were investigated. Use of the AMBER polarizable force field ff02 in combination with the polarizable POL3 water potential was found to result in increased stability of the c-Fos-c-Jun complex. MM/PB(GB)SA calculations revealed that MD simulations using the POL3 water potential give the lowest predicted free energies of interaction compared to other nonpolarizable water potentials. In addition, the calculated absolute free energy of binding was predicted to be closest to the experimental value using the MM/GBSA method with independent MD simulation trajectories using the POL3 water potential and the polarizable ff02 force field, while all other binding affinities were overestimated.     

Modelling zwitterions in solution: 3-fluoro-γ-aminobutyric acid (3F-GABA)

The conformations and relative stabilities of folded and extended 3-fluoro-γ-aminobutyric acid (3F-GABA) conformers were studied using explicit solvation models. Geometry optimisations in the gas phase with one or two explicit water molecules favour folded and neutral structures containing intramolecular NH···O-C hydrogen bonds. With three or five explicit water molecules zwitterionic minima are obtained, with folded structures being preferred over extended conformers. The stability of folded versus extended zwitterionic conformers increases on going from a PCM continuum solvation model to the microsolvated complexes, though extended structures become less disfavoured with the inclusion of more water molecules. Full explicit solvation was studied with a hybrid quantum-mechanical/molecular-mechanical (QM/MM) scheme and molecular dynamics simulations, including more than 6000 TIP3P water molecules. According to free energies obtained from thermodynamic integration at the PM3/MM level and corrected for B3LYP/MM total energies, the fully extended conformer is more stable than folded ones by about -4.5 kJ mol(-1). B3LYP-computed (3)J(F,H) NMR spin-spin coupling constants, averaged over PM3/MM-MD trajectories, agree best with experiment for this fully extended form, in accordance with the original NMR analysis. The seeming discrepancy between static PCM calculations and experiment noted previously is now resolved. That the inexpensive semiempirical PM3 method performs so well for this archetypical zwitterion is encouraging for further QM/MM studies of biomolecular systems.     

QM/MM-PBSA method to estimate free energies for reactions in proteins

We have developed a method to estimate free energies of reactions in proteins, called QM/MM-PBSA. It estimates the internal energy of the reactive site by quantum mechanical (QM) calculations, whereas bonded, electrostatic, and van der Waals interactions with the surrounding protein are calculated at the molecular mechanics (MM) level. The electrostatic part of the solvation energy of the reactant and the product is estimated by solving the Poisson-Boltzmann (PB) equation, and the nonpolar part of the solvation energy is estimated from the change in solvent-accessible surface area (SA). Finally, the change in entropy is estimated from the vibrational frequencies. We test this method for five proton-transfer reactions in the active sites of [Ni,Fe] hydrogenase and copper nitrite reductase. We show that QM/MM-PBSA reproduces the results of a strict QM/MM free-energy perturbation method with a mean absolute deviation (MAD) of 8-10 kJ/mol if snapshots from molecular dynamics simulations are used and 4-14 kJ/mol if a single QM/MM structure is used. This is appreciably better than the original QM/MM results or if the QM energies are supplemented with a point-charge model, a self-consistent reaction field, or a PB model of the protein and the solvent, which give MADs of 22-36 kJ/mol for the same test set.     

On the origin of the stabilization of the zwitterionic resting state of cysteine proteases: a theoretical study

Papain-like cysteine proteases are ubiquitous proteolytic enzymes. The protonated His199/deprotonated Cys29 ion pair (cathepsin B numbering) in the active site is essential for their proper functioning. The presence of this ion pair stands in contrast to the corresponding intrinsic residue p K a values, indicating a strong influence of the enzyme environment. In the present work we show by molecular dynamics simulations on quantum mechanical/molecular mechanical (QM/MM) potentials that the ion pair is stabilized by a complex hydrogen bond network which comprises several amino acids situated in the active site of the enzyme and 2-4 water molecules. QM/MM reaction path computations for the proton transfer from His199 to the thiolate of the Cys29 moiety indicate that the ion pair is about 32-36 kJ mol (-1) more stable than the neutral form if the whole hydrogen bonding network is active. Without any hydrogen bonding network the ion pair is predicted to be significantly less stable than the neutral form. QM/MM charge deletion analysis and QM model calculations are used to quantify the stabilizing effect of the active-site residues and the L1 helix in favor of the zwitterionic form. The active-site water molecules contribute about 30 kJ mol (-1) to the overall stabilization. Disruption of the hydrogen bonding network upon substrate binding is expected to enhance the nucleophilic reactivity of the thiolate.