Hiroo Miyagawa

Charge Pairing of Headgroups in Phosphatidylcholine Membranes: A Molecular Dynamics Simulation Study

Molecular dynamics simulation of the hydrated dimyristoylphosphatidylcholine (DMPC) bilayer membrane in the liquid-crystalline phase was carried out for 5 ns to study the interaction among DMPC headgroups in the membrane/water interface region. The phosphatidylcholine headgroup contains a positively charged choline group and negatively charged phosphate and carbonyl groups, although it is a neutral molecule as a whole. Our previous study (Pasenkiewicz-Gierula, M., Y. Takaoka, H. Miyagawa, K. Kitamura, and A. Kusumi. 1997. J. Phys. Chem. 101:3677-3691) showed the formation of water cross-bridges between negatively charged groups in which a water molecule is simultaneously hydrogen bonded to two DMPC molecules. Water bridges link 76% of DMPC molecules in the membrane. In the present study we show that relatively stable charge associations (charge pairs) are formed between the positively and negatively charged groups of two DMPC molecules. Charge pairs link 93% of DMPC molecules in the membrane. Water bridges and charge pairs together form an extended network of interactions among DMPC headgroups linking 98% of all membrane phospholipids. The average lifetimes of DMPC-DMPC associations via charge pairs, water bridges and both, are at least 730, 1400, and over 1500 ps, respectively. However, these associations are dynamic states and they break and re-form several times during their lifetime.

Crystal structure and molecular dynamics simulation of ubiquitin-like domain of murine parkin

Parkin is the gene product identified as the major cause of autosomal recessive juvenile Parkinsonism (AR-JP). Parkin, a ubiquitin ligase E3, contains a unique ubiquitin-like domain in its N-terminus designated Uld which is assumed to be a interaction domain with the Rpn 10 subunit of 26S proteasome. To elucidate the structural and functional role of Uld in parkin at the atomic level, the X-ray crystal structure of murine Uld was determined and a molecular dynamics simulation of wild Uld and its five mutants (K27N, R33Q, R42P, K48A and V56E) identified from AR-JP patients was performed. Murine Uld consists of two alpha helices [Ile23-Arg33 (alpha1) and Val56-Gln57 (alpha2)] and five beta strands [Met1-Phe7 (beta1), Tyr11-Asp18 (beta2), Leu41-Phe45 (beta3), Lys48-Pro51 (beta4) and Ser65-Arg72 (beta5)] and its overall structure is essentially the same as that of human ubiquitin with a 1.22 A rmsd for the backbone atoms of residues 1-76; however, the sequential identity and similarity between both molecules are 32% and 63%, respectively. This close resemblance is due to the core structure built by same hydrogen bond formations between and within the backbone chains of alpha1 and beta1/2/5 secondary structure elements and by nearly the same hydrophobic interactions formed between the nonpolar amino acids of their secondary structures. The side chain NetaH of Lys27 on the alpha1 helix was crucial to the stabilization of the spatial orientations of beta3 and beta4 strands, possible binding region with Rpn 10 subunit, through three hydrogen bonds. The MD simulations showed the K27N and R33Q mutations increase the structural fluctuation of these beta strands including the alpha1 helix. Reversely, the V56E mutant restricted the spatial flexibility at the periphery of the short alpha2 helix by the interactions between the polar atoms of Glu56 and Ser19 residues. However, a large fluctuation of beta4 strand with respect to beta5 strand was induced in the R42P mutant, because of the impossibility of forming paired hydrogen bonds of Pro for Arg42 in wild Uld. The X-ray structure showed that the side chains of Asp39, Gln40 and Arg42 at the N-terminal periphery of beta3 strand protrude from the molecular surface of Uld and participate in hydrogen bonds with the polar residues of neighboring Ulds. Thus, the MD simulation suggests that the mutation substitution of Pro for Arg42 not only causes the large fluctuation of beta3 strand in the Uld but also leads to the loss of the ability of Uld to trap the Rpn 10 subunit. In contrast, the MD simulation of K48A mutant showed little influence on the beta3-beta4 loop structure, but a large fluctuation of Lys48 side chain, suggesting the importance of flexibility of this side chain for the interaction with the Rpn 10 subunit. The present results would be important in elucidating the impaired proteasomal binding mechanism of parkin in AR-JP.

Error evaluation in the design of a special-purpose processor that calculates nonbonded forces in molecular dynamics simulations

Special‐purpose parallel machines that are plugged into a workstation to accelerate molecular dynamics (MD) simulations are attracting a considerable amount of interest. These machines comprise scalable homogeneous multiprocessors for calculating nonbonded forces (Coulombic and van der Waals forces), which consume more than 99% of the central processing unit (CPU) time in standard MD simulations. Each processor element in the machine has a pipeline architecture to calculate the total nonbonded force exerted on a particle by all of the other particles using information regarding the coordinates, the electric charge, and the species of each particle broadcast by the host computer. The processor then sends the calculated force back to the host computer. This article addresses the precision of the calculated nonbonded forces in the design of a processor LSI with minimal complexity. The precision of the arithmetic inside the processor that is required to calculate forces for MD simulations using Verlets procedure was critically evaluated. Forward and backward error analysis, coupled with numerical MD experiments on one‐dimensional systems, was performed, and the following results were obtained: (1) Each element of the position vector which the processor receives from the host computer should have a precision of at least 25 bits; and (2) the pairwise forces should be calculated using floating point numbers with at least 29 bits of mantissa in the processor. Calculation of a pairwise force, which involves second‐order polynomial interpolation using a table‐driven algorithm, requires a key which contains a duplicate of at least 11 most significant bits of mantissa of the squared pairwise distance. The final result was that (3) the total force that acts on a particle, which is obtained by summing the forces exerted by all of the other particles, should be calculated using an accumulator that has a mantissa of at least 48 bits.

Importance of C-terminal flexible region of 4E-binding protein in binding with eukaryotic initiation factor 4E

Although the α‐helical Y(X)4Lϕ containing region of eIF4E‐binding protein (4EBP) is the major binding region with eukaryotic initiation factor 4E (eIF4E), the roles of its N‐ and C‐terminal regions in the binding are hardly known. To clarify the roles of these flexible regions in the interaction, the binding features of the sequentially N‐, C‐, or both‐terminal‐residue‐deleted 4EBP2 mutants were investigated by surface plasmon resonance (SPR) analysis. It was shown that the C‐terminal His74‐Glu89 sequence has an auxiliary, but indispensable, function in stabilizing the binding to eIF4E. The possible interaction with eIF4E was estimated by molecular dynamics simulation. This is the first report on the importance of the C‐terminal flexible region in the eIF4E‐binding regulation of 4EBP.

X-ray crystal structure determination and molecular dynamics simulation of prophospholipase A2 inhibited by amide-type substrate analogues.

X-ray crystal structures of bovine pancreas prophospholipase A2 (proPLA2) inhibited by two amide-type inhibitors, [(R)-2-dodecanoyl-amino-1-hexanolphosphocholine (DAHPc) and (R)-2-dodecanoylamino-1-hexanolphosphoglycol (DAHPg)], were determined to R = 0.208 and 0.215 using reflections with up to 2.1 A resolution, respectively. Both complex crystals lacked defined electron densities for the prosequence of the N-terminal and for a loop region consisting of residues 65-70, retaining the disordered feature observed in free proPLA2 despite stabilization due to complex formation. The polar and nonpolar moieties of the amide-type inhibitors were located in the calcium-binding pocket and in the N-terminal alpha-helical hydrophobic region of the enzyme, respectively. As for the amide group of the inhibitor, which is lacking in the true substrate, a strong hydrogen bond was formed between the NH of the inhibitor and the unprotonated N(delta1) atom of His-48, resulting in the tight binding of the inhibitor to proPLA2, as well as to PLA2. The 20-30 times more potent inhibitory activity of DAHPg than DAHPc toward PLA2 could be explained by hydrogen bond formation between the glycol OH of DAHPg and the carbonyl O of Asp-49. The seven residues of the N-terminal prosequence of proPLA2, though disordered, block the access of a water molecule to Ala-1 of PLA2 or change the hydrogen-bonding property of Ala-1 alpha-amino group, resulting in breakage of the water-mediated hydrogen-bond network which is commonly formed in PLA2. The results of molecular dynamics (MD) calculation in an aqueous solution at 300 K indicate that this, rather than the close contact between the prosequence and the residues 65-70 loop region, is the main reason why the latter region becomes flexible in proPLA2, compared with in PLA2.

Application of a high-performance, special-purpose computer, GRAPE-2A, to molecular dynamics

The special‐purpose computer GRAPE‐2A accelerates the calculation of pairwise interactions in many‐body systems. This computer is a back‐end processor connected to a host computer through a Versa Module Europe (VME) bus. GRAPE‐2A receives coordinates and other physical data for particles from the host and then calculates the pairwise interactions. The host then integrates an equation of motion by using these interactions. We did molecular dynamics simulations for two systems of liquid water: System 1 (1000 molecules), and System 2 (1728 molecules). The time spent for one step of molecular dynamics was 3.9 s (System l), and 10.2 s (System 2). The larger the molecular system, the higher the performance. The speed of GRAPE‐2A did not depend on the formula describing the pairwise interaction. The cost performance was about 20 times better than that of the fastest workstations available today, and GRAPE‐2A cost only

Large Scale Molecular Dynamics Simulation Using MD-Engine

22,000.

Hydrogen Bonding of Water to Phosphatidylcholine in the Membrane As Studied by a Molecular Dynamics Simulation: Location, Geometry, and Lipid-Lipid Bridging via Hydrogen-Bonded Water

Recently, demands for computing and designing large complex systems, such as proteins, biomembrain and so on, grow tremendously. For those systems, accurate calculation of non-bonding interactions becomes crucial to predict correct stable structures and thermodynamic properties in molecular dynamics(MD) simulations. However, for examples, truncation of Coulombic interaction must be taken very large and consequently calculation cost scales as O(N2) Special purpose molecular dynamics(MD) hardware accelerator, which is called the MD-Engine, has been used to overcome this difficulty and applied to several complex systems. The MD-Engine consists of custum prosessors that calculate pairwise potentials and forces in parallel. A user just has to send coordinate of atoms and force field parameters, then receive forces and potential energy. Our test calculations show good linear scaling with the system size.