2019, Vol.92, No.9

The structure prediction of Dictyostelium Histidine Kinase M (DhkM), a candidate for the receptor of differentiation inducing factor-1 (DIF-1), was carried out and the structural determination and refinement were performed with one hundred nanoseconds molecular dynamics (MD) simulations. Three simulations with different initial velocities generated by pseudo-random number seeds were performed to ensure the accuracy of our simulations and almost the same results were obtained. Docking simulations were executed employing the receptor-structures from the very early stage of the MD simulations. The obtained result exhibited that there is a very high possibility that DhkM could be the receptor of DIF-1. The residues in the core part which are adjacent to Leu111, Asp115, Arg150, Val151, Tyr351 and Val354 play a significant role in ligand binding mechanism. Furthermore, the binding energy was also estimated from free energy analysis for verification. The docking simulations for DIF-2 and DIF-3, which have molecular structures similar to DIF-1 have been explored as well. DIF-3 was especially found to have very low capacity of binding/no docking-simulation which was consistent with experimental data, and two chloro groups of DIF-1 could lead to a transition to a multicellular slug.

Dictyostelium discoideum is a species of soil-living amoeba able to transition from a collection of unicellular amoebae into a multicellular slug, and moreover into a fruiting body at the onset of starvation. D. discoideum has been commonly used as a model organism to study genetic, cellular, and biochemical processes in other organisms. Genome-sequence analysis of D. discoideum was completed in 2005 and forty-five polyketide synthase (PKS) genes have been reported.1 PKS SteelyB has a very unique structure which contains a type I PKS domain combined with a type III PKS domain.2 SteelyB employs acetyle-CoA as the starting material to synthesize phlorocaprophenone (PCP) (Figure 1) and finally a chlorinated compound differentiation inducing factor-1 (DIF-1) could be composed,2 which has been studied as the inducer of stalk differentiation in D. discoideum.3,4

Unfortunately, the complete inducing process is still unknown after about 40 years of study, and many research groups have contributed in a major way to search for the specific DIF-1 receptor using either mutation or protein purification. For example in 1990 a study was conducted identifying the receptor for DIF-1 using radioactive ligands and spun column by Insall & Kay,5 and also an experimental measurement was performed by affinity chromatography in 2016 by Kuwayama et al.6 However it seemed a tough problem for these experimental approaches to determine the observed process. The receptor for DIF-1 could not be identified among the candidates,5 and the association between DIF-1 and the proteins was not proven.6 There has been no single-crystal X-ray diffraction reported for the receptor of DIF-1. During the process of stalk differentiation of D. discoideum, cell vacuolization occurs in prestalk cells and until they are genetically dead.7 This could be considered to be a kind of autophagic cell death induced by DIF-1 and it has been reported that the receptor Dictyostelium histidine kinase M (DhkM) is required in the autophagic cell death.8 Thus, DhkM has been chosen as the most likely candidate for the receptor of DIF-1. To actively cooperate in the measurement, an in silico approach has been employed to explore the receptor in the present work.

Since DhkM is a huge protein which is composed of 2388 amino-acid residues, and it is well known that the core part of DhkM (residue number 1073–1498) contains a histidine kinase domain, and also locates in the downstream transmembrane domains and the upstream response regulatory domains, we first focused on the core part of DhkM (residue number 1073–1498) in the present work; our structure prediction has started on the comparative modeling approach,9 with the knowledge of the amino-acid sequence because no information on the crystal structure was available. The determination of initial structure for docking simulations and energy analysis have been performed with molecular dynamics (MD) simulations. In order to reveal the relation between DIF-1 and DhkM, docking simulations and free energy evaluation based on molecular mechanics Poisson-Boltzmann Surface Area (MMPBSA) Method10 have been performed.

In the present study, the protein-structure prediction was first employed by SWISS-MODEL11 and furthermore molecular dynamics (MD) simulations by AMBER16.12 SYBYL-X 1.313 were performed to analyze the docking. Theory and methodology for the stability and binding free energy calculations were based on the molecular mechanics Poisson–Boltzmann surface area method10 (with MMPBSA.py14). The main equation for calculating the free energy in the process of a biomolecule system changing conformations from unbound state to bound state is according to

\begin{align*} \Delta \text{G}_{\text{binding,solvated}} &= \Delta \text{G}_{\text{complex,solvated}} - [\Delta \text{G}_{\text{receptor,solvated}} \\&\quad+ \Delta \text{G}_{\text{ligand,solvated}}] \end{align*}

The change of free energy associated with terms on the right-hand side could be estimated by

\begin{equation*} \Delta \text{G}_{\text{solvated}} = \text{E}_{\text{gas}} + \Delta \text{G}_{\text{solvation}} - TS_{\text{solute}} \end{equation*}

ΔGsolvation are calculated using an implicit solvent model and the gas-phase energies (Egas) are equal to the molecular mechanical (MM) energies from the force field.

The DhkM amino-acid sequence was obtained from DictyBase15 in fasta format and the information of protein domains and predicted functions were obtained from UniProt database16 (UniProtKB-Q54SK5). Based on the amino-acid sequence and information of protein domains, the structure prediction for the core part of DhkM (residue number 1073–1498) has been obtained with SWISS-MODEL.11 Since it is well known that a part of the sequence from DhkM was defined as a histidine domain,11,15,16 another histidine kinase 4JAU.1.A17 which contains one histidine kinase domain and response regulator similar to DhkM, was used as the template. The determination of initial structure for docking simulations and energy analysis have has been performed by the molecular dynamics (MD) simulations which was discussed in the next section.

In order to refine the artificial structure except the core, our MD simulations starting with the structure obtained with SWISS-MODEL were performed with AMBER16 using the AMBER force field ff14SB. The force field of DIF-1 was determined by the density functional theory (DFT) at B3LYP/cc-pVDZ level with the Merz-Singh-Kollman (MK) analysis. All ab initio calculation was carried out with the Gaussian 09 package.18 The whole system for the MD simulation was neutralized by adding Na+. The MD simulations for the equilibration and relaxing the protein structure has been performed very cautiously with a time step of 0.1 fs at T = 300 K, to make sure that reasonable structures could be obtained, although the time step of 1.0 fs is usually used in such a long simulation more than 10 ns. Subsequently the production runs of MD simulations at 300 K were executed for 100 ns. The time step of 0.2 fs has been used and three simulations with different initial velocities generated by pseudo-random number seeds have been performed to ensure the accuracy of our results. Larger time step simulations were also tried at first, but it seemed that performing MD simulations cautiously with small time step would be necessary for the large systems which contain active binding sites in an unstable state. The Langevin thermostat was used to maintain the temperature of the system, because it is significantly more efficient at equilibrating the system temperature than the Berendsen temperature coupling scheme. MD simulations were employed not in aqueous solution, but in vacuum. Of course a version with water solution has been created. DhkM was solvated by TIP3P water. As a result, the initial structure of DhkM contained 297829 residues/900186 atoms in it. A huge system was obtained. Thus, MD simulations were carried out without aqueous solution, due to the computational cost. The change of the protein structure is visualized with VMD All simulations were performed on our own computer system, using the Intel Xeon E5-2680(16 Cores 2 CPUs at 2.7 GHz/128 Gbytes RAM/NVIDIA TESLA K40C) (TS3DR2-E5(27)L, Concurrent Systems). The total computational time for each simulation was 4 months, but it actually took one year to compute the three different simulations.

In the present work, SiteID and Surflex-Dock20 packages are used to reveal the relation between DIF-1 and DhkM. Protomols were generated by Surflex-Dock at the threshold of 0.50. The binding properties were analyzed with SYBYL.13 Total score was expressed in terms of −log (Kd) units to evaluate the docking results, where Kd represents a dissociation constant.21 The scoring function includes hydrophobic, polar, repulsive, entropic, solvation and crash terms.20,21 Hydrophobic term characterizes interactions between nonpolar atoms. Crash score is the degree of inappropriate penetration into the protein by the ligand and the interpenetration between ligand atoms separated by rotatable bonds. Polar score shows the contribution of the polar interactions to the total score. Repulsive term measures the penalty for placing atoms of similar polarity in close proximity and is scaled by direction. Entropic term captures the degrees of rotational and translational freedom lost to the ligand upon binding. Solvation terms are linearly related to a count of the number of missed opportunities for appropriate polar contacts at the ligand/protein interface. The full scoring function is the sum of these terms over the appropriate atom pairs, that the nearest surface distance is less than 2.0 Å.

The most important factors consisting of the total score, the crash value and the size of pockets were employed to evaluate the binding models. Further analysis for binding models showing scores corresponding to 3–8 for the binding system have been carried out, in terms of the structural arrangement and hydrophilic interaction or hydrophobic effect between DhkM and DIF-1. The stability of the binding model was confirmed by estimating the free energy ΔG.

6.1 Amino-Acid Sequence Alignment and Structure Predicted by SWISS-MODEL.

DhkM amino-acid sequence obtained in DictyBase contains 2388 amino acids. It also contains seven transmembrane domains (residue number 550–570, 589–609, 645–665, 679–699, 896–916, 953–973, and 1025–1045), and two response regulatory domains (residue number 1541–1656 and 2262–2383) and the binding site was considered to locate between these two regions (residue number 1046–1540) from a biological point of view. The functional residues in this core part (residue number 1073–1498) which kept very high identities was just between the transmembrane domain and the response regulatory domain which was recognized as a histidine kinase domain.11,15,16 Therefore, the region of residue number 1073–1498 was considered as the core part of DhkM which includes the binding site for ligand.

The similarity between DhkM and the template was confirmed by Clustal Omega.22 Histidine kinase 4JAU.1.A17 was used as the template for the structure prediction. Fifty seven identical positions and eighty six similar positions were defined among two hundred and thirty eight residues, which were applied to predict the structure of histidine kinase domain in the core part. In particular, the arrangement for the eighty six residues reached the characteristic arrangement of the histidine kinase domain by the template. These residues were supposed to keep the properties of histidine kinase. From this result, the histidine kinase domain contained in the core, could take a tertiary structure similar to the histidine kinase domain in the template with reliability, because of the conservation of the same domains. Therefore, the employed template in the present work should have reliability provided by the experimental measurement. Of course, the completely randomized structure could be started using the random seeds, and moreover the reasonable structure would be determined by replica-exchange approach. But some reliable data obtained by the experimental measurement has been already known, which could also provide a reasonable structure. The predicted structure by SWISS-MODEL11 is shown in Figure 2. As seen in the figure, the predicted structure of DhkM contains a long stretching chain except the core. Then the determination of initial structure for docking simulations and energy analysis could be performed by the MD simulations.

6.2 Molecular Dynamics Simulation and Docking Simulation.

The determination of initial structure of the core part of DhkM and the structural refinement were carried out by the production MD-simulation of one hundred nanoseconds and the final structure is shown in Figure 3. The time-profile of root mean square deviation (RMSD) for the early stage of the MD simulation, which is considered to be the most important part in this case, has also been calculated to verify the structural convergence. RMSD was calculated for the backbone: all carbon, alpha carbon and nitrogen atoms, of the core part of DhkM. Best-fit RMSD was performed, in which each structure is rotated and translated so as to minimize the RMSD to the reference structure. In this case, the first frame was set to be the reference. The result is shown in Figure 4, and it can be seen that the structure of protein changed as the simulation underwent the long-time-step MD simulation, the folding structure appeared gradually and was used for docking simulations with DIF-1. Docking scores were obtained at several different time steps.

Since DhkM is a kind of histidine kinase which contains an ATP-binding site and the function is to induce the cell death, the hypothesis is as follows;

i) From a biological point of view, the receptor in a stable state should be inactive because the activation costs cell death in nature.

ii) Once the cell-death process is required, ATP-binding process provides the necessary energy for receptor-activation while the receptor should be inactive without ATP-binding in a stable state.

Therefore, it is supposed that the structures which contain activated binding sites can only be observed at the very early stage of the MD simulations in a comparatively unstable state. Furthermore, the activation and inactivation processes would be controlled by the structural changes of the binding site, which could be expressed in characteristic changes of the RMSD value.

As predicted, unnatural protein structure with an incompact chain disappeared at around 3–4 ns (Figure 5a) the folding structure including six binding pockets emerged (Figure 5b), which involved high docking scores in binding simulation as listed in Table 1.

Table 1. Docking properties obtained by Surflex-Dock. The employed structure was produced by the MD simulation around 3,200 ps. Total score was expressed in terms of −log (Kd) units to evaluate the docking results.
Table 1. Docking properties obtained by Surflex-Dock. The employed structure was produced by the MD simulation around 3,200 ps. Total score was expressed in terms of −log (Kd) units to evaluate the docking results.
Pocket Solvent Spheres Total Score Crash Polar
RED 78 −3.7091 −10.9844 1.9707
CYAN 18 4.7601 −2.2194 2.1884
WHITE 13 −2.4776 −11.9981 1.1847
GREEN 11 0.7012 −0.3084 2.5410
MAGENTA 10 3.5641 −1.3359 1.0976
YELLOW 10 3.1037 −0.5994 0.7463

In case of docking simulation for DIF-1, a higher total score, lower crash value, and appropriate pocket size would be desirable. CYAN, MAGENTA and YELLOW pockets clearly show the potential binding sites for DIF-1 as the result of the comprehensive evaluations.

6.3 Analysis of the Docking Simulation Results.

Further analysis for binding models which showed high scores have been carried out in terms of structural arrangement and hydrophilic interaction or hydrophobic effect between DhkM and DIF-1. The stability of the binding model was confirmed by calculating the binding energy with the MD simulation.

6.3.1 Structural Arrangement Analysis:

Hydrophilic or hydrophobic properties were mapped by lipophilic potential with the MOLCAD option of SYBYL13 and are shown in Figure 6. In the bar of the figures, red tendency shows high lipophilic potential and blue tendency shows low lipophilic potential. The result for pocket CYAN is shown in Figure 6a. The ligand and receptor fit each other quite well in the structure. The side chain of DIF-1 is located just inside the pocket like a key and there are six hydrogen bonds formed between DIF-1 and the binding site. It is well known that there is a trend in which hydrogen bond decides the binding strength between ligand and receptor. The six hydrogen bonds appearing in CYAN pocket would ensure the stability of the system. An analysis was also performed on the molecular surface which was determined by the Connolly surface. The side chain of DIF-1 and binding site fit each other so well that pocket CYAN was filled up with the side chain of DIF-1. Thus, the pocket CYAN could be an appropriate binding site for DIF-1.

The result for pocket MAGENTA is shown in Figure 6b. The ligand and receptor did not fit each other quite as well in this structure. The side chain of DIF-1 seems to be pulled toward the pocket but is mostly located outside it. Since there is only a single hydrogen bond formed, the interaction is expected to be quite weak. A remarkable point is that the shape of ligand DIF-1 is quite warped which means the molecule had to take such an unstable structure in order to bind with the site. Subsequently as a result of analysis of the molecular surface, the side chain of DIF-1 did not fit the binding site as well as in the case of pocket CYAN. Thus, it is considered that pocket MAGENTA could not be an appropriate binding site for DIF-1.

Pocket YELLOW showed almost the same result as pocket MAGENTA and is shown in Figure 6c; it is considered to not be an appropriate binding site for DIF-1 either. Generally, pocket CYAN (LEU111 ASP115 ARG150 VAL151 TYR351 VAL354) shows the best result among these three potential binding sites and a further analysis from the point of view of hydrophilic interaction and hydrophobic effect has been carried out.

6.3.2 Analysis of Hydrophilic Interaction and Hydrophobic Effect:

It has been reported that the presence of halogen atoms in a molecule increases its lipophilicity and hydrophobicity.23 The structure of ligand molecule DIF-1 was optimized by ab initio calculation and it contains two lipophilic areas (around chlorine, Cl), one hydrophilic area (around oxygen, O) and one side chain which appeared slightly lipophilic (Figure 7a). On the other hand, there were two lipophilic areas outside the pocket, one hydrophilic area at the entrance of the pocket and a slightly lipophilic region inside the pocket (Figure 7b.i). As shown in Figure 7b.ii, the lipophilic and hydrophilic areas of DIF-1 and pocket CYAN matched each other well in the structure; therefore the binding model of DIF-1 and pocket CYAN is expected to be stable because of the hydrophilic interaction and hydrophobic effect and it can be argued that the binding model on pocket CYAN is suitable.

6.4 Free Energy Analysis.

Our goal is to verify whether the whole system becomes more stable or not after binding, and one of the approaches is to evaluate the stability of the binding model. The binding energy analysis is done based on free energy ΔG obtained by the molecular mechanics Poisson–Boltzmann surface area (MMPBSA) method.10 MMPBSA typically gives excessively large energies but is shown by Genheden and Ryde to be useful for post-processing of docked structures or to rationalize observed differences.24 In the PB/SA part, ionic strength (in mM) for the PB equation was set to be 0.200, and the modern nonpolar solvent model was applied. The time interval of the frame was 2 ps. The binding energy was estimated to be −11.09 kcal/mol. As a result, the whole system is found to be quite stable after binding and pocket CYAN in DhkM could be an appropriate and suitable binding site for DIF-1.

6.5 Intermediate State of the Activated Pocket CYAN.

Based on the RMSD curve, the characteristic structure changes were expected to occur at 21–23 ns during the MD simulation up to 100 ns. However, an interval of twenty nanoseconds is said to be long enough for the structure change. The spatial area and the configuration of the pocket constituted by almost the same residues with pocket CYAN at 3 ns can still be observed at 4–21 ns but closes gradually. The structures around 22 ns were also investigated in the present work (Figure 8a), and the pocket was actually closed and the ligand could not get in and bind to the site in the docking simulations (Figure 8b). At 62 ns, the pocket CYAN could not be observed anymore and the protein was expected to be completely inactive.

It is supposed that the structures for activated binding sites could be observed at an early stage of the MD simulations. Therefore, our results show that the pocket containing the binding site could only be activated; the emergence as the system is being stabilized is thought to be quite reasonable for the binding process.

6.6 Mechanism of the Binding Process.

The prediction of the mechanism of the binding process is based on the result of docking simulations. Two lipophilic areas outside the pocket, one hydrophilic area at the entrance of the pocket and a lipophilic region inside the pocket are observed in our simulation. It is considered that the lipophilic areas play the important role of ligand-recognition and translocation which agrees with the results in drug design research.25,26 On the other hand the hydrophilic site at the entrance of the pocket and a lipophilic region inside the pocket make the system stable and have relevance to the function of the receptor. The verification is performed using the molecule DIF-2 and DIF-3, shown in Figure 9, which have a similar molecular structure to DIF-1.

DIF-2 contains a shorter alkyl chain structure with a butyl group than DIF-1 having a pentyl group, and DIF-3 takes the structure of monochloro-DIF-1. In our prediction, since DIF-2 contains a shorter alkyl chain structure than DIF-1, DIF-2 could be recognized and bind to DhkM with a lower stability than DIF-1 which may lead to a lower bioactivity. On the other hand, in contrast to the dichloride structure, lipophilic area is lost in DIF-3 decreasing the lipophilicity of the molecule. As a result, DIF-3 could not be normally recognized and the binding process would occur with a low probability.

Docking simulations of DIF-2 and DIF-3 have also been carried out under the same conditions as DIF-1. Results for the three MD simulation trajectories of DIF-1 and DIF-2 are summarized in Table 2. DIF-2 showed lower score than DIF-1 such as 3.9492 in trajectory 1, but it was still able to constitute a well binding model. The ligand and receptor also match each other quite well in molecular structure (Figure 10). DIF-3 could bind to the protein in the docking simulation and the score was 3.2542 in trajectory 1. But as shown in Figure 11, DIF-3 is unrealistically warped in order to bind with the site, which is not observed in the case of DIF-1 and DIF-2. The respective bond-length of the C1-C2 and C1-O bonds were 0.35135 and 4.27622 Å; the ring structure of benzene was extremely distorted with the dissociation of C-OH bond. In general, the biosystem should keep the ring structure of benzene, which means DIF-3 is quite unrealistic as a result of docking simulation. Note that this is the result given by docking simulation by SYBYL, which means no molecular dynamics. The protein-structure keeps the shape in docking simulation, while the ligand only can change the shape. However, we could analyze some properties using the Sybyl package. Each Cl atom of DIF-1 and DIf-2 was contained in the receptor-binding site and affects the binding process as a necessary factor. Because of the lack of hydrophobic interaction of chlorine (when performing the docking simulation of DIF-3), the obtained docking model seems to form hydrogen bonds in order to maximize the score. As a result of steric effects, and the bonds of the DIF-3 molecule extending or shrinking to bind with the binding-pocket of the protein, the structure is unrealistically warped.

Table 2. Summary of the results for the docking simulations of DIF-1 and DIF-2
Table 2. Summary of the results for the docking simulations of DIF-1 and DIF-2
  DIF-1 DIF-2
Trajectory Total Score Crash Total Score Crash
1 4.7601 −2.2194 3.9492 −1.1127
2 4.9485 −1.4977 4.0668 −2.4695
3 3.1823 −0.8192 2.9645 −0.5301

On the other hand, the binding energies of DIF-2 and DIF-3 have been also calculated with MMPBSA as DIF-1. The energy was estimated as 0.5856 kcal/mol and 2.035 kcal/mol. As a result, we could remarkably see that DIF-1&DhkM (−11.09 kcal/mol) is a more favorable complex.

The reason for lower scores of DIF-2 and DIF-3 than DIF-1 is thought to be:


DIF-2 contains a shorter alkyl chain structure with a butyl group than DIF-1 having a pentyl group, causing the configuration changes of the ligand due to hydrophobic cost, in order to bind with the receptor during the docking simulation. It made the ligand form hydrogen bonds with the receptor in a slightly different manner than DIF-1. As a result, the number of hydrogen bonds and the interaction decreased between ligand and receptor. Significantly, it also makes the well-fitted keyhole-lock-key model of DIF-1&DhkM contain some extra space. On the another hand, the hydrophobic interaction still remained in DIF-2 causing the score to decrease but not so much.


DIF-3 takes the structure of monochloro-DIF-1 and the lack of hydrophobic interaction would make the model become unstable. The less stable the whole system, the lower the docking score obtained. Thus, the docking score of DIF-3 decreased more than DIF-2. And as described above, the score of DIF-3 is not suited for further analysis.

Furthermore, it has been reported that DIF-2 possessed 40% of the activity of DIF-1 and the monochloro-DIF-1 is much less active than DIF-1.27 And our speculation and the result of docking simulations agree very well with the experimental data27 as summarized in Table 3, which shows our binding model and proposed binding mechanism could be correct.

Table 3. Summary of the results for prediction, docking simulation and experimental data
Table 3. Summary of the results for prediction, docking simulation and experimental data
  DIF-1 DIF-2 DIF-3
Hydrophilic sites (Cl) 2 2 1
Hydrophilic sites (O) 1 1 1
Hydrophobic chain C6 C5 C6
Prediction for capacity of binding High Common Very Low
Docking simulation High Common No
In vitro experiment activity16 100% 42% <1%

6.7 Effects of DIF-1 Binding Process on Structure Folding of DhkM.

The folding mechanism of DhkM has also been studied based on the binding results. It is very interesting that whether the differences between the structures with and without ligand binding would occur or not. In our work, two MD simulations were performed (Figure 12). One is the simulation for DhkM only without DIF-1, another one is the simulation for DhkM bound with DIF-1. These two results were compared, and they folded in different tertiary structures at 90 ns. It is considered that ligand binding could obviously affect protein folding process, with a totally different tertiary structure appearing, and finally producing an unknown function. By referring to Figure 5a, the tertiary structure seems to keep the shape up to 90 ns if docking with the ligand, DIF-1.

Six pockets were observed appearing at around three nanoseconds in our simulation. One of the pockets, pocket CYAN, was found to be an appropriate binding site for DIF-1. It is proposed that lipophilic and hydrophilic sites play an important role in ligand-recognition and making the system stable. Hydrogen bonds are also necessary in this case and have relevance to the function of the receptor. It was also verified that binding site for DIF-1 could only be activated by the system being stabilized, which agrees with the properties of histidine kinase and the function of DhkM.

It is said that it is difficult to find the receptor for small molecular ligands such as DIF-1 because of the limitations of in vitro or in vivo experiments. But with our theoretical approach we have worked out a binding model and binding mechanism. It is possible for experimental groups to make mutants by operating the amino residues that have been shown to be necessary or important for the binding process in our result. This kind of approach is expected to provide a new way to search for the receptors of small molecular ligands.

Shinkoh Nanbu

Shinkoh Nanbu was born in Miyagi and completed his Doctor of Science in Chemistry at Keio University, Japan, in 1994, under the direction of Prof. Suehiro Iwata. From 1991 to 1992, he was a JSPS doctoral fellow at Keio University. In 1992, he became an Assistant Professor at Computer Centre of Institute for Molecular Science (IMS). He moved to Computer Centre of Kyushu University as an Associate Professor in 2005. Since 2009, he has been a full Professor, Faculty of Science & Technology, Sophia University, Tokyo, Japan. His current interests are photochemistry and biochemistry.