Tadaaki Mashimo

Molecular Dynamics Simulations Accelerated by GPU for Biological Macromolecules with a Non-Ewald Scheme for Electrostatic Interactions

A molecular dynamics (MD) simulation program for biological macromolecules was implemented with a non-Ewald scheme for long-ranged electrostatic interactions and run on a general purpose graphics processing unit (GPU). We recently developed several non-Ewald methods to compute the electrostatic energies with high precision. In particular, the zero-dipole summation (ZD) method, which takes into account the neutralities of charges and dipoles in a truncated subset, enables the calculation of electrostatic interactions with high accuracy and low computational cost, and its algorithm is simple enough to be implemented in a GPU. We developed an MD program with the space decomposition algorithm, myPresto/psygene, and applied it to several biological macromolecular systems with GPUs implementing the ZD method. Rapid computing performance with high accuracy was obtained.

Virtual‐system‐coupled adaptive umbrella sampling to compute free‐energy landscape for flexible molecular docking

A novel enhanced conformational sampling method, virtual‐system‐coupled adaptive umbrella sampling (V‐AUS), was proposed to compute 300‐K free‐energy landscape for flexible molecular docking, where a virtual degrees of freedom was introduced to control the sampling. This degree of freedom interacts with the biomolecular system. V‐AUS was applied to complex formation of two disordered amyloid‐β (Aβ30–35) peptides in a periodic box filled by an explicit solvent. An interpeptide distance was defined as the reaction coordinate, along which sampling was enhanced. A uniform conformational distribution was obtained covering a wide interpeptide distance ranging from the bound to unbound states. The 300‐K free‐energy landscape was characterized by thermodynamically stable basins of antiparallel and parallel β‐sheet complexes and some other complex forms. Helices were frequently observed, when the two peptides contacted loosely or fluctuated freely without interpeptide contacts. We observed that V‐AUS converged to uniform distribution more effectively than conventional AUS sampling did.

In Silico Fragment Screening by Replica Generation (FSRG) Method for Fragment-Based Drug Design

We developed a new in silico screening method, which is a structure-based virtual fragment screening with protein-compound docking. The structure-based in silico screening of small fragments is known to be difficult due to poor surface complementarity between protein surfaces and small compound (fragment) surfaces. In our method, several side chains were attached to the fragment in question to generate a set of replica molecules of different sizes. This chemical modification enabled us to select potentially active fragments more easily than basing the selection on the original form of the fragment. In addition, the Coulombic and hydrogen bonding interactions were ignored in the docking simulation to reduce the variety of chemical modifications. Namely, we focused on the sizes and the shapes of the side chains and could ignore the atomic charges and types of elements. This procedure was validated in the screenings of inhibitors of six target proteins using known active compounds, and the results revealed that our procedure was effective.

Variation of free-energy landscape of the p53 C-terminal domain induced by acetylation: Enhanced conformational sampling.

The C‐terminal domain (CTD) of tumor suppressor protein p53 is an intrinsically disordered region that binds to various partner proteins, where lysine of CTD is acetylated/nonacetylated and histidine neutralized/non‐neutralized. Because of the flexibility of the unbound CTD, a free‐energy landscape (FEL) is a useful quantity for determining its statistical properties. We conducted enhanced conformational sampling of CTD in the unbound state via virtual system coupled multicanonical molecular dynamics, in which the lysine was acetylated or nonacetylated and histidine was charged or neutralized. The fragments were expressed by an all‐atom model and were immersed in an explicit solvent. The acetylation and charge‐neutralization varied FEL greatly, which might be convenient to exert a hub property. The acetylation slightly enhanced alpha‐helix structures that are more compact than sheet/loop conformations. The charge‐neutralization produced hairpins. Additionally, circular dichroism experiments confirmed the computational results. We propose possible binding mechanisms of CTD to partners by investigating FEL.

Elastic properties of dynein motor domain obtained from all-atom molecular dynamics simulations

Dyneins are large microtubule motor proteins that convert ATP energy to mechanical power. High-resolution crystal structures of ADP-bound cytoplasmic dynein have revealed the organization of the motor domain, comprising the AAA+ ring, the linker, the stalk/strut and the C sequence. Recently, the ADP.vanadate-bound structure, which is similar to the ATP hydrolysis transition state, revealed how the structure of dynein changes upon ATP binding. Although both the ADP- and ATP-bound state structures have been resolved, the dynamic properties at the atomic level remain unclear. In this work, we built two models named ‘the ADP model’ and ‘the ATP model’, where ADP and ATP are bound to AAA1 in the AAA+ ring, respectively, to observe the initial procedure of the structural change from the unprimed to the primed state. We performed 200-ns molecular dynamics simulations for both models and compared their structures and dynamics. The motions of the stalk, consisting of a long coiled coil with a microtubule-binding domain, significantly differed between the two models. The elastic properties of the stalk were analyzed and compared with the experimental results.

Synthesis and biological comparison of enantiomers of mepenzolate bromide, a muscarinic receptor antagonist with bronchodilatory and anti-inflammatory activities

Chronic obstructive pulmonary disease (COPD) is characterized by abnormal inflammatory responses and airflow limitations. We recently proposed that the muscarinic antagonist mepenzolate bromide (mepenzolate) would be therapeutically effective against COPD due to its muscarinic receptor-dependent bronchodilatory activity as well as anti-inflammatory properties. Mepenzolate has an asymmetric carbon atom, thus providing us with the opportunity to synthesize both of its enantiomers ((R)- and (S)-mepenzolate) and to examine their biochemical and pharmacological activities. (R)- or (S)-mepenzolate was synthesized by condensation of benzilic acid with (R)- or (S)-alcohol, respectively, followed by quaternization of the tertiary amine. As predicted by computational simulation, a filter-binding assay in vitro revealed that (R)-mepenzolate showed a higher affinity for the muscarinic M3 receptor than (S)-mepenzolate. In vivo, the bronchodilatory activity of (R)-mepenzolate was superior to that of (S)-mepenzolate, whereas anti-inflammatory activity was indistinguishable between the two enantiomers. We confirmed that each mepenzolate maintained its original stereochemistry in the lung when administered intratracheally. These results suggest that (R)-mepenzolate may have superior properties to (S)-mepenzolate as a drug to treat COPD patients given that the former has more potent bronchodilatory activity than the latter.

Miscellaneous Topics in Computer-Aided Drug Design: Synthetic Accessibility and GPU Computing, and Other Topics.

Abstract: Background Computer-aided drug design is still a state-of-the-art process in medicinal chemistry, and the main topics in this field have been extensively studied and well reviewed. These topics include compound databases, ligand-binding pocket prediction, protein-compound docking, virtual screening, target/off-target prediction, physical property prediction, molecular simulation and pharmacokinetics/pharmacodynamics (PK/PD) prediction. Message and Conclusion: However, there are also a number of secondary or miscellaneous topics that have been less well covered. For example, methods for synthesizing and predicting the synthetic accessibility (SA) of designed compounds are important in practical drug development, and hardware/software resources for performing the computations in computer-aided drug design are crucial. Cloud computing and general purpose graphics processing unit (GPGPU) computing have been used in virtual screening and molecular dynamics simulations. Not surprisingly, there is a growing demand for computer systems which combine these resources. In the present review, we summarize and discuss these various topics of drug design.

myPresto/omegagene: a GPU-accelerated molecular dynamics simulator tailored for enhanced conformational sampling methods with a non-Ewald electrostatic scheme

Molecular dynamics (MD) is a promising computational approach to investigate dynamical behavior of molecular systems at the atomic level. Here, we present a new MD simulation engine named “myPresto/omegagene” that is tailored for enhanced conformational sampling methods with a non-Ewald electrostatic potential scheme. Our enhanced conformational sampling methods, e.g., the virtual-system-coupled multi-canonical MD (V-McMD) method, replace a multi-process parallelized run with multiple independent runs to avoid inter-node communication overhead. In addition, adopting the non-Ewald-based zero-multipole summation method (ZMM) makes it possible to eliminate the Fourier space calculations altogether. The combination of these state-of-the-art techniques realizes efficient and accurate calculations of the conformational ensemble at an equilibrium state. By taking these advantages, myPresto/omegagene is specialized for the single process execution with Graphics Processing Unit (GPU). We performed benchmark simulations for the 20-mer peptide, Trp-cage, with explicit solvent. One of the most thermodynamically stable conformations generated by the V-McMD simulation is very similar to an experimentally solved native conformation. Furthermore, the computation speed is four-times faster than that of our previous simulation engine, myPresto/psygene-G. The new simulator, myPresto/omegagene, is freely available at the following URLs: http://www.protein.osaka-u.ac.jp/rcsfp/pi/omegagene/ and http://presto.protein.osaka-u.ac.jp/myPresto4/.

Quantitative analysis of aggregation-solubility relationship by in-silico solubility prediction

Aggregator (frequent hitter) compounds show non-selective binding activity against any target protein and must be removed from the compound library to reduce false positives in drug screening. A previous study suggested that aggregators show high hydrophobicity. The LogS values of aggregators and non-aggregators were estimated by the artificial neural network (ANN) model, the multi-linear regression (MLR) model, and the partial least squares regression (PLS) models, with the weighted learning (WL) method, and the results showed the same trend. The WL method is weighted on the data of the learning set molecules that are similar to the test molecule and improves the prediction accuracy. Bayesian analysis was applied, revealing a simple relationship between aggregation and solubility. Namely, the molecules with LogS  −5 were non-aggregators. In contrast, most of the molecules with LogS  −5 were aggregators. We also made a simple look-up table of probability of aggregation depending on the molecular weight and the number of hetero-atoms.

Prediction of Protein−compound Binding Energies from Known Activity Data: Docking-score-based Method and its Applications

We used protein−compound docking simulations to develop a structure‐based quantitative structure−activity relationship (QSAR) model. The prediction model used docking scores as descriptors. The binding free energy was approximated by a weighted average of docking scores for multiple proteins. This approximation was based on a pharmacophore model of receptor pockets and compounds. The weights of the docking scores were restricted to small values to avoid unrealistic weights by a regularization term. Additional outlier elimination improved the results. We applied this method to two groups of targets. The first target was the kinase family. The cross‐validation results of 107 kinase proteins showed that the RMSE of predicted binding free energies was 1.1 kcal/mol. The second target was the matrix metalloproteinase (MMP) family, which has been difficult for docking programs. MMPs require metal‐binding groups in their inhibitor structures in many cases. A quantum effect contributes to the metal−ligand interaction. Despite this difficulty, the present method worked well for the MMPs. This method showed that the RMSE of predicted binding free energies was 1.1 kcal/mol. In comparison, with the original docking method the RMSE was 1.7 kcal/mol. The results suggest that the present QSAR model should be applied to general target proteins.