Engineered MATE multidrug transporters reveal two functionally distinct ion-coupling pathways in NorM from Vibrio cholerae.

Multidrug and toxic compound extrusion (MATE) transport proteins confer multidrug resistance on pathogenic microorganisms and affect pharmacokinetics in mammals. Our understanding of how MATE transporters work, has mostly relied on protein structures and MD simulations. However, the energetics of drug transport has not been studied in detail. Many MATE transporters utilise the electrochemical H+ or Na+ gradient to drive substrate efflux, but NorM-VC from Vibrio cholerae can utilise both forms of metabolic energy. To dissect the localisation and organisation of H+ and Na+ translocation pathways in NorM-VC we engineered chimaeric proteins in which the N-lobe of H+-coupled NorM-PS from Pseudomonas stutzeri is fused to the C-lobe of NorM-VC, and vice versa. Our findings in drug binding and transport experiments with chimaeric, mutant and wildtype transporters highlight the versatile nature of energy coupling in NorM-VC, which enables adaptation to fluctuating salinity levels in the natural habitat of V. cholerae.

A New Lipid Force Field (FUJI).

To explore inhomogeneous and anisotropic systems such as lipid bilayers, the Lennard-Jones particle mesh Ewald (LJ-PME) method has been applied without a conventional isotropic dispersion correction. As the popular AMBER and CHARMM lipid force fields were developed using a cutoff scheme, their lipid bilayers unacceptably shrink when using the LJ-PME method. In this study, a new all-atom lipid force field (FUJI) was developed on the basis of the AMBER force-field scheme including the Lipid14 van der Waals parameters. Point charges were calculated using the restrained electrostatic potentials of many lipid conformers. Further, torsion energy profiles were calculated using high-level ab initio molecular orbitals (LCCSD(T)/Aug-cc-pVTZ//LMP2/Aug-cc-pVTZ), following which the molecular mechanical dihedral parameters were derived through a fast Fourier transform. By incorporation of these parameters into a new lipid force field without fitting experimental data, the desired lipid characteristics such as the area per lipid and lateral diffusion coefficients were obtained through GROMACS molecular dynamics simulations using the LJ-PME method and virtual hydrogen sites. The calculated area per lipid and lateral diffusion coefficients showed satisfactory agreement with experimental data. Furthermore, the electron-density profiles along the membrane normal were calculated for pure lipid bilayers, and the resulting membrane thicknesses agreed well with the experimental values. As the new lipid force field is compatible with FUJI for protein and small molecules, the new FUJI force field will offer accurate modeling for complex systems consisting of various membrane proteins and lipids.

Cupid and Psyche system for the diagnosis and treatment of advanced cancer.

In advanced cancer patients, malignant cells invade and disseminate within normal cells and develop resistance to therapy with additional genetic mutations, which makes radical cure very difficult. Precision medicine against advanced cancer is hampered by the lack of systems aimed at multiple target molecules within multiple loci. Here, we report the development of a versatile diagnostic and therapeutic system for advanced cancer, named the Cupid and Psyche system. Based on the strong non-covalent interaction of streptavidin and biotin, a low immunogenic mutated streptavidin, Cupid, and a modified artificial biotin, Psyche, have been designed. Cupid can be fused with various single-chain variable fragment antibodies and forms tetramer to recognize cancer cells precisely. Psyche can be conjugated to a wide range of diagnostic and therapeutic agents against malignant cells. The Cupid and Psyche system can be used in pre-targeting therapy as well as photo-immunotherapy effectively in animal models supporting the concept of a system for precision medicine for multiple targets within multiple loci.

Affinity Improvement of a Cancer-Targeted Antibody through Alanine-Induced Adjustment of Antigen-Antibody Interface.

To investigate favorable single amino acid substitutions that improve antigen-antibody interactions, alanine (Ala) mutagenesis scanning of the interfacial residues of a cancer-targeted antibody, B5209B, was performed based on X-ray crystallography analysis. Two substitutions were shown to significantly enhance the binding affinity for the antigen, by up to 30-fold. One substitution improved the affinity by a gain of binding enthalpy, whereas the other substitution improved the affinity by a gain of binding entropy. Molecular dynamics simulations showed that the enthalpic improvement could be attributed to the stabilization of distant salt bridges located at the periphery of the antigen-antibody interface. The entropic improvement was due to the release of water molecules that were stably trapped in the antigen-antibody interface of the wild-type antibody. Importantly, these effects of the Ala substitutions were caused by subtle adjustments of the binding interface. These results will be helpful to design high-affinity antibodies with avoiding entropy-enthalpy compensation.

Initiation of prolyl cis-trans isomerisation in the CDR-H3 loop of an antibody in response to antigen binding.

Proline cis-trans isomerisation is a regulatory mechanism used in a range of biological processes, and is related to various diseases such as Alzheimers disease and cancer. However, the details of the exact molecular mechanism by which it occurs are not known. Using X-ray crystallography, proline isomerisation has been shown to occur following formation of an antigen-antibody complex between the target epiregulin (EPR) and the antibody 9E5, at proline (Pro103), located in the third complementarity-determining region (CDR) of the heavy chain of 9E5. To obtain an accurate description of the pathway involved in cis-trans isomerisation in this system, we performed ten independent long molecular dynamics (MD) simulations starting at a stable transient bound structure obtained from many short binding MD simulations. As a result, we were able to describe the process by which cis-trans isomerisation is initiated, and suggest a catalysis mechanism for cis-trans isomerization in this antigen-antibody system. We found that Asp102, which is immediately adjacent to Pro103, rotates while changing its interacting partner residues in the light chain of 9E5, and at the same time EPR polar residues help to stabilise the intermediate states in the isomerisation process by interacting strongly with Asp102.

Molecular dynamics analysis to evaluate docking pose prediction.

The accurate prediction of a ligand-protein complex structure is important for computer-assisted drug development. Although many docking methods have been developed over the last three decades, the success of binding structure prediction remains greatly limited. The purpose of this study was to demonstrate the usefulness of molecular dynamics (MD) simulation in assessing a docking pose predicted using a docking program. If the predicted pose is not unstable in an aqueous environment, MD simulation equilibrates the system and removes the ligand from the predicted position. Here we investigated two proteins that are important potential therapeutic targets: ß2 adrenergic receptor (ß2AR) and PR-Set7. While ß2AR is rigid and its ligands are very similar to the template ligand (carazolol), PR-Set7 is very flexible and its ligands vary greatly from the template ligand (histone H4 tail peptide). On an empirical basis, we usually expect that the docking prediction is accurate when the protein is rigid and its ligands are similar to the template ligand. The MD analyses in this study clearly suggested such a tendency. Furthermore, we discuss the possibility that the MD simulation can predict the binding pose of a ligand.

Epiregulin Recognition Mechanisms by Anti-epiregulin Antibody 9E5: STRUCTURAL, FUNCTIONAL, AND MOLECULAR DYNAMICS SIMULATION ANALYSES.

Epiregulin (EPR) is a ligand of the epidermal growth factor (EGF) family that upon binding to its epidermal growth factor receptor (EGFR) stimulates proliferative signaling, especially in colon cancer cells. Here, we describe the three-dimensional structure of the EPR antibody (the 9E5(Fab) fragment) in the presence and absence of EPR. Among the six complementarity-determining regions (CDRs), CDR1-3 in the light chain and CDR2 in the heavy chain predominantly recognize EPR. In particular, CDR3 in the heavy chain dramatically moves with cis-trans isomerization of Pro(103). A molecular dynamics simulation and mutational analyses revealed that Arg(40) in EPR is a key residue for the specific binding of 9E5 IgG. From isothermal titration calorimetry analysis, the dissociation constant was determined to be 6.5 nm. Surface plasmon resonance analysis revealed that the dissociation rate of 9E5 IgG is extremely slow. The superimposed structure of 9E5(Fab)·EPR on the known complex structure of EGF·EGFR showed that the 9E5(Fab) paratope overlaps with Domains I and III on the EGFR, which reveals that the 9E5(Fab)·EPR complex could not bind to the EGFR. The 9E5 antibody will also be useful in medicine as a neutralizing antibody specific for colon cancer.

The feasibility of an efficient drug design method with high-performance computers.

In this study, we propose a supercomputer-assisted drug design approach involving all-atom molecular dynamics (MD)-based binding free energy prediction after the traditional design/selection step. Because this prediction is more accurate than the empirical binding affinity scoring of the traditional approach, the compounds selected by the MD-based prediction should be better drug candidates. In this study, we discuss the applicability of the new approach using two examples. Although the MD-based binding free energy prediction has a huge computational cost, it is feasible with the latest 10 petaflop-scale computer. The supercomputer-assisted drug design approach also involves two important feedback procedures: The first feedback is generated from the MD-based binding free energy prediction step to the drug design step. While the experimental feedback usually provides binding affinities of tens of compounds at one time, the supercomputer allows us to simultaneously obtain the binding free energies of hundreds of compounds. Because the number of calculated binding free energies is sufficiently large, the compounds can be classified into different categories whose properties will aid in the design of the next generation of drug candidates. The second feedback, which occurs from the experiments to the MD simulations, is important to validate the simulation parameters. To demonstrate this, we compare the binding free energies calculated with various force fields to the experimental ones. The results indicate that the prediction will not be very successful, if we use an inaccurate force field. By improving/validating such simulation parameters, the next prediction can be made more accurate.

Structural features of interfacial tyrosine residue in ROBO1 fibronectin domain-antibody complex: Crystallographic, thermodynamic, and molecular dynamic analyses.

ROBO1, fibronectin Type-III domain (Fn)-containing protein, is a novel immunotherapeutic target for hepatocellular carcinoma in humans. The crystal structure of the antigen-binding fragment (Fab) of B2212A, the monoclonal antibody against the third Fn domain (Fn3) of ROBO1, was determined in pursuit of antibody drug for hepatocellular carcinoma. This effort was conducted in the presence or absence of the antigen, with the chemical features being investigated by determining the affinity of the antibody using molecular dynamics (MD) and thermodynamics. The structural comparison of B2212A Fab between the complex and the free form revealed that the interfacial Tyr(L) 50 (superscripts L, H, and F stand for the residues in the light chain, heavy chain, and Fn3, respectively) played important roles in Fn3 recognition. That is, the aromatic ring of Tyr(L) 50 pivoted toward Phe(F) 68, forming a CH/π interaction and a new hydrogen bond with the carbonyl O atom of Phe(F) 68. MD simulations predicted that the Tyr(L) 50-Phe(F) 68 interaction almost entirely dominated Fab-Fn3 binding, and Ala-substitution of Tyr(L) 50 led to a reduced binding of the resultant complex. On the contrary, isothermal titration calorimetry experiments underscored that Ala-substitution of Tyr(L) 50 caused an increase of the binding enthalpy between B2212A and Fn3, but importantly, it induced an increase of the binding entropy, resulting in a suppression of loss in the Gibbs free energy in total. These results suggest that mutation analysis considering the binding entropy as well as the binding enthalpy will aid in the development of novel antibody drugs for hepatocellular carcinoma.

Molecular dynamics simulation-based evaluation of the binding free energies of computationally designed drug candidates: importance of the dynamical effects.

The computational structure-based drug design (SBDD) mainly aims at generating or discovering new chemical compounds with sufficiently large binding free energy. In any de novo drug design methods and virtual screening methods, drug candidates are selected by approximately evaluating the binding free energy (or the binding affinity). This approximate binding free energy, usually called "empirical score," is critical to the success of the SBDD. The purpose of this work is to yield physical insight into the approximate evaluation method in comparison with an exact molecular dynamics (MD) simulation-based method (named MP-CAFEE), which can predict binding free energies accurately. We calculate the binding free energies for 58 selected drug candidates with MP-CAFEE. Here, the compounds are generated by OPMF, a novel fragment-based de novo drug design method, and the ligand-protein interaction energy is used as an empirical score. The results show that the correlation between the binding free energy and the interaction energy is not strong enough to clearly distinguish compounds with nM-affinity from those with µM-affinity. This implies that it is necessary to take into account the natural protein motion with explicitly surrounded by water molecules to improve the efficiency of the drug candidate selection procedure.

Docking study and binding free energy calculation of poly (ADP-ribose) polymerase inhibitors.

Recently, the massively parallel computation of absolute binding free energy with a well-equilibrated system (MP-CAFEE) has been developed. The present study aimed to determine whether the MP-CAFEE method is useful for drug discovery research. In the drug discovery process, it is important for computational chemists to predict the binding affinity accurately without detailed structural information for protein/ligand complex. We investigated the absolute binding free energies for Poly (ADP-ribose) polymerase-1 (PARP-1)/inhibitor complexes, using the MP-CAFEE method. Although each docking model was used as an input structure, it was found that the absolute binding free energies calculated by MP-CAFEE are well consistent with the experimental ones. The accuracy of this method is much higher than that using molecular mechanics Poisson-Boltzmann/surface area (MM/PBSA). Although the simulation time is quite extensive, the reliable predictor of binding free energies would be a useful tool for drug discovery projects.

Massively parallel computation of absolute binding free energy with well-equilibrated states.

A force field formulator for organic molecules (FF-FOM) was developed to assign bond, angle, and dihedral parameters to arbitrary organic molecules in a unified manner including proteins and nucleic acids. With the unified force field parametrization we performed massively parallel computations of absolute binding free energies for pharmaceutical target proteins and ligands. Compared with the previous calculation with the ff99 force field in the Amber simulation package (Amber99) and the ligand charges produced by the Austin Model 1 bond charge correction (AM1-BCC), the unified parametrization gave better absolute binding energies for the FK506 binding protein (FKBP) and ligand system. Our method is based on extensive work measurement between thermodynamic states to calculate the free energy difference and it is also the same as the traditional free energy perturbation. There are important requirements for accurate calculations. The first is a well-equilibrated bound structure including the conformational change of the protein induced by the binding of the ligand. The second requirement is the convergence of the work distribution with a sufficient number of trajectories and dense spacing of the coupling constant between the ligand and the rest of the system. Finally, the most important requirement is the force field parametrization.

High-Level ab Initio Calculations To Improve Protein Backbone Dihedral Parameters.

We present new molecular mechanical dihedral parameters for the Ramachandran angles ϕ and ψ of a protein backbone based on high-level ab initio molecular orbital calculations for hydrogen-blocked or methyl-blocked glycine and alanine dipeptides. Fully relaxed 15° (ϕ, ψ) contour maps were calculated at the MP2/6-31G(d) level of theory. Finding out the lowest energy path for ϕ (or ψ) to change from -180° to 180° in the contour map, we performed a DF-LCCSD(T0)/Aug-cc-pVTZ//DF-LMP2/Aug-cc-pVTZ level calculation to get the torsional energy profiles of ϕ (or ψ). Molecular mechanical torsion profiles with AMBER force field variants significantly differed from the ab initio profiles, so we derived new molecular mechanical dihedral parameters of a protein backbone to fit the ab initio profiles.

Direct calculation of the binding free energies of FKBP ligands.

Direct calculations of the absolute free energies of binding for eight ligands to FKBP protein were performed using the Fujitsu BioServer massively parallel computer. Using the latest version of the general assisted model building with energy refinement (AMBER) force field for ligand model parameters and the Bennett acceptance ratio for computing free-energy differences, we obtained an excellent linear fit between the calculated and experimental binding free energies. The rms error from a linear fit is 0.4 kcal/mol for eight ligand complexes. In comparison with a previous study of the binding energies of these same eight ligand complexes, these results suggest that the use of improved model parameters can lead to more predictive binding estimates, and that these estimates can be obtained with significantly less computer time than previously thought. These findings make such direct methods more attractive for use in rational drug design.