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Detailed Atomistic Analysis of the HIV-1 Protease Interface Sergio Filipe Sousa, Bruno Tamames, Pedro Alexandrino Fernandes, and Maria Jo~ao Ramos* REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ci^encias, Universidade do Porto, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal
bS Supporting Information ABSTRACT: HIV-1 protease is a very attractive target for the development of new anti-HIV drugs and has been extensively studied over the past decades. In this study, we present a detailed atomic level characterization of the dimer interface in the enzyme HIV-1 protease through computational alanine scanning mutagenesis and molecular dynamics simulations. In addition to a full mapping of the amino acid residues present at the subunit interface, in terms of the corresponding energetic contribution for dimer formation and of their classification as hot spots, warm spots, and null spots, we trace a dynamic analysis of the subunit interacting and solvent accessible surface areas and of the most important hydrogen bonds between subunits. The results presented illustrate the high energetic importance for dimer formation of a small set of five amino acid residue pairs at the subunit interface—Leu5, Ile50, Arg87, Leu97, and Phe99—and provide important clues on the most important structural and energetic determinants for dimer formation. In addition, the results presented suggest several key targets at the subunit interface for the development of new molecules that aim to inhibit HIV-1 protease (PR) activity through blocking the formation of the fully active PR homodimeric form, providing important clues for drug design.
’ INTRODUCTION The Acquired Immune Deficiency Syndrome (AIDS) is a disease of the human immune system caused by the Human Immunodeficiency Virus type-1 virus (HIV-1) that currently infects something like 33 million people worldwide, with 25 million fatal cases so far.1 The immense magnitude of these numbers, together with their terrible socioeconomic and demographic implications, has led to unprecedental political attention and to a massive financial response, making this virus one of the most important targets for the development of new drugs. The several enzymes and structural proteins that constitute the HIV-1 virus are synthesized in the form of two large polyproteins: gag and gag-pol. These two proteins are inactive and need to be cleaved into the individual proteins and enzymes for proper biological activity. This essential process of cleavage is performed exclusively by the enzyme HIV-1 protease (PR), in what may be regarded as one of the most critical parts of the HIV-1 virus life cycle. For this reason, the enzyme HIV-1 PR has become a very attractive target for the development of new anti-HIV drugs.210 Structurally, HIV-1 PR is composed by two identical monomers,11,12 each with 99 amino acid residues, interlocked into one another to form a dimer.13,14 PR acts on gag and gag-pol proteins by breaking these large inactive proteins through very precise hydrolysis reactions at specific sequence points. In particular, this enzyme recognizes sequences of eight amino acid residues in the two large proteins discussed above and for each of these sequences breaks the peptidic bond between the amino acid residues 4 and 5.15 r 2011 American Chemical Society
A total of nine different sequences of eight amino acid residues have shown to be recognized by HIV PR, with some of these sequences differing in all the eight amino acid residues that characterize them.15,16 In particular, in such sequences this enzyme has been shown to catalyze the hydrolysis of the following peptidic bonds: Tyr-Pro, Leu-Ala, Met-Met, PheLeu, Phe-Pro, Phe-Tyr, and Leu-Pro.15 Despite this interesting diversity, a single amino acid change in one of the recognized sequences is enough to transform a substrate of PR into a nonsubstrate. These subtle requirements for PR recognition and the relatively large diversity of recognized sequences for such a small enzyme make the PR active site a particularly challenging target for the development of new drugs.6,17 While several PR inhibitors have reached the market or are in advanced stages of clinical testing,210 resistance to these drugs as a result of an increased prevalence of new strains containing mutations at the active site is an additional limitation to the success of such inhibitors.4,18 New strategies for the inhibition of this important enzyme would therefore be of great relevance and are currently under active investigation.1923 Early experimental denaturation studies have suggested that folding and HIV-1 dimerization occur simultaneously,2426 proposing also that the folded dimer exists in equilibrium with the unfolded monomers and that individual folded monomers do Received: January 4, 2011 Revised: April 6, 2011 Published: May 05, 2011 7045
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The Journal of Physical Chemistry B not exist at appreciable concentration, as they are intrinsically unstable. However, other more recent studies have shown that monomeric HIV-1 PR appears to be relatively stable27 and that the isolated monomer has secondary and tertiary structure that is very similar to that of the bound monomer. In addition, several studies of HIV-1 protease variants have shown that mutations at or near the interface can shift the monomerdimer equilibrium in favor of the folded but inactive monomeric form.24,25,28,29 Also, a folded monomeric HIV-1 PR for several mutants has been reported in the literature.28,30,31 Together these results suggest an alternative route for the development of anti-HIV-1 drugs: inhibiting the formation of the active PR dimer, through the development of dimerization inhibitors.19,20,3237 Alanine scanning mutagenesis (ASM) is a very powerful tool for the study of proteinprotein or subunit interfaces,38 which can be applied to identify the regions that are responsible for most of the binding energy between the two components that make up the interface. The residues at these regions are called hot spots and have been defined as those residues that upon alanine mutation result in a binding free energy difference of 4.0 kcal/ mol or greater.39,40 Other relevant residues for the binding process between subunits are the warm spots, which upon alanine mutation result in a binding free energy difference between 2.0 and 4.0 kcal/mol. Amino acid residues that upon alanine mutation result in a binding free energy difference below this 2.0 kcal/mol threshold are called null spots.39,40 Computational alanine scanning mutagenesis was developed as a faster alternative to the much slower experimental ASM, allowing large interfaces to be efficiently analyzed for the presence of hot spots, warm spots, and null spots. Several different schemes, based on the application of molecular dynamics simulations, have been described in the literature, at different levels of sophistication and computational cost and with different levels of success.4146 In particular, a variation of the standard computational ASM method46 has been developed in our group,47 based on the use of the well-established MMPBSA (molecular mechanics/PoissonBoltzmann surface area) approach,48,49 optimized for the use of a continuum solvation description considering different internal dielectric constant values for different types of amino acid residues, and has been shown to yield particularly high accurate results in a variety of different biological systems,5052 with an overall success of 82% in the identification of hot spots.47 In this study, we assess a relatively new and innovative PR inhibition paradigm, by targeting the subunit interface of this critical enzyme, to identify highly important points for PR dimerization inhibition. For this, we have conducted a systematic application of the computational ASM method to a total of 70 residues present at the interface of the two subunits of this enzyme, identifying the ones that have a higher contribution to the formation of the dimer. While some previous pharmacological studies have focused on the inhibition of PR dimerization,19,20,3237 showing it as a viable and very promising strategy for the development of new anti-HIV drugs, the existence of specific spots for dimerization inhibition at the subunit interface remains very poorly understood. Hence, the knowledge arising from the computational ASM analysis of the HIV PR interface described in this study provides important clues about the association determinants for the two PR subunits and could serve as a basis for the development of a new class of HIV-1 PR inhibitors designed to block the association of the two subunits to yield the fully active PR homodimeric form.
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’ METHODS Model Setup and Molecular Dynamics Simulations. Two models were considered for this study: a substrate-free PR enzyme and a substrate-bound PR enzyme (substrate Lys-AlaArg-Val-Leu-Ala-Glu-Ala-Met-Ser). Even though some structures available in the RCSB Protein Data Bank53 refer to the substrate-free PR form (2PC0, 2HB4, 2HB2, 1HHP, 3HVP, etc.) they typically contain only one of the two PR subunits and have only a moderate resolution. To obtain a representative highresolution structure of the PR in the absence of substrate that contains full PR interface in atomic detail, a typical choice has been to start from a high-resolution structure containing a small inhibitor that does not disrupt the interface symmetry. This approach typically guarantees that the full PR interface maintains the characteristics expected for the substrate-free form, in terms of range of interaction and symmetric nature. Following this idea, we used as a starting structure the 1T3R structure (resolution 1.20 Å),54 after removing the small inhibitor present. For the substrate-bound PR enzyme, the model was prepared from the 1F7A crystallographic structure (resolution 2.0 Å).55 This structure contains a natural PR substrate (KARVLAEAM) and a D25N mutation to prevent catalytic activity. In preparing our model, we have modeled an O group at the position of the Asn NH2 group to have the wild-type Asp residue at position 25. Several mechanistic studies have demonstrated that the Asp25 amino acid residues of both subunits have different protonation states and act together with a conserved water molecule to hydrolase the peptidic bond.5659 In particular, the protonated Asp25 residue establishes an important hydrogen bond with the peptidic carbonyl oxygen of the substrate in the substrate-bound PR enzyme,60 while in the free enzyme, at optimal pH both Asp25 residues interact with each other through a symmetric low barrier hydrogen bond (LBHB), that shifts from Asp25 to Asp25 residues.61,62 According to this information, the Asp25 residue with the proper orientation for such an interaction in the substrate-bound enzyme was protonated (subunit R in structure 1F7A), while in the free enzyme an approximation had to be made due to the intrinsic limitations of classic molecular dynamics, assigning the proton to one of the two Asp25 residues (subunit β in structure 1T3R). In both cases, the choice on the oxygen atom to be protonated was carefully done to maximize the resulting hydrogen bonds. Hence, in the PR substrate-free form, it was the “lower” oxygen atom that was protonated, maximizing the H-bond interaction with the “lower” oxygen atom of the Asp25 residue in the other subunit. In the substratebound form, it was the upper oxygen that was protonated, maximizing the hydrogen-bond interaction with the peptide substrate carbonyl group. Even though the optimal pH for HIV-1 PR activity is known to be 4.7,56 conventional protonation states for all the amino acids (with the exception of Asp25) at pH 7 were considered to reproduce in more detail the more typical human physiological conditions. All the hydrogen atoms were added using the LEAP program in the AMBER 9.0 Software package.63 Cl counterions were used to neutralize the excess of positive charges in both models, and a minimum distance of 12 Å of water (TIP3P model)64 was considered to solvate the systems, allowing the application of periodic boundary conditions. The Cornell et al. force field was used in all calculations.65 Both models were subjected to a four-stage refinement protocol using the SANDER module of AMBER 9.0, in which the constraints on 7046
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the R-subunit, β-subunit, and the dimer, respectively. The binding free energy difference between an alanine mutant and the wild-type enzyme (ΔΔGBinding) in water is defined as ΔΔGBinding ¼ ΔGBindingmutant ΔGBindingwildtype The binding free energy of the two subunits in water can be written as the difference between the free energy of the enzyme and that of the corresponding subunits. ΔGBinding ¼ Gcomplex ðGR-subunit þ Gβ-subunit Þ
the enzyme were gradually removed. In the first stage (10 000 steps), 50 kcal mol1 Å2 harmonic forces were used to restrain the positions of all atoms in the systems except the ones from the water molecules. In the second stage (10 000 steps), these constraints were applied only to the heavy atoms and in the third stage (30 000 steps) were limited to the CA and N atomtype atoms (backbone alfa carbons and nitrogens). This process ended in a full energy minimization (fourth stage, maximum 80 000 steps) until the rms gradient was smaller than 0.02 kcal/ mol. Following an initial warming period during 40 ps (in an NVT ensemble), 10 ns of simulation (NPT) was performed for each model, yielding a total simulation time of 20 ns. Only the last 6 ns of each simulation was considered for the analysis presented. In the simulations performed, bond lengths involving hydrogen atoms were constrained using the SHAKE algorithm,66 and the equations of motion were integrated with a 2 fs time step. A nonbond-interaction cutoff radius of 10 Å was adopted for use with a Particle-Mesh Ewald scheme,67 whereas the Langevin thermostat6870 was applied to maintain the temperature of the system at 310 K. Computational Alanine Scanning Mutagenesis. Our alanine scanning mutagenesis protocol uses a single MD trajectory of the wild-type system to calculate the binding free energies. From this trajectory, each interfacial residue of the HIV-1 PR subunits is mutated to alanine, allowing the differences in the binding free energy (ΔΔGbinding) for each mutation in relation to the wild-type to be calculated with the MM-PBSA approach, optimized by considering different internal dielectric constant values for different types of amino acid residues. The use of a single trajectory in this process to calculate ΔΔGbinding has been shown to give a better agreement with the experimental data than the use of multiple trajectories.47 In fact, for the use of a single trajectory, error cancelation has been assumed to overcome the reduced sampling of the conformational space.71,72 Following these general principles, the MM-PBSA script implemented in AMBER was used to perform a postprocessing treatment of the PR R/β dimer by using the structure of the full enzyme and calculating its respective energy and those of the interacting monomers (R and β). To generate the structure of the alanine mutants, a simple truncation of the mutated side chain was made, replacing Cγ with a hydrogen atom and setting the alanine CβH bond direction to that of the former CγCβ. For the binding free energy calculations, 300 snapshots were extracted from the last 6000 ps of the MD run. The binding free energy in solution [ΔGBinding(aq)] was calculated using the thermodynamic cycle shown in Scheme 1 in which ΔGBinding(g) is the interaction free energy between the R-subunit and β-subunit in the gas phase and ΔGR-subunitSolv, ΔGβ-subunitSolv, and ΔGEnzymeSolv are the solvation free energy of
The free energy of the dimer and respective monomers in water or in solvent can be calculated48 by summing the internal energy (bond, angle, and dihedral), the electrostatic and the van der Waals interactions, the free energy of polar solvation, the free energy of nonpolar solvation, and the entropic contribution to yield the free energy of each component Gcomponent ¼ Einternal þ Eelectrostatic þ EvdW þ GPolar Solv þ GNonpolarSolv TS In this equation, the first three terms were calculated using the force field considered in the MD simulation, but with no cutoff. The electrostatic solvation free energy was calculated by solving the PoissonBoltzmann equation with the software Delphi version 4.73,74 According to this method, the protein is modeled as a dielectric continuum of low polarizability embedded in a dielectric medium of high polarizability.74 The solvent is assumed to be a homogeneous medium characterized by a single dielectric constant with a value that is usually near 80, which is taken to be equal to the bulk value for pure solvent. Separated by an abrupt interface, the solvent is in contact with the solute that is represented as a dielectric body whose shape is defined by atomic coordinates and radii.74 For the energy calculations, three internal dielectric constant values, exclusively characteristic of the mutated amino acids, were used: 2 for the nonpolar amino acids, 3 for the polar residues, and 4 for the charged amino acids.47 The different internal dielectric constants considered have been shown to mimic the different degree of relaxation of the subunit interface when different types of amino acids are mutated for alanine.47 The nonpolar contribution to solvation free energy due to van der Waals interactions between the solute and the solvent and cavity formation was modeled as a term that is dependent on the solvent-accessible surface area of the molecule, estimated through the use of the following empirical relation ΔGnonpolar ¼ RA þ β where A is the solvent-accessible surface area that was estimated using the molsurf program (based on the idea primarily developed by Connolly)75 and R and β are empirical constants for which the values of 0.00542 kcal Å2 mol2 and 0.92 kcal mol1 were used. Finally, the entropy term, obtained from the sum of the sum of translational, rotational, and vibrational components, was not calculated because it was assumed, based on previous work, to have a neglectable contribution to ΔΔGbinding.71 The ASM protocol used here,47 based in the well-known MMPBSA approach,46,48,49 has been used with success in the study of several biological systems, including the IgG1 streptococcal protein G (C2 fragment) complex,50 the ZipA:FtsZ complex, the complex formed between the hen egg white lysozyme (HEL) 7047
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Figure 1. RMSd analysis of the backbone CR atoms in the MD simulations of the free enzyme and of the substrate-bound enzyme. Particular attention is dedicated to the behavior of the interface amino acid residues.
and the antibody HyHEL-10,51 and the MDM2P53 complex,52 and in previous benchmarking studies against experimental data has been shown to have an overall success of 82% in identifying hot spots and 80% in identifying warm spots and to yield a mean unsigned error of around 0.5 kcal/mol. Mutant Selection. Residues to mutate were selected from a detailed analysis of the HIV PR interface based on the 1F7A crystallographic structure (resolution 2.0 Å).55 A total of 70 interface residues were identified (35 in each subunit). Alanine, glycine, and proline interface residues were not considered in this study. Alanine scanning mutagenesis of alanine residues is a redundant process. Glycine has a smaller side chain than alanine and hence cannot be effectively mutated to alanine in a consistent fashion within the protocol considered for the other amino acids. In fact, in glycine the absence of a side chain confers to the backbone an additional conformational freedom that typically results in relevant structural rearrangements of the backbone. Hence, the experimental measure of the ΔΔGbinding for such mutation does not correspond to the subunit interaction difference between glycine and alanine but rather to a more global interaction difference that includes also the backbone rearrangement effects. This is an obvious limitation of the experimental ASM method that could be eventually surpassed computationally, namely with the use of a single trajectory. The difficulty here lies in the lack of experimental reference values for glycinealanine mutations, essential to fully calibrate the computational ASM method employed to treat all the other amino acid residues in this study. A similar problem can be highlighted for proline. In fact, the proline ring restricts the geometry of the backbone chain in the proteins where it is present, sometimes resulting in a very different backbone conformation than the one adopted when an alanine or any other residue is present. Experimental ASM of proline is therefore particularly troublesome, as it can produce abnormal changes to the binding free energies as a result of the backbone conformational differences, masking the results.47,76 For these reasons, proline residues were also not evaluated. ΔΔGbinding values were calculated for all the other interface amino acid residues for both the free enzyme and the substratebound enzyme. SASA Analysis. Solvent accessible surface area values were calculated for all interface amino acid residues (backbone þ side chain) using the program Visual Molecular Dynamics (VMD)77
and considering the standard probe radius for water of 1.4 Å. From the MD simulations performed and for each residue, SASA values were calculated for a total of 3000 MD snapshots from the last 6 ns of simulation, considering: (1) the full dimer— SASAdimer; (2) only the residues from the same subunit, neglecting the shielding effect of the other subunit—SASAmonomer— and yielding a value for the potential SASA in the monomer. Final values were expressed as a percentage of the potential SASA for the free residue.
’ RESULTS AND DISCUSSION 1. General Analysis of the MD Simulation. Prior to the ASM study, a general analysis of the MD trajectories generated was performed to evaluate the convergence and stability of the most relevant properties within the 10 ns simulation times considered. Properties evaluated included the potential and kinetic energies, the temperature, the pressure, the root-mean-square deviation, etc. From these general properties, the root-mean-square deviation (RMSd) is normally the most difficult to converge and is usually taken as a reference to assess the stability of an MD simulation. Figure 1 illustrates the RMSd values for the backbone CR atoms in the MD simulations of the free enzyme and of the substrate-bound enzyme. In addition, this figure also shows the RMSd values for the subset of backbone CR atoms of the 70 interface amino acid residues that will be the subject of more detail in this study. The results show that both models are well equilibrated after the initial 4 ns of simulation and in particular the interface amino acid residues. In agreement with this observation, the remaining 6 ns of simulation was taken into consideration for the ASM calculations and subsequent analysis. 2. Analysis of the Interface in the Free PR Enzyme. The PR interface is remarkably extensive for such a small enzyme. In fact, upon dimerization, each monomer buries more than 50% of its total surface area, with 64% and 62% of its nonpolar and polar surfaces becoming shielded from the solvent.26 The PR subunit interface is defined to a great extent by a group of eight interdigiting N- and C-terminal residues (residues 14 and 9699) from each of the two monomers, arranged in a set of four interlocking antiparallel beta strands.11,19 This set of 16 residues has also been shown to represent 45% of the buried surface area during dimer formation.78 In this study, we are interested in understanding how these effects can be translated in terms of an energetic contribution to 7048
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Table 1. Differences in the Binding Free Energies (ΔΔGBinding) for the Wild-Type PR and Alanine Mutant Variants Calculated for PR in the Absence of Substratesa
a Values for the interface residues in both subunits. Average Solvent Accessible Surface Area (SASA) values for the residues in each amino acid position in both subunits. (Red, Hot spot; Yellow, Warm spot; White, Null spots).
the association of the dimer. Table 1 summarizes the computational ASM results for all the interface amino acid residues in the free HIV PR. The values of ΔΔGbinding calculated from the ASM studies allow us to identify the amino acid residues that contribute the most to the binding of the two monomers in the free enzyme. As these amino acid residues are determinant to the binding of the two monomers, the development of drugs that are able to prevent
or disrupt their interaction may prevent dimer formation, hence inhibiting PR enzymatic activity. Such amino acid residues are called hot spots. For this computational study, we considered, as a hot spot, the amino acid residue with a ΔΔGbinding value higher than 4 kcal/mol. The amino acids with a ΔΔGbinding value between 2 and 4 kcal/mol are less important but still vital to the binding process and are designated as warm spots. 7049
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Figure 2. Schematic representation of the most important interactions between residues from the different subunits, with indication of the hot spots and warm spots identified.
From the 35 pairs of residues evaluated at the interface of the subunit of PR, only 9 (i.e., ca. 25%) were shown to have an energetic contribution higher than 2 kcal/mol to the formation of the dimer. In this regard, the most important residues are Arg87, Phe99, Ile50, Leu97, and Leu5. All these 5 pairs of residues have ΔΔGbinding of more than 4 kcal/mol and hence are considered hot spots for dimer formation. The remaining 4 pairs of residues, with ΔΔG binding values between 2 and 4 kcal/mol, are Trp6, Asp29, Thr26, and Thr96 and are warm spots. The remaining 26 pairs of residues at the interface are null spots or cold spots, designations that refer, respectively, to amino acid residues whose side chain has little effect on the stabilization of the dimer (between 0 and 2 kcal/mol) or whose contribution is detrimental to dimer formation (negative contribution). A systematic study79 analyzing a total of 2325 alanine mutants for which the change in the free energy of binding upon mutation to alanine had been experimentally measured has identified
tryptophan, tyrosine, arginine, isoleucine, and aspartate as the most common residues present among those contributing more than 2 kcal/mol for the ΔΔGbinding. These general trends are in agreement with the identification of Arg87, Ile50, Trp6, and Asp29 as hot spots and warm spots in HIV PR. Leu5 and Leu97, however, are rather uncommon among hot spots and warm spots, with the above-mentioned study having identified only two leucine residues contributing more than 2 kcal/mol to ΔΔG binding among a total of 242 leucinealanine mutants considered. According to the same study,79 phenylalanines and threonines are also rather uncommon as hot spots or warm spots, with only five Phe and two Thr identified in a total of 166 phenyalaninealanine mutants and 131 threoninealanine mutants. The unusually high energetic contribution calculated in this study for Leu5, Thr26, Thr96, Leu97, and Phe99 could be a distinctive characteristic of HIV-1 protease and will be the subject of particular detail over the subsequent sections. 7050
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Figure 3. Schematic representation of the two individual monomers that constitute the active HIV PR dimer, illustrating the relative position of all the hot spots and warm spots identified.
The values presented in Table 1 also allow one to have an energetic view of the symmetric nature of the interface formed, by enabling a direct comparison of the ΔΔGbinding calculated upon alanine mutation for the same residue in each subunit. For most interface amino acid residues in the free PR study, there is an energetic difference of less than 0.5 kcal/mol between the energetic contribution of a given amino acid residue in subunit R and that of the same residue at subunit β. In addition, for 33 of the 35 pairs of residues there is a perfect qualitative agreement between the classification of the residue as hot spot, warm spot, or null spot in both subunits. The only exceptions are Asp25 (for which different protonation states were assigned in the MD simulations performed as an approximation, even though experimentally a low barrier hydrogen bond—LBHB—shifting from one Asp25 to the other has been shown to take place)62,80 and the amino acid residue Gln2. In line with the LBHD theory,62,80 for Asp25 the real value should be an average of the two extreme values calculated for the protonated (0.94 kcal/mol) and deprotonated alternatives (4.44 kcal/mol). The importance of Asp25 for dimer formation has been previously discussed in the literature.28,30,81 In fact, mutating Asp25 to Asn has been shown to destabilize dimer formation, however, to a lesser extent than the T26A mutation.28 Figure 2 illustrates the position of the hot spots and warm spots identified in this study in the structure of the HIV PR dimer, while Figure 3 gives a more detailed view of the two individual monomers.
The energetic agreement between the results calculated for amino acid position in both subunits is in line with previous structural studies, which highlighted the symmetric nature of the PR enzyme in the absence of substrate,55 a characteristic feature that is lost upon substrate binding. In fact, the average difference between the calculated ΔΔGbinding for all 35 pairs of residues of the two subunits is of only 0.31 kcal/mol in the free enzyme (0.17 kcal/mol discarding the outliers Gln2, Pro6, and Asp25). Due to this symmetric nature of the PR interface, we considered as criteria for hot/warm/null spot for each amino acid position in the absence of substrate the average ΔΔG binding (kcal/mol) obtained for each of the two subunits. A computational structure-based thermodynamic analysis of the PR dimer had already suggested Leu5, Thr96, Leu97, and Phe99 as very important residues for dimerization.26 Deletion of the C-terminal β-strand of HIV-1 protease,30 which includes the hot spots Leu97 and Phe99 and the warm spot Thr96, has been previously shown to produce a folded monomer in solution, instead of the normally more stable dimer. In addition, the stability of the PR dimer in a I50V mutant has been previously evaluated experimentally against that of the wild-type PR by assessing the PR activity with increasing concentrations of denaturing urea.82 In particular, the results showed that a mutation at this position led to a 60% decrease in the dimer stability, a value which is in agreement with the high energetic contribution of this residue envisioned from the present 7051
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Table 2. Energetic Contribution of All the Individual Components to the Differences in the Binding Free Energies (ΔΔGBinding) between the Wild-Type PR and Alanine Mutant Variants for the Hot Spots, Warm Spots, and Null Spots Identified in the Absence of Substratea hot spots
warm spots
amino acid pairs identified
5
4
average ΔΔGBinding (kcal/mol)
5.98
2.66
0.80
1.75
0.78
6.84
1.10
1.49
average ΔΔEelectrostatic (kcal/mol) average ΔΔEvdW (kcal/mol)
26
total 35
7.19
1.75
0.87
1.87
average ΔΔGPolarSolv (kcal/mol) average ΔΔGNonPolarSolv(kcal/mol)
0.75 0.32
6.07 0.14
1.18 0.02
1.68 0.08
average hydrophilic contribution (kcal/mol)
1.53
0.77
0.09
0.19
7.51
1.89
0.89
1.95
54.0%
45.3%
25.1%
31.5%
9.2%
20.3%
17.2%
16.4%
44.7%
25.0%
7.9%
average hydrophobic contribution (kcal/mol) average SASA in monomer (%) average SASA in dimer (%) average SASA lost upon dimerization (%) average SASA lost upon dimerization (Å2) a
null spots
133.9
65.5
21.5
15.1% 42.6
Average Solvent Accessible Surface Area (SASA) values.
computational study and with its role as a hot spot. Mutations at this position have also been addressed in several other studies analyzing inhibitor binding in the PR dimer.83,84 Interestingly, this same study82 examined the importance of mutations at position 54 (I54M and I54V), showing that mutations at this residue do not affect dimer stability, a result which is also in agreement with the suggested role of this residue as a null spot in the present analysis. Several other studies28,30,31 have also demonstrated that the introduction of mutations at the PR hot spot Arg87 (R87K), and into the warm spots Thr26 and Asp29 (T26A, D29N, and D29A), disrupts the dimer interface contacts and destabilizes the protease dimer, causing the inhibition of protease dimerization. Several studies have suggested Darunavir, a second generation, recently accepted PR inhibitor, to block dimerization.20 The binding affinity of this inhibitor for the wild-type enzyme is about 2 orders of magnitude stronger than that of first generation PR inhibitors,85 with structural studies showing that Darunavir can bind to two distinct sites: (1) the active site cavity, interacting with the active-site amino acids Asp29 and Asp30 and (2) the surface of one of the flexible flaps in the PR dimer, interacting with residues Glu35, Trp42, Pro44-Met46, Lys55-Arg57, and Val77-Pro79.84,86,87 Darunavir and two other related experimental PR inhibitors have been shown to block PR dimerization in concentrations as low as 0.01 μM and to block HIV-PR replication in vitro with IC50 values of 0.00020.48 μM.20 However, once the PR monomers dimerize to yield a mature PR dimer, the dimer is not dissociated by these dimerization inhibitors, suggesting that these agents block dimerization at the nascent stage of protease maturation.20 Further studies analyzing the interaction of these inhibitors with the free monomer are required to fully understand their mechanism of interaction. 3. Energetic Contribution to Hot Spots. Table 2 presents the energetic contribution of all the individual components to the differences in the binding free energies (ΔΔGBinding) between the wild-type PR and alanine mutant variants for hot spots, warm spots, and null spots identified. The components presented include the electrostatic energy term (ΔΔEelectrostatic), the van der Waals energy term (ΔΔEvdW), and the polar (ΔΔGPolarSolv) and nonpolar (ΔΔGNonPolarSolv) contributions to the solvation free energy. The last two columns—hydrophilic and hydrophobic
contributions—represent, respectively, the sum of the electrostatic energy term and of polar contribution to the solvation free energy (hydrophilic contribution) and of the van der Waals and nonpolar contribution to the solvation free energy (hydrophobic contribution). Table 2 shows that the five hot spots identified have an average ΔΔGBinding of 5.98 kcal/mol, while the five warm spots have an average value of 2.66 kcal/mol and the remaining interface amino acids residues (null spots) of only 0.80 kcal/mol. Analyzing the several contributions to the ΔΔGBinding, it can be observed that the hot spots identified have a lower ΔΔEelectrostatic and a dramatically higher ΔΔEvdW in comparison with the other amino acid residues studied. In terms of the solvation free energy terms, the hot spots identified have a higher (less negative) contribution to the ΔΔGPolarSolv and an also higher (more positive) contribution to the ΔΔGNonPolarSolv. Combining these energy terms into hydrophilic and hydrophobic contributions to the binding free energy (Table 2) highlights the importance of the hydrophobic interactions to the binding free energies in the hot spots identified (average contribution 7.51 kcal/mol for hot spots, against 1.89 in the warm spots and 0.89 in the null spots). The contribution of the hydrophilic energy term is much smaller, and in this case the differences in the average values calculated for hot spots, warm spots, and null spots are also less dramatic. 4. SASA Analysis. Bogan and Thorn79 in their seminal work “Anatomy of Hot-Spots in Protein Interfaces” have found the existence of very little correlation between the buried surface area and the free energy of binding, and have concluded that the occlusion from solvent is a necessary although not sufficient requirement for the occurrence of hot spots. In this section, we analyze this issue for the case of HIV PR but from a different point of view. Table 1 presents also the solvent accessible surface area (SASA) values calculated for each residue, considering the full dimer (SASAdimer) and considering only the residues in the same subunit (SASAmonomer). Given the large difference in size that characterizes the different amino acid residues present at the interface, the values are presented as a percentage of the potential SASA for the free residue. In the convention used in this study, a high SASAmonomer indicates that a given residue is poorly shielded by residues from 7052
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Figure 4. Representation of the SASA lost upon dimerization (Å2) as a function of the ΔΔGBinding (kcal/mol) for the interfacial charged, polar, and nonpolar amino acid residues.
its own subunit and exposed to the solvent and/or to residues from the other subunit, while a low SASAmonomer indicates a residue that is highly protected from other interactions by residues of its own subunit. Similarly, a high SASAdimer illustrates that in the dimer a given residue is very much exposed to the solvent, while a low SASAdimer indicates a residue extremely protected by residues from the two subunits. From the difference between SASAdimer and SASAmonomer (described as SASA lost upon dimerization), it is possible to get an idea of the contribution in terms of interaction surface area of each residue to the subunit interface (values expressed in percentage and in area). The results expressed in Table 1 show that all the hot spots identified have a very low SASA in the dimer. Leu97 and Leu5 have average SASAdimer values of 0.2% and 0.7%, respectively, while Arg87 and Ile50 have average SASAdimer values of 10.1% and 12.3%. Phe99 confirms this general tendency with a SASAdimer of 22.8%. These values contrast markedly with the estimated SASAmonomer values for these residues, for which values between 35.0% (Arg87) and 76.7% (Phe99) were determined. Considering the SASAdimer and SASAmonomer values, it becomes evident that dimerization leads to a major decrease in the SASA for these residues. In fact, for Leu5, Ile50, Leu97, and Phe99, this decrease is of more than 40% of their potential amino acid SASA. This difference is particularly noticeable in terms of absolute SASA (expressed in Å2) with the hot spots identified having typically average values of SASA lost upon dimerization higher than 100 Å2 (the only exception is Arg87, albeit with 83.9 Å2). Table 2 presents the average SASA values calculated for hot spots, warm spots, and null spots, considering the categories presented above. Hot spots have clearly a higher SASA when considering residues only from their own subunit (SASAmonomer), with 54% of their potential surface area free for interaction with the other subunit and/or with solvent molecules, a value that decreases to 9.2% in the dimer (average decrease of 133.9 Å2 per residue). Warm spots are less exposed when considering only the monomer (45%), and upon dimer formation the corresponding SASA decreases to 20.3%, which represents an average decrease of 65.5 Å2 per amino acid residue. Null spots interact preferentially with their own subunit, exhibiting an average SASAmonomer
of only 25.1%, a value which decreases only slightly when considering the dimer (to 17.4%, 21.4 Å2). Following the study of Bogan and Thorn,79 we have evaluated the existence of a relationship between the solvent exposed surface area of the interface amino acid residues and their corresponding free energy of binding. For that, we have compared the free energy of binding with the SASA lost upon dimerization for all the interfacial amino acid residue pairs. Results are presented in Figure 4, illustrating the behavior observed for the charged, polar, and nonpolar amino acid residues. The results demonstrate the existence of an evident correlation between the two quantities evaluated, particularly for the nonpolar amino acid residues. In general, the higher the decrease in the SASA when going from the monomer to the dimer in HIV PR (i.e., the SASA lost upon dimerization) the higher the energetic contribution of that particular amino acid for dimer formation. This correlation is naturally less linear for charged amino acid residues, for which other features (the charge for example) came into play. Globally, these results confirm that for HIV protease the contribution of the hot spots identified to the interfacial surface area is clearly higher than that of warm and null spots and that in the dimer formed these residues have typically a much smaller solvent accessible surface area than warm spots and null spots. 5. Hydrogen Bonding Analysis. One particular advantage that MD simulations in general offer when studying biological systems is the ability to determine a statistical picture on a variety of atomic level dynamic events. This feature is particularly important for events that are transient or at least short-lived, such as the formation and breaking of hydrogen bonds. To obtain a more detailed portrait of the PR subunit interface, we have performed a hydrogen bonding analysis of all the interactions between residues from the two subunits. Results are presented in Table 3 and graphically illustrated also in Figure 2. The results show that there are 19 hydrogen bonds between both subunits that are present during more than 40% of the simulation. All of these interactions involve residues that have been identified as hot spots or warm spots. Nine of these interactions (47%) involve hot spots, with four out of the five pairs of hot spots identified in this study participating in these interactions. The exception among the identified hot spots is 7053
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Table 3. Summary of the Hydrogen Bonds Formed between the Two Monomers in PR Calculated from the MD Simulation Performed for the Free Wild-Type Enzymea
*
Average distance calculated between the two heavy atoms involved, according to the AMBER notation. a The colors indicate the hot spots (red) and warm spots (yellow).
Ile50 for which no hydrogen bond with duration higher than 40% of the simulation was identified in this study. The most important hydrogen bonds (in terms of percentage of occupation) are established between Thr26 (warm spots) from one subunit and Leu24 of the other subunit, with percentages of occupation of 90.07% and 90.03%, respectively, for the Leu24βThr26R and Leu24RThr26β interactions. The relevance of these interactions in HIV PR has been previously discussed by Strisovsky et al.81 in the context of the “Fireman’s grip” hypothesis, which highlighted the importance of the hydrogen bonds with amino acid residues at position 26 for overall dimer stability. In particular, Strisovsky et al. demonstrated that the sulphydryl group of a cysteine mutant at this position could effectively substitute for the hydroxyl group of the threonine in the wild-type, with similar activity, but that in the alanine mutant dimer formation was compromised. This result was also confirmed in a more recent study.20 Our dynamic analysis of the hydrogen bonds in the HIV PR dimer interface confirms these hydrogen bonds as the most important in the HIV PR interface, with higher percentages of occupation, with smaller average interaction distances, and among the ones with the higher average lifetimes and maximal occupancy times. Hydrogen bonds established between hot spots from both subunits may also work as particularly important targets for the inhibition of dimer formation. Examples include the pairs Leu5RArg87β and Leu5βArg87R, two strong hydrogen bonds with percentages of occupation higher than 70%. Interestingly,
Ishima et al.31 have shown that mutating Arg87 by lysine disrupts the dimer interface contacts and leads to inhibition of PR dimerization. Other potentially important targets are the hydrogen bonds established between Leu97 and Ile3 (from both subunits) for which a total of 4 hydrogen bonds have been reported in the top 19 hydrogen bonds observed. Globally, these results provide an atomic level explanation for the energetic trend observed from the computational ASM results, showing the importance of the hydrogen bonds formed in the energetic contribution that each residue makes for dimer formation. 6. Analysis of the Interface in the Substrate-Bound HIV-PR Enzyme. To evaluate the differences in the HIV PR interface upon substrate binding, we have evaluated the contribution of the same amino acids considered in the previous analysis but in the substrate-bound PR enzyme. The results presented in Table 4 show that all the hot spots identified in PR in the absence of substrate (Leu5, Ile50, Arg87, Leu97, and Phe99) are also hot spots in the presence of substrate. A similar observation can be made regarding three of the four warm spots previously identified (Trp6, Thr26, Thr96). However, very significant differences take place in the substrate-bound enzyme, most of them directly associated to the interaction with the substrate molecule. Both Asp25 residues become hot spots, a behavior that is in agreement with the wellknown importance of this residue for substrate binding. Asp29 7054
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Table 4. Differences in the Binding Free Energies (ΔΔGBinding) for the Wild-Type PR and Alanine Mutant Variants Calculated for PR in the Presence of a Substratea
a
Values for the interface residues in both subunits.
changes from warm spot to hot spot with substrate binding, while Arg8 changes from null spot to warm spot. These three residues also interact with the substrate. Ile3 on the other hand has no interaction with the substrate molecule but experiences a moderate increase in ΔΔGbinding (average 0.32 kcal/mol) that transforms it into a warm spot. A very marked difference between the data obtained for the free enzyme and substrate-bound enzyme concerns the differences between the values calculated for the residues in the R and β chains. The ΔΔGbinding values calculated upon alanine mutation for the same residue in each subunit differ in many cases considerably, an observation that highlights the more asymmetric structure adopted by the dimer in the presence of the substrate,
when comparing with the results obtained for the enzyme in the absence of the substrate. In fact, the asymmetry of the substrate side chains results in distinct adaptations of each monomer. The average difference between residues from the two subunits is now 3 times higher than in the substrate-free enzyme, with several pairs of amino acid residues displaying differences in calculated ΔΔGbinding of more than 1.5 kcal/mol. Examples include Arg8, Asp25, Asp29, Asp30, Lys45, Arg87, and Phe99. From these residues, the Arg8, Asp29, Asp30, and Arg87 pairs interact with the different extremities of the peptide substrate (Lys-Ala-Arg-Val-Leu-Ala-Glu-Ala-Met-Ser). The asymmetry of this substrate molecule contributes directly to the differences in the ΔΔGbinding calculated for these pairs of residues. 7055
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The Journal of Physical Chemistry B These observations are in agreement with previous X-ray studies that have analyzed how this symmetric enzyme adjusts to recognize its asymmetric substrates.55 Although the specific alterations observed in this study are characteristic of the specific substrate considered, similar trends (albeit involving probably different combinations of residues) would also be observed for substrates having any of the other recognition sequences that HIV-1 protease cleaves specifically, as all of them are structurally asymmetric.
’ CONCLUSIONS In this study, we presented a detailed atomistic analysis of the subunit interface in the HIV-1 protease enzyme, an important target in HIV therapy. In particular, we have evaluated the energetic contribution of all the amino acid residues at the subunit interface of this homodimeric enzyme, in an attempt to identify the most important determinants for dimer formation. This analysis was further complemented with a dynamic analysis of the hydrogen bonds formed between residues from different subunits and of the solvent accessible surface areas of all interfacial amino acid residues, providing relevant clues for the development of new inhibitors of HIV-1 protease designed to block the association of the two subunits, thereby preventing the formation of the fully active PR homodimeric form. The ASM results highlight the particular importance of a set of five amino acid pairs for dimer formation (hot spots): Arg87, Phe99, Ile50, Leu97, and Leu5 (Figure 2 and Figure 3). Mutating one of these residues by alanine leads to a destabilization of the resulting dimer formed of more than 4 kcal/mol when comparing with the wild-type PR. These residues have also been shown to be the largest contributors, in terms of area, to the subunit interface of the dimer, with each of these residues losing on average 134 Å2 of solvent accessible surface area upon dimer formation. Four other pairs of residues also make a significant energetic contribution (albeit more modest than the latter) to dimer formation. These residues, identified as warm spots, are Trp6, Asp29, Thr26, and Thr96, and have also an intermediate contribution in terms of area to the subunit interface. All the remaining residues evaluated have only a residual energetic contribution to dimer formation and a much more modest contribution to the interfacial area between both subunits. This study also shows that all the most important hydrogen bonds between residues from different subunits involve at least one hot spot or one warm spot. In particular, the hydrogen bonds formed between Leu24 and Thr26 from the two subunits were shown to be the most stable ones during the molecular dynamics simulations performed, a result that is in line with previous experimental studies that have shown the importance of this hydrogen bond to dimer formation.20,81 Other key hydrogen bonds involve the pairs Leu5Arg87 and Ile3Leu97. Inhibitors designed to bind the residues identified in this study as hot spots or warm spots, or to interfere or disrupt the more relevant and persistent hydrogen bonds observed in the course of the MD simulations, are much more likely to have a greater effect on blocking the formation of the fully active PR homodimeric form. These features should be taken into account in future drug design and development studies targeting HIV-1 protease. ’ ASSOCIATED CONTENT
bS
Supporting Information. Detailed description and illustration on the protonation of the Asp 25 residues for both
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subunits in the free and in the substrate-bound PR enzyme. Comparison of the ΔΔGBinding values calculated by ASM for a selected number of interfacial amino acid residues starting from a PR model prepared from an alternative X-ray structure (2PC0). This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected].
’ ACKNOWLEDGMENT FCT (PTDC/QUI/68302/2006). ’ REFERENCES (1) Kallings, L. O. J. Int. Med. 2008, 263, 218–243. (2) Flexner, C. New Engl. J. Med. 1998, 338, 1281–1292. (3) Misson, J.; Clark, W.; Kendall, M. J. J. Clin. Pharm. Ther. 1997, 22, 109–117. (4) Deeks, S. G.; Smith, M.; Holodniy, M.; Kahn, J. O. J. Am. Med. Assoc. 1997, 277, 145–153. (5) Wlodawer, A.; Vondrasek, J. Annu. Rev. Biophys. Biomol. Struct. 1998, 27, 249–284. (6) Mastrolorenzo, A.; Rusconi, S.; Scozzafava, A.; Supuran, C. T. Exp. Opin. Ther. Pat. 2006, 16, 1067–1091. (7) Aruksakunwong, O.; Promsri, S.; Wittayanarakul, K.; Nimmanpipug, P.; Lee, V. S.; Wijitkosoom, A.; Sompornpisut, P.; Hannongbua, S. Curr. Comput. Aided Drug Des. 2007, 3, 201–213. (8) Martinez-Cajas, J. L.; Wainberg, M. A. Antiviral Res. 2007, 76, 203–221. (9) Fernandez-Montero, J. V.; Barreiro, P.; Soriano, V. Exp. Opin. Pharmacother. 2009, 10, 1615–1629. (10) Tamames, B.; Fernandes, P. A.; Ramos, M. J. Curr. Bioact. Compd. 2006, 2, 43–62. (11) Wlodawer, A.; Miller, M.; Jaskolski, M.; Sathyanarayana, B. K.; Baldwin, E.; Weber, I. T.; Selk, L. M.; Clawson, L.; Schneider, J.; Kent, S. B. H. Science 1989, 245, 616–621. (12) Miller, M.; Schneider, J.; Sathyanarayana, B. K.; Toth, M. V.; Marshall, G. R.; Clawson, L.; Selk, L.; Kent, S. B. H.; Wlodawer, A. Science 1989, 246, 1149–1152. (13) Copeland, T. D.; Oroszlan, S. Gene Anal. Tech. 1988, 5, 109–115. (14) Erickson, J. W.; Burt, S. K. Annu. Rev. Pharmacol. Toxicol. 1996, 36, 545–571. (15) Kurt, N.; Haliloglu, T.; Schiffer, C. A. Biophys. J. 2003, 85, 853–863. (16) Ozer, N.; Haliloglu, T.; Schiffer, C. A. Proteins 2006, 64, 444–456. (17) Moore, J. P.; Stevenson, M. Nat. Rev. Chem. Discovery 2000, 1, 40–49. (18) Condra, J. H.; Schleif, W. A.; Blahy, O. M.; Gabryelski, L. J.; Graham, D. J.; Quintero, J. C.; Rhodes, A.; Robbins, H. L.; Roth, E.; Shivaprakash, M.; Titus, D.; Yang, T.; Teppler, H.; Squires, K. E.; Deutsch, P. J.; Emini, E. A. Nature 1995, 374, 569–571. (19) Davis, D. A.; Brown, C. A.; Singer, K. E.; Wang, V.; Kaufman, J.; Stahl, S. J.; Wingfield, P.; Maeda, K.; Harada, S.; Yoshimura, K.; Kosalaraksa, P.; Mitsuya, H.; Yarchoan, R. Antiviral Res. 2006, 72, 89–99. (20) Koh, Y.; Matsumi, S.; Das, D.; Amano, M.; Davis, D. A.; Li, J. F.; Leschenko, S.; Baldridge, A.; Shioda, T.; Yarchoan, R.; Ghosh, A. K.; Mitsuya, H. J. Biol. Chem. 2007, 282, 28709–28720. (21) Spaltenstein, A.; Kamierski, W. M.; Miller, J. F.; Samano, V. Curr. Top. Med. Chem. 2005, 5, 1589–1607. (22) Camarasa, M. J.; Velazquez, S.; San-Felix, A.; Perez-Perez, M. J.; Gago, F. Antiviral Res. 2006, 71, 260–267. (23) Broglia, R. A.; Levy, Y.; Tiana, G. Curr. Opin. Struct. Biol. 2008, 18, 60–66. 7056
dx.doi.org/10.1021/jp200075s |J. Phys. Chem. B 2011, 115, 7045–7057
The Journal of Physical Chemistry B (24) Grant, S. K.; Deckman, I. C.; Culp, J. S.; Minnich, M. D.; Brooks, I. S.; Hensley, P.; Debouck, C.; Meek, T. D. Biochemistry 1992, 31, 9491–9501. (25) Xie, D.; Gulnik, S.; Gustchina, E.; Yu, B.; Shao, W.; Qoronfleh, W.; Nathan, A.; Erickson, J. W. Protein Sci. 1999, 8, 1702–1707. (26) Todd, M. J.; Semo, N.; Freire, E. J. Mol. Biol. 1998, 283, 475–488. (27) Levy, Y.; Caflisch, A. J. Phys. Chem. B 2003, 107, 3068–3079. (28) Louis, J. M.; Ishima, R.; Nesheiwat, I.; Pannell, L. K.; Lynch, S. M.; Torchia, D. A.; Gronenborn, A. M. J. Biol. Chem. 2003, 278, 6085–6092. (29) Levy, Y.; Caflisch, A.; Onuchic, J. N.; Wolynes, P. G. J. Mol. Biol. 2004, 340, 67–79. (30) Ishima, R.; Torchia, D. A.; Lynch, S. M.; Gronenborn, A. M.; Louis, J. M. J. Biol. Chem. 2003, 278, 43311–43319. (31) Ishima, R.; Ghirlando, R.; Tozser, J.; Gronenborn, A. M.; Torchia, D. A.; Louis, J. M. J. Biol. Chem. 2001, 276, 49110–49116. (32) Bowman, M. J.; Chmielewski, J. Bioorg. Med. Chem. 2009, 17, 967–976. (33) El Dine, R. S.; El Halawany, A. M.; Ma, C. M.; Hattori, M. J. Nat. Prod. 2009, 72, 2019–2023. (34) Babe, L. M.; Rose, J.; Craik, C. S. Proc. Natl. Acad. Sci. U.S.A. 1995, 92, 10069–10073. (35) Babe, L. M.; Rose, J.; Craik, C. S. Protein Sci. 1992, 1, 1244–1253. (36) Zutshi, R.; Chmielewski, J. Bioorg. Med. Chem. Lett. 2000, 10, 1901–1903. (37) Shultz, M. D.; Bowman, M. J.; Ham, Y. W.; Zhao, X. M.; Tora, G.; Chmielewski, J. Angew. Chem., Int. Ed. 2000, 39, 2710–2713. (38) Clackson, T.; Wells, J. A. Science 1995, 267, 383–386. (39) Pons, J.; Rajpal, A.; Kirsch, J. F. Protein Sci. 1999, 8, 958–968. (40) Keskin, O.; Ma, B. Y.; Nussinov, R. J. Mol. Biol. 2005, 345, 1281–1294. (41) Lopez, M. A.; Kollman, P. A. Protein Sci. 1993, 2, 1975–1986. (42) Kortemme, T.; Baker, D. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 14116–14121. (43) Schapira, M.; Totrov, M.; Abagyan, R. J. Mol. Recognit. 1999, 12, 177–190. (44) Aqvist, J.; Medina, C.; Samuelsson, J. E. Protein Eng. 1994, 7, 385–391. (45) Verkhivker, G. M.; Bouzida, D.; Gehlhaar, D. K.; Rejto, P. A.; Freer, S. T.; Rose, P. W. Proteins 2002, 48, 539–557. (46) Massova, I.; Kollman, P. A. J. Am. Chem. Soc. 1999, 121, 8133–8143. (47) Moreira, I. S.; Fernandes, P. A.; Ramos, M. J. J. Comput. Chem. 2007, 28, 644–654. (48) Kollman, P.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D. A.; Cheatham, T. E., III Acc. Chem. Res. 2000, 33, 889–897. (49) Massova, I.; Kollman, P. A. Perspect. Drug Discovery Des. 2000, 18, 113–135. (50) Moreira, I. S.; Fernandes, P. A.; Ramos, M. J. J. Phys. Chem. B 2006, 110, 10962–10969. (51) Moreira, I. S.; Fernandes, P. A.; Ramos, M. J. Int. J. Quantum Chem. 2007, 107, 299–310. (52) Moreira, I. S.; Fernandes, P. A.; Ramos, M. J. Theor. Chem. Acc. 2008, 120, 533–542. (53) Bernstein, F. C.; Koetzle, T. F.; Williams, G. J. B.; Meyer, E. F.; Brice, M. D.; Rodgers, J. R.; Kennard, O.; Shimanouchi, T.; Tasumi, M. J. Mol. Biol. 1977, 112, 535–542. (54) Surleraux, D. L. N. G.; Tahri, A.; Verschueren, W. G.; Pille, G. M. E.; de Kock, H. A.; Jonckers, T. H. M.; Peeters, A.; De Meyer, S.; Azijn, H.; Pauwels, R.; de Bethune, M. P.; King, N. M.; Prabu-Jeyabalan, M.; Schiffer, C. A.; Wigerinck, P. B. T. P. J. Med. Chem. 2005, 48, 1813–1822. (55) Prabu-Jeyabalan, M.; Nalivaika, E.; Schiffer, C. A. J. Mol. Biol. 2000, 301, 1207–1220. (56) Hyland, L. J.; Tomaszek, T. A.; Meek, T. D. Biochemistry 1991, 30, 8454–8463.
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(57) Piana, S.; Bucher, D.; Carloni, P.; Rothlisberger, U. J. Phys. Chem. B 2004, 108, 11139–11149. (58) Liu, H. Y.; MullerPlathe, F.; Vangunsteren, W. F. J. Mol. Biol. 1996, 261, 454–469. (59) Brik, A.; Wong, C. H. Org. Biomol. Chem. 2003, 1, 5–14. (60) Jaskolski, M.; Tomasselli, A. G.; Sawyer, T. K.; Staples, D. G.; Heinrikson, R. L.; Schneider, J.; Kent, S. B. H.; Wlodawer, A. Biochemistry 1991, 30, 1600–1609. (61) Porter, M. A.; Molina, P. A. J. Chem. Theory Comput. 2006, 2, 1675–1684. (62) Piana, S.; Carloni, P. Proteins 2000, 39, 26–36. (63) Case, D. A.; Darden, T. A.; Cheatham, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Merz, K. M.; Pearlman, D. A.; Crowley, M.; Walker, R. C.; Zhang, W.; Wang, B.; Hayik, S.; Roitberg, A.; Seabra, G.; Wong, K. F.; Paesani, F.; Wu, X.; Brozell, S.; Tsui, V.; Gohlke, H.; Yang, L.; Tang, C.; Mongan, J.; Hornak, V.; Cui, G.; Beroza, P.; Mathews, D. H.; Schafmeister, C.; Ross, W. S.; Kollman, P. A. AMBER 9.0 Software package; University of California: San Francisco, 2006. (64) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. J. Chem. Phys. 1983, 79, 926–935. (65) Cornell, W. D.; Cieplak, P.; Bayly, C. I.; Gould, I. R.; Merz, K. M.; Fergunson, D. M.; Spellmeyer, D. C.; Fox, T.; Caldwell, J. W.; Kollman, P. A. J. Am. Chem. Soc. 1995, 117, 5179–5197. (66) Ryckaert, J. P.; Ciccotti, G.; Berendsen, H. C. J. Comput. Phys. 1977, 23, 327–341. (67) Essman, V.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G. J. Chem. Phys. 1995, 103, 8577–8593. (68) Pastor, R. W.; Brooks, B. R.; Szabo, A. Mol. Phys. 1988, 65, 1409–1419. (69) Loncharich, R. J.; Brooks, B. R.; Pastor, R. W. Biopolymers 1992, 32, 523–535. (70) Izaguirre, J. A.; Catarello, D. P.; Wozniak, J. M.; Skeel, R. D. J. Chem. Phys. 2001, 114, 2090–2098. (71) Huo, S.; Massova, I.; Kollman, P. A. J. Comput. Chem. 2002, 23, 15–27. (72) Gohlke, H.; Case, D. A. J. Comput. Chem. 2004, 25, 238–250. (73) Rocchia, W.; Alexov, E.; Honig, B. J. Phys. Chem. B 2001, 105, 6507–6514. (74) Rocchia, W.; Sridharan, S.; Nicholls, A.; Alexov, E.; Chiabrera, A.; Honig, B. J. Comput. Chem. 2002, 23, 128–137. (75) Connolly, M. L. J. Appl. Crystallogr. 1983, 16, 548–558. (76) Moreira, I. S.; Fernandes, P. A.; Ramos, M. J. Proteins 2007, 68, 803–812. (77) Humphrey, W.; Dalke, A.; Schulten, K. J. Mol. Graphics 1996, 14, 33–&. (78) Weber, I. T. J. Biol. Chem. 1990, 265, 10492–10496. (79) Bogan, A. A.; Thorn, K. S. J. Mol. Biol. 1998, 280, 1–9. (80) Porter, M. A.; Molina, P. A. J. Chem. Theory Comput. 2006, 2, 1675–1684. (81) Strisovsky, K.; Tessmer, U.; Langner, J.; Konvalinka, J.; Krausslich, H. G. Protein Sci. 2000, 9, 1631–1641. (82) Liu, F. L.; Kovalevsky, A. Y.; Tie, Y. F.; Ghosh, A. K.; Harrison, R. W.; Weber, I. T. J. Mol. Biol. 2008, 381, 102–115. (83) Bottcher, J.; Blum, A.; Heine, A.; Diederich, W. E.; Klebe, G. J. Mol. Biol. 2008, 383, 347–357. (84) Kovalevsky, A. Y.; Tie, Y. F.; Liu, F. L.; Boross, P. I.; Wang, Y. F.; Leshchenko, S.; Ghosh, A. K.; Harrison, R. W.; Weber, I. T. J. Med. Chem. 2006, 49, 1379–1387. (85) King, N. M.; Prabu-Jeyabalan, M.; Nalivaika, E. A.; Wigerinck, P.; de Bethune, M. P.; Schiffer, C. A. J. Virol. 2004, 78, 12012–12021. (86) Kovalevsky, A. Y.; Liu, F. L.; Leshchenko, S.; Ghosh, A. K.; Louis, J. M.; Harrison, R. W.; Weber, I. T. J. Mol. Biol. 2006, 363, 161–173. (87) Tie, Y. F.; Boross, P. I.; Wang, Y. F.; Gaddis, L.; Hussain, A. K.; Leshchenko, S.; Ghoshl, A. K.; Louis, J. M.; Harrison, R. W.; Weber, I. T. J. Mol. Biol. 2004, 338, 341–352. 7057
dx.doi.org/10.1021/jp200075s |J. Phys. Chem. B 2011, 115, 7045–7057
