C2-symmetric inhibitors of Plasmodium falciparum plasmepsin II: synthesis and theoretical predictions

A series of C(2)-symmetric compounds with a mannitol-based scaffold has been investigated, both theoretically and experimentally, as Plm II inhibitors. Four different stereoisomers with either benzyloxy or allyloxy P1/P1' side chains were studied. Computational ranking of the binding affinities of the eight compounds was carried out using the linear interaction energy (LIE) method relying on a complex previously determined by crystallography. Within both series of isomers the theoretical binding energies were in agreement with the enzymatic measurements, illustrating the power of the LIE method for the prediction of ligand affinities prior to synthesis. The structural models of the enzyme-inhibitor complexes obtained from the MD simulations provided a basis for interpretation of further structure-activity relationships. Hence, the affinity of a structurally similar ligand, but with a different P2/P2' substituent was examined using the same procedure. The predicted improvement in binding constant agreed well with experimental results.     

Computational analysis of binding of P1 variants to trypsin

The binding of P1 variants of bovine pancreatic trypsin inhibitor (BPTI) to trypsin has been investigated by means of molecular dynamics simulations. The specific interaction formed between the amino acid at the primary binding (P1) position of the binding loop of BPTI and the specificity pocket of trypsin was estimated by use of the linear interaction energy (LIE) method. Calculations for 13 of the naturally occurring amino acids at the P1 position were carried out, and the results obtained were found to correlate well with the experimental binding free energies. The LIE calculations rank the majority of the 13 variants correctly according to the experimental association energies and the mean error between calculated and experimental binding free energies is only 0.38 kcal/mole, excluding the Glu and Asp variants, which are associated with some uncertainties regarding protonation and the possible presence of counter-ions. The three-dimensional structures of the complex with three of the P1 variants (Asn, Tyr, and Ser) included in this study have not at present been solved by any experimental techniques and, therefore, were modeled on the basis of experimental data from P1 variants of similar size. Average structures were calculated from the MD simulations, from which specific interactions explaining the broad variation in association energies were identified. The present study also shows that explicit treatment of the complex water-mediated hydrogen bonding network at the protein-protein interface is of crucial importance for obtaining reliable binding free energies. The successful reproduction of relative binding energies shows that this type of methodology can be very useful as an aid in rational design and redesign of biologically active macromolecules.     

Probing the effect of point mutations at protein-protein interfaces with free energy calculations

We have studied the effect of point mutations of the primary binding residue (P1) at the protein-protein interface in complexes of chymotrypsin and elastase with the third domain of the turkey ovomucoid inhibitor and in trypsin with the bovine pancreatic trypsin inhibitor, using molecular dynamics simulations combined with the linear interaction energy (LIE) approach. A total of 56 mutants have been constructed and docked into their host proteins. The free energy of binding could be reliably calculated for 52 of these mutants that could unambiguously be fitted into the binding sites. We find that the predicted binding free energies are in very good agreement with experimental data with mean unsigned errors between 0.50 and 1.03 kcal/mol. It is also evident that the standard LIE model used to study small drug-like ligand binding to proteins is not suitable for protein-protein interactions. Three different LIE models were therefore tested for each of the series of protein-protein complexes included, and the best models for each system turn out to be very similar. The difference in parameterization between small drug-like compounds and protein point mutations is attributed to the preorganization of the binding surface. Our results clearly demonstrate the potential of free energy calculations for probing the effect of point mutations at protein-protein interfaces and for exploring the principles of specificity of hot spots at the interface.     

Comparison of end-point continuum-solvation methods for the calculation of protein-ligand binding free energies

We have compared the predictions of ligand‐binding affinities from several methods based on end‐point molecular dynamics simulations and continuum solvation, that is, methods related to MM/PBSA (molecular mechanics combined with Poisson–Boltzmann and surface area solvation). Two continuum‐solvation models were considered, viz., the Poisson–Boltzmann (PB) and generalised Born (GB) approaches. The nonelectrostatic energies were also obtained in two different ways, viz., either from the sum of the bonded, van der Waals, nonpolar solvation energies, and entropy terms (as in MM/PBSA), or from the scaled protein–ligand van der Waals interaction energy (as in the linear interaction energy approach, LIE). Three different approaches to calculate electrostatic energies were tested, viz., the sum of electrostatic interaction energies and polar solvation energies, obtained either from a single simulation of the complex or from three independent simulations of the complex, the free protein, and the free ligand, or the linear‐response approximation (LRA). Moreover, we investigated the effect of scaling the electrostatic interactions by an effective internal dielectric constant of the protein (?_int). All these methods were tested on the binding of seven biotin analogues to avidin and nine 3‐amidinobenzyl‐1H‐indole‐2‐carboxamide inhibitors to factor Xa. For avidin, the best results were obtained with a combination of the LIE nonelectrostatic energies with the MM+GB electrostatic energies from a single simulation, using ?_int = 4. For fXa, standard MM/GBSA, based on one simulation and using ?_int = 4–10 gave the best result. The optimum internal dielectric constant seems to be slightly higher with PB than with GB solvation. © Proteins 2012; © 2012 Wiley Periodicals, Inc.     

Superimposing the 27 crystal protein/inhibitor complexes of β-secretase to calculate the binding affinities by the linear interaction energy method

BACE-1 is an important target for designing therapeutic agents for the treatment of Alzheimer's disease. An improved linear interaction energy (LIE) model has been developed to calculate the binding free energies of β-secretase (BACE-1) by superimposing the 27 crystal BACE-1/inhibitor complexes to put a diverse set of 27 co-crystallized ligands into the binding pocket. These co-crystallized conformations of ligands were set as the initial binding conformations for LIE simulation. The effects of two protein conformations (i.e., 1W51 and 1FKN), two sampling methods (i.e., energy minimization and hybrid Monte Carlo [HMC]), and energy terms were studied. Using 1W51 crystal structure and HMC sampling technique, the best binding affinity model for the full set of ligands was found to have a root-mean-square error of 0.996 kcal/mol.     

Energetics of codon-anticodon recognition on the small ribosomal subunit

Recent crystal structures of the small ribosomal subunit have made it possible to examine the detailed energetics of codon recognition on the ribosome by computational methods. The binding of cognate and near-cognate anticodon stem loops to the ribosome decoding center, with mRNA containing the Phe UUU and UUC codons, are analyzed here using explicit solvent molecular dynamics simulations together with the linear interaction energy (LIE) method. The calculated binding free energies are in excellent agreement with experimental binding constants and reproduce the relative effects of mismatches in the first and second codon position versus a mismatch at the wobble position. The simulations further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with the Phe UUU codon. It is also found that the ribosome significantly enhances the intrinsic stability differences of codon-anticodon complexes in aqueous solution. Structural analysis of the simulations confirms the previously suggested importance of the universally conserved nucleotides A1492, A1493, and G530 in the decoding process.     

Improved Ligand-Protein Binding Affinity Predictions Using Multiple Binding Modes

Accurate ligand-protein binding affinity prediction, for a set of similar binders, is a major challenge in the lead optimization stage in drug development. In general, docking and scoring functions perform unsatisfactorily in this application. Docking calculations, followed by molecular dynamics simulations and free energy calculations can be applied to improve the predictions. However, for targets with large, flexible binding sites, with no experimentally determined binding modes for a set of ligands, insufficient sampling can decrease the accuracy of the free energy calculations. Cytochrome P450s, a protein family of major importance for drug metabolism, is an example of a challenging target for binding affinity predictions. As a result, the choice of starting structure from the docking solutions becomes crucial. In this study, an iterative scheme is introduced that includes multiple independent molecular dynamics simulations to obtain weighted ensemble averages to be used in the linear interaction energy method. The proposed scheme makes the initial pose selection less crucial for further simulation, as it automatically calculates the relative weights of the various poses. It also properly takes into account the possibility that multiple binding modes contribute similarly to the overall affinity, or of similar compounds occupying very different poses. The method was applied to a set of 12 compounds binding to cytochrome P450 2C9 and it displayed a root mean-square error of 2.9 kJ/mol.     

Analysis of cyclosporin A and a set of analogs as inhibitors of a T. cruzi cyclophilin by docking and molecular dynamics

Cyclophilins (CyPs) are enzymes involved in protein folding. In Trypanosoma cruzi (T. cruzi), the most abundantly expressed CyP is the isoform TcCyP19. It has been shown that TcCyP19 is inhibited by the immunosuppressive drug cyclosporin A (CsA) and analogs, which also proved to have potent trypanosomicidal activity in vitro. In this work, we continue and expand a previous study on the molecular interactions of CsA, and a set of analogs modeled in complexes with TcCyP19. The modeled complexes were used to evaluate binding free energies by molecular dynamics (MD), applying the Linear Interaction Energy (LIE) method. In addition, putative binding sites were identified by molecular docking. In our analysis, the binding free energy calculations did not correlate with experimental data. The heterogeneity of the non-bonded energies and the variation in the pattern of hydrogen bonds suggest that the systems may not be suitable for the application of the LIE method. Further, the docking calculations identified two other putative binding sites with comparable scoring energies to the active site, a fact that may also explain the lack of correlation found. Kinetic experiments are needed to confirm or reject the multiple binding sites hypothesis. In the meantime, MD simulations at the alternative sites, employing other methods to compute binding free energies, might be successful at finding good correlations with the experimental data.     

Calculation of binding affinities of HIV-1 RT and β-secretase inhibitors using the linear interaction energy method with explicit and continuum solvation approaches

The linear interaction energy (LIE) approach has been applied to estimate the binding free energies of representative sets of HIV-1 RT and β-Secretase inhibitors, using both molecular dynamics (MD) and tethered energy minimization sampling protocols with the OPLS-AA potential, using a range of solvation methodologies. Generalized Born (GB), ‘shell’ and periodic boundary condition (PBC) solvation were used, the latter with reaction field (RF) electrostatics. Poisson-Boltzmann (PB) and GB continuum electrostatics schemes were applied to the simulation trajectories for each solvation type to estimate the electrostatic ligand-water interaction energy in both the free and bound states. Reasonable agreement of the LIE predictions was obtained with respect to experimental binding free energy estimates for both systems: for instance, ‘PB’ fits on MD trajectories carried out with PBC solvation and RF electrostatics led to models with standard errors of 1.11 and 1.03 kcal mol⁻¹ and coefficients of determination, r ² of 0.76 and 0.75 for the HIV-1 RT and β-Secretase sets. However, it was also found that results from MD sampling using PBC solvation provided only slightly better fits than from simulations using shell or Born solvation or tethered energy minimization sampling. Figure Evolution of the running averages for compound H11 (binding to HIV-1RT) of the bound state ligand-water and ligand-protein interaction energies. The ligand-water electrostatic terms are twice the corresponding GB and PB electrostatic solvation free energies. The ligand-receptor van der Waals and Coulombic interaction energies are also shown, in addition to the ligand-water van der Waals interaction term. The terms were calculated (without application of a cut-off) from a trajectory sampled under PBC solvation with reaction field electrostatics Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Absolute binding free energy calculations: On the accuracy of computational scoring of protein–ligand interactions

Calculating the absolute binding free energies is a challenging task. Reliable estimates of binding free energies should provide a guide for rational drug design. It should also provide us with deeper understanding of the correlation between protein structure and its function. Further applications may include identifying novel molecular scaffolds and optimizing lead compounds in computer‐aided drug design. Available options to evaluate the absolute binding free energies range from the rigorous but expensive free energy perturbation to the microscopic linear response approximation (LRA/β version) and related approaches including the linear interaction energy (LIE) to the more approximated and considerably faster scaled protein dipoles Langevin dipoles (PDLD/S‐LRA version) as well as the less rigorous molecular mechanics Poisson–Boltzmann/surface area (MM/PBSA) and generalized born/surface area (MM/GBSA) to the less accurate scoring functions. There is a need for an assessment of the performance of different approaches in terms of computer time and reliability. We present a comparative study of the LRA/β, the LIE, the PDLD/S‐LRA/β, and the more widely used MM/PBSA and assess their abilities to estimate the absolute binding energies. The LRA and LIE methods perform reasonably well but require specialized parameterization for the nonelectrostatic term. The PDLD/S‐LRA/β performs effectively without the need of reparameterization. Our assessment of the MM/PBSA is less optimistic. This approach appears to provide erroneous estimates of the absolute binding energies because of its incorrect entropies and the problematic treatment of electrostatic energies. Overall, the PDLD/S‐LRA/β appears to offer an appealing option for the final stages of massive screening approaches. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Examining methods for calculations of binding free energies: LRA, LIE, PDLD-LRA, and PDLD/S-LRA calculations of ligands binding to an HIV protease

Several strategies for evaluation of the protein-ligand binding free energies are examined. Particular emphasis is placed on the Linear Response Approximation (LRA) (Lee et. al., Prot Eng 1992;5:215-228) and the Linear Interaction Energy (LIE) method (Aqvist et. al., Prot Eng 1994;7:385-391). The performance of the Protein Dipoles Langevin Dipoles (PDLD) method and its semi-microscopic version (the PDLD/S method) is also considered. The examination is done by using these methods in the evaluating of the binding free energies of neutral C2-symmetric cyclic urea-based molecules to Human Immunodeficiency Virus (HIV) protease. Our starting point is the introduction of a thermodynamic cycle that decomposes the total binding free energy to electrostatic and non-electrostatic contributions. This cycle is closely related to the cycle introduced in our original LRA study (Lee et. al., Prot Eng 1992;5:215-228). The electrostatic contribution is evaluated within the LRA formulation by averaging the protein-ligand (and/or solvent-ligand) electrostatic energy over trajectories that are propagated on the potentials of both the polar and non-polar (where all residual charges are set to zero) states of the ligand. This average involves a scaling factor of 0.5 for the contributions from each state and this factor is being used in both the LRA and LIE methods. The difference is, however, that the LIE method neglects the contribution from trajectories over the potential of the non-polar state. This approximation is entirely valid in studies of ligands in water but not necessarily in active sites of proteins. It is found in the present case that the contribution from the non-polar states to the protein-ligand binding energy is rather small. Nevertheless, it is clearly expected that this term is not negligible in cases where the protein provides preorganized environment to stabilize the residual charges of the ligand. This contribution can be particularly important in cases of charged ligands. The analysis of the non-electrostatic term is much more complex. It is concluded that within the LRA method one has to complete the relevant thermodynamic cycle by evaluating the binding free energy of the "non-polar" ligand, l;, where all the residual charges are set to zero. It is shown that the LIE term, which involves the scaling of the van der Waals interaction by a constant beta (usually in the order of 0.15 to 0.25), corresponds to this part of the cycle. In order to elucidate the nature of this non-electrostatic term and the origin of the scaling constant beta, it is important to evaluate explicitly the different contributions to the binding energy of the non-polar ligand, DeltaG(bind,l;). Since this cannot be done at present (for relatively large ligands) by rigorous free energy perturbation approaches, we evaluate DeltaG(bind,l;) by the PDLD approach, augmented by microscopic calculations of the change in configurational entropy upon binding. This evaluation takes into account the van der Waals, hydrophobic, water penetration and entropic contributions, which are the most important free energy contributions that make up the total DeltaG(bind,l;). The sum of these contributions is scaled by a factor straight theta and it is argued that obtaining a quantitative balance between these contributions should result in straight theta = 1. By doing so we should have a reliable estimate of the value of the LIE beta and a way to understand its origin. The present approach gives straight theta values between 0.5 and 0.73, depending on the approximation used. This is encouraging but still not satisfying. Nevertheless, one might be able to use our PDLD approach to estimate the change of the LIE straight theta between different protein active sites. It is pointed out that the LIE method is quite similar to our original approach where the electrostatic term was evaluated by the LRA method and the non-electrostatic term by the PDLD method (with its vdw, solvation,     

A molecular dynamics study of a miRNA:mRNA interaction

In this paper we present a methodology to evaluate the binding free energy of a miRNA:mRNA complex through molecular dynamics (MD)–thermodynamic integration (TI) simulations. We applied our method to the Caenorhabditis elegans let-7 miRNA:lin-41 mRNA complex—a validated miRNA:mRNA interaction—in order to estimate the energetic stability of the structure. To make the miRNA:mRNA simulation possible and realistic, the methodology introduces specific solutions to overcome some of the general challenges of nucleic acid simulations and binding free energy computations that have been discussed widely in many previous research reports. The main features of the proposed methodology are: (1) positioning of the restraints imposed on the simulations in order to guarantee complex stability; (2) optimal sampling of the phase space to achieve satisfactory accuracy in the binding energy value; (3) determination of a suitable trade-off between computational costs and accuracy of binding free energy computation by the assessment of the scalability characteristics of the parallel simulations required for the TI. The experiments carried out demonstrate that MD simulations are a viable strategy for the study of miRNA binding characteristics, opening the way to the development of new computational target prediction methods based on three-dimensional structure information.     

Quantum and molecular dynamics study for binding of macrocyclic inhibitors to human alpha-thrombin

Molecular dynamics simulations followed by quantum mechanical calculation and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) analysis have been carried out to study binding of proline- and pyrazinone-based macrocyclic inhibitors (L86 and T76) to human alpha-thrombin. Detailed binding interaction energies between these inhibitors and individual protein fragments are calculated using DFT method based on a new quantum mechanical approach for computing protein-ligand interaction energy. The analysis of detailed interaction energies provides insight on the protein-ligand binding mechanism. Study shows that T76 and L86 bind to thrombin in a very similar "inhibition mode" except that T76 has relatively weaker binding interaction with Glu(217). The analysis from quantum calculation of binding interaction is consistent with the MM-PBSA calculation of binding free energy, and the calculated free energies for L86/T76-thrombin binding agree well with the experimental data.     

Molecular interactions of c-ABL mutants in complex with imatinib/nilotinib: a computational study using linear interaction energy (LIE) calculations

In spite of the effectiveness of Imatinib for chronic myeloid leukemia (CML) treatment, resistance has repeatedly been reported and is associated with point mutations in the BCR-ABL chimeric gene. To overcome this resistance, several inhibitors of BCR-ABL tyrosine kinase activity were developed. In this context, computational simulations have become a powerful tool for understanding drug-protein interactions. Herein, we report a comparative molecular dynamics analysis of the interaction between two tyrosine kinase inhibitors (imatinib or nilotinib) against wild type c-ABL protein and 12 mutants, using the semi-empirical linear interaction energy (LIE) method, to assess the feasibility of this approach for studying resistance against the inhibitory activity of these drugs. In addition, to understand the structural changes that are associated with resistance, we describe the behavior of water molecules that interact simultaneously with specific residues (Glu286, Lys271 and Asp381) of c-ABL (wild type or mutant) and their relationship with drug resistance. Experimental IC50 values for the interaction between imatinib, wild type c-ABL, and 12 mutants were used to obtain the proper LIE coefficients (α, β and γ) to estimate the free energy of the binding of imatinib with wild-type and mutant proteins, and values were extrapolated for the analysis of the nilotinib/c-ABL interaction. Our results indicate that LIE was suitable to predict the superior inhibitory activity of nilotinib and the resistance to inhibition that was observed in c-ABL mutants. Additionally, for c-ABL mutants, the observed number of water molecules being turned over while interacting with amino acids Glu286, Lys271 and Asp381 was associated with resistance to imatinib, resulting in a less effective inhibition of the kinase activity.     

Improved prediction of HIV-1 protease-inhibitor binding energies by molecular dynamics simulations

Background  

The accurate prediction of enzyme-substrate interaction energies is one of the major challenges in computational biology. This study describes the improvement of protein-ligand binding energy prediction by incorporating protein flexibility through the use of molecular dynamics (MD) simulations.     

Macrocyclic inhibitors of the malarial aspartic proteases plasmepsin I, II, and IV

The first macrocyclic inhibitor of the Plasmodium falciparum aspartic proteases plasmepsin I, II, and IV with considerable selectivity over the human aspartic protease cathepsin D has been identified. A series of macrocyclic compounds were designed and synthesized. Cyclizations were accomplished using ring-closing metathesis with the second generation Grubbs catalyst. These compounds contain either a 13-membered or a 16-membered macrocycle and incorporate a 1,2-dihydroxyethylene as transition state mimicking unit. The binding mode of this new class of compounds was predicted with automated docking and molecular dynamics simulations, with an estimation of the binding affinities through the linear interaction energy (LIE) method.     

Prediction of the binding modes between BB-83698 and peptide deformylase from Bacillus stearothermophilus by docking and molecular dynamics simulation

BB-83698 is a first potent inhibitor of peptide deformylase in this novel class to enter clinical trials. In this study, automated docking, molecular dynamics simulation and binding free energy calculations with the linear interaction energy (LIE) method are first applied to investigate the binding of BB-83698 to the peptide deformylase from Bacillus stearothermophilus. The lowest docking energy structure from each cluster is selected as different representative binding modes. Compared with the experimental data, the results show that the binding of BB-83698 in Mode 1 is the most stable, with a binding free energy of -41.35 kJ/mol. The average structure of the Mode 1 complex suggests that inhibitor interacts with Ile59 and Gly109 by hydrogen bond interaction and with Pro47, Pro57, Ile59 and Leu146 by hydrophobic interaction are essential for the activity of BB-83698. Mode 2 represents a new binding mode. Additionally, if the hydrophilic group is introduced to the benzo-[1,3]-dioxole ring, the binding affinity of BB-83698 to the peptide deformylase from B. stearothermophilus will be greatly improved.     

Non-Boltzmann thermodynamic integration (NBTI) for macromolecular systems: relative free energy of binding of trypsin to benzamidine and benzylamine

The relative free energies of binding of trypsin to two amine inhibitors, benzamidine (BZD) and benzylamine (BZA), were calculated using non-Boltzmann thermodynamic integration (NBTI). Comparison of the simulations with the crystal structures of both complexes, trypsin-BZD and trypsin-BZA, shows that NBTI simulations better sample conformational space relative to thermodynamic integration (TI) simulations. The relative binding free energy calculated using NBTI was much closer to the experimentally determined value than that obtained using TI. The error in the TI simulation was found to be primarily due to incorrect sampling of BZA's conformation in the binding pocket. In contrast, NBTI produces a smooth mutation from BZD to BZA using a surrogate potential, resulting in a much closer agreement between the inhibitors' conformations and the omit electron density maps. This superior agreement between experiment and simulation, of both relative binding free energy differences and conformational sampling, demonstrates NBTI's usefulness for free energy calculations in macromolecular simulations.