Biology subjects

Fukuda, I.

Publications and source records attributed to Fukuda, I..

5 recordsLinked to original sources

bioRxiv2023

Binding free-energy landscapes of small molecule binder and non-binder to FMN riboswitch: All-atom molecular dynamics

Binding of a small and flexible molecule, ribocil A (non-binder) or B (binder), to the deep pocket of the aptamer domain of the FMN riboswitch was studied by mD-VcMD, which is a generalized-ensemble method based on molecular dynamics (MD) simulation. Ribocil A and B are structurally similar because they are optical isomers mutually. In the initial conformation of simulation, both ligands and the aptamer were completely dissociated in explicit solvent. The resultant free-energy landscape of ribocil B binding to the aptamer was funnel-like, whereas that of ribocil A was rugged, which agrees qualitatively with an experiment. When entering the gate (named "front gate") of the pocket, the ligand interacted with the aptamer by native and non-native {pi}-{pi} stackings, and the stackings restrained the molecular orientation of the ligands to be advantageous to reach the binding site smoothly. The simulation showed another pathway, which also led the ligands to the binding site. Its gate (maned "rear gate") located completely opposite to the front gate on the aptamers surface. However, approach from the rear gate required overcoming a free-energy barrier before reaching the binding site, and the ligands should rotate largely and sharply at the free-energy barrier. This ligands orientation property is discussed referring to a ligand orientation selection mechanism exserted by a membrane protein capturing its ligand.

biophysics↗

bioRxiv2022

Fly-casting with ligand-sliding and orientational selection to support the complex formation of a GPCR and a middle-sized flexible molecule

To elucidate computationally a binding mechanism of a middle-sized flexible molecule, bosentan, to a GPCR protein, human endothelin receptor type B (hETB), a GA-guided multidimensional virtual-system coupled molecular dynamics (GA-mD-VcMD) simulation was performed. This method is one of generalized ensemble methods and produces a free-energy landscape of the ligand-receptor binding by searching large-scale motions accompanied with stably keeping the fragile cell-membrane structure. All molecular components (bosentan, hETB, membrane, and solvent) were represented with an all-atom model, and sampling was carried out from conformations where bosentan was distant from the binding site in the hETBs binding pocket. The deepest basin in the resultant free-energy landscape was assigned to the native-like complex conformation. The obtained binding mechanism is as follows. First, bosentan fluctuating randomly in solution is captured by a tip region of the flexible N-terminal tail of hETB via nonspecific attractive interactions (fly-casting). Bosentan then occasionally slides from the tip to root of the N-terminal tail (ligand-sliding). In this sliding, bosentan passes the gate of the binding pocket from outside to inside of the pocket with accompanying a quick reduction of the molecular orientational variety of bosentan (orientational selection). Last, in the pocket, ligand-receptor attractive native contacts are formed, and eventually the native-like complex is completed. The bosentan-captured conformations by the tip- and root-regions of the N-terminal tail correspond to two basins in the free-energy landscape, and the ligand-sliding corresponds to overcoming a free-energy barrier between the basins.

biophysics↗

bioRxiv2020

Molecular interaction mechanism of 14-3-3ε protein with phosphorylated Myeloid leukemia factor 1 revealed by an enhanced conformational sampling

Enhanced conformational sampling, a genetic-algorithm-guided multi-dimensional virtual-system coupled molecular dynamics, can provide equilibrated conformational distributions of a receptor protein and a flexible ligand at room temperature. The distributions provide not only the most stable but also semi-stable complex structures, and propose a ligand-receptor binding process. This method was applied to a system consisting of a receptor protein, 14-3-3{varepsilon}, and a flexible peptide, phosphorylated Myeloid leukemia factor 1 (pMLF1). The results present comprehensive binding pathways of pMLF1 to 14-3-3{varepsilon}. We identified four thermodynamically stable clusters of MLF1 on the 14-3-3{varepsilon} surface, and free-energy barriers among some clusters. The most stable cluster includes two high-density spots connected by a narrow corridor. When pMLF1 passes the corridor, a salt-bridge relay (switching) related to the phosphorylated residue of pMLF1 occurs. Conformations in one high-density spots are similar to the experimentally determined complex structure. Three-dimensional distributions of residues in the intermolecular interface rationally explain the binding-constant changes resultant from alanine-mutation experiment for the residues. We performed a simulation of non-phosphorylated peptide and 14-3-3{varepsilon}, which demonstrated that the complex structure was unstable, suggesting that phosphorylation of the peptide is crucially important for binding to 14-3-3{varepsilon}.

biophysics↗

bioRxiv2019

Temperature-Energy-space Sampling Molecular Dynamics: Deterministic, Iteration-free, and Single-replica Method utilizing Continuous Temperature System

We developed coupled Nose-Hoover (NH) molecular dynamics equations of motion (EOM), wherein the heat-bath temperature for the physical system (PS) fluctuates according to an arbitrary predetermined weight. The coupled NH is defined by suitably jointing the NH EOM of the PS and the NH EOM of the temperature system (TS), where the inverse heat-bath temperature {beta} is a dynamical variable. In this study, we define a method to determine the effective weight for enhanced sampling of the PS states. The method, based on ergodic theory, is reliable, and eliminates the need for time-consuming iterative procedures and resource-consuming replica systems. The resulting TS potential in a two dimensional ({beta}, {epsilon})-space forms a valley, and the potential minimum path forms a river flowing through the valley. {beta} oscillates around the potential minima for each energy{epsilon} , and the motion of {beta} derives a motion of{epsilon} and receives the{epsilon} s feedback, which leads to a mutual boost effect. Thus, it also provides a specific dynamical mechanism to explain the features of enhanced sampling such that the temperature-space \"random walk\" enhances the energy-space \"random walk.\" Surprisingly, these mutual dynamics between {beta} and{epsilon} naturally arise from the static probability theory formalism of double density dynamics that was previously developed, where the Liouville equation with an arbitrarily given probability density function is the fundamental polestar. Numerical examples using a model system and an explicitly solvated protein system verify the reliability, simplicity, and superiority of the method.

biophysics↗

bioRxiv2017

Symmetric, explicit numerical integrator for molecular dynamics equations of motion with a generalized friction

A general mathematical scheme to construct symmetric, explicit numerical integrators of Newtonian equations of motion endowed with a generalized friction is provided for molecular dynamics (MD) study. The exact integrations are done for all the decomposed vector fields, including the one that contains the friction term. On the basis of the symmetric composition scheme with the adjoint for the resulting maps, integrators with any local order of accuracy can be systematically constructed. Among them, the second order P2S1 integrator gives the least evaluation of atomic force and potential, which are most time consuming in MD simulations. As examples of the friction function, three functional types are considered: constant, Laurent polynomial, and exponential with respect to the kinetic energy. Several MD equations of motion fall into these categories, and the numerical examinations of their integrators using a basic model system give positive results on the accuracy and efficiency. The extended phase-space scheme also presents an invariant function, which allows to easily detect numerical errors in the integration process by monitoring the function value.

biophysics↗