2022-

2022

    • A modified Hamiltonian in FEP calculations for reducing the computational cost of electrostatic interactions

      Hiraku Oshima and Yuji Sugita
      The free-energy perturbation (FEP) method predicts relative and absolute free-energy changes of biomolecules in solvation and binding with other molecules. FEP is, therefore, one of the most essential tools in in-silico drug design. In conventional FEP, to smoothly connect two thermodynamic states, the potential energy is modified as a linear combination of the end state potential energies by introducing scaling factors. When the particle mesh Ewald is used for electrostatic calculations, conventional FEP requires two reciprocal-space calculations per time step, which largely decreases the computational performance. To overcome this problem, we propose a new FEP scheme by introducing a modified Hamiltonian instead of interpolation of the end state potential energies. The scheme introduces non-uniform scaling into the electrostatic potential as used in Replica-Exchange with Solute Tempering 2 (REST2) and does not require additional reciprocal-space calculations. We tested this modified Hamiltonian in FEP calculations in several biomolecular systems. In all cases, the calculated free-energy changes with the current scheme are in good agreement with those from conventional FEP. The modified Hamiltonian in FEP greatly improves the computational performance, which is particularly marked for large biomolecular systems whose reciprocal-space calculations are the major bottleneck of total computational time.
    • Practical protocols for efficient sampling of kinase-inhibitor binding pathways using two-dimensional replica-exchange molecular dynamics

      Ai Shinobu, Suyong Re, and Yuji Sugita
      Molecular dynamics (MD) simulations are increasingly used to study various biological processes such as protein folding, conformational changes, and ligand binding. These processes generally involve slow dynamics that occur on the millisecond or longer timescale, which are difficult to simulate by conventional atomistic MD. Recently, we applied a two-dimensional (2D) replica-exchange MD (REMD) method, which combines the generalized replica exchange with solute tempering (gREST) with the replica-exchange umbrella sampling (REUS) in kinase-inhibitor binding simulations, and successfully observed multiple ligand binding/unbinding events. To efficiently apply the gREST/REUS method to other kinase-inhibitor systems, we establish modified, practical protocols with non-trivial simulation parameter tuning. The current gREST/REUS simulation protocols are tested for three kinase-inhibitor systems: c-Src kinase with PP1, c-Src kinase with Dasatinib, and c-Abl kinase with Imatinib. We optimized the definition of kinase-ligand distance as a collective variable (CV), the solute temperatures in gREST, and replica distributions and umbrella forces in the REUS simulations. Also, the initial structures of each replica in the 2D replica space were prepared carefully by pulling each ligand from and toward the protein binding sites for keeping stable kinase conformations. These optimizations were carried out individually in multiple short MD simulations. The current gREST/REUS simulation protocol ensures good random walks in 2D replica spaces, which are required for enhanced sampling of inhibitor dynamics around a target kinase.
    • Computational analysis on the allostery of tryptophan synthase: relationship between α/β-ligand binding and distal domain closure

      Shingo Ito, Kiyoshi Yagi, and Yuji Sugita
      Tryptophan synthase (TRPS) is a bifunctional enzyme consisting of α and β-subunits and catalyzes the last two steps of l-tryptophan (L-Trp) biosynthesis, namely, cleavage of 3-indole-d-glycerol-3′-phosphate (IGP) into indole and glyceraldehyde-3-phosphate (G3P) in the α-subunit, and a pyridoxal phosphate (PLP)-dependent reaction of indole and l-serine (L-Ser) to produce L-Trp in the β-subunit. Importantly, the IGP binding at the α-subunit affects the β-subunit conformation and its ligand-binding affinity, which, in turn, enhances the enzymatic reaction at the α-subunit. The intersubunit communications in TRPS have been investigated extensively for decades because of the fundamental and pharmaceutical importance, while it is still difficult to answer how TRPS allostery is regulated at the atomic detail. Here, we investigate the allosteric regulation of TRPS by all-atom classical molecular dynamics (MD) simulations and analyze the potential of mean-force (PMF) along conformational changes of the α- and β-subunits. The present simulation has revealed a widely opened conformation of the β-subunit, which provides a pathway for L-Ser to enter into the β-active site. The IGP binding closes the α-subunit and induces a wide opening of the β-subunit, thereby enhancing the binding affinity of L-Ser to the β-subunit. Structural analyses have identified critical hydrogen bonds (HBs) at the interface of the two subunits (αG181-βS178, αP57-βR175, etc.) and HBs between the β-subunit (βT110 – βH115) and a complex of PLP and L-Ser (an α-aminoacrylate intermediate). The former HBs regulate the allosteric, β-subunit opening, whereas the latter HBs are essential for closing the β-subunit in a later step. The proposed mechanism for how the interdomain communication in TRPS is realized with ligand bindings is consistent with the previous experimental data, giving a general idea to interpret the allosteric regulations in multidomain proteins.
  • Implementation of residue-level coarse-grained models in GENESIS for large-scale molecular dynamics simulations

    Cheng Tan, Jaewoon Jung, Chigusa Kobayashi, Diego Ugarte La Torre, Shoji Takada, and Yuji Sugita
    Residue-level coarse-grained (CG) models have become one of the most popular tools in biomolecular simulations in the trade-off between modeling accuracy and computational efficiency. To investigate large-scale biological phenomena in molecular dynamics (MD) simulations with CG models, unified treatments of proteins and nucleic acids, as well as efficient parallel computations, are indispensable. In the GENESIS MD software, we implement several residue-level CG models, covering structure-based and context-based potentials for both well-folded biomolecules and intrinsically disordered regions. An amino acid residue in protein is represented as a single CG particle centered at the Cα atom position, while a nucleotide in RNA or DNA is modeled with three beads. Then, a single CG particle represents around ten heavy atoms in both proteins and nucleic acids. The input data in CG MD simulations are treated as GROMACS-style input files generated from a newly developed toolbox, GENESIS-CG-tool. To optimize the performance in CG MD simulations, we utilize multiple neighbor lists, each of which is attached to a different nonbonded interaction potential in the cell-linked list method. We found that random number generations for Gaussian distributions in the Langevin thermostat are one of the bottlenecks in CG MD simulations. Therefore, we parallelize the computations with message-passing-interface (MPI) to improve the performance on PC clusters or supercomputers. We simulate Herpes simplex virus (HSV) type 2 B-capsid and chromatin models containing more than 1,000 nucleosomes in GENESIS as examples of large-scale biomolecular simulations with residue-level CG models. This framework extends accessible spatial and temporal scales by multi-scale simulations to study biologically relevant phenomena, such as genome-scale chromatin folding or phase-separated membrane-less condensations.
  • Protein assembly and crowding simulations

    Lim Heo, Yuji Sugita, and Michael Feig
    Proteins encounter frequent molecular interactions in biological environments. Computer simulations have become an increasingly important tool in providing mechanistic insights into how such interactions in vivo relate to their biological function. The review here focuses on simulations describing protein assembly and molecular crowding effects as two important aspects that are distinguished mainly by how specific and long-lived protein contacts are. On the topic of crowding, recent simulations have provided new insights into how crowding affects protein folding and stability, modulates enzyme activity, and affects diffusive properties. Recent studies of assembly processes focus on assembly pathways, especially for virus capsids, amyloid aggregation pathways, and the role of multivalent interactions leading to phase separation. Also, discussed are technical challenges in achieving increasingly realistic simulations of interactions in cellular environments.
  • The inherent flexibility of receptor binding domains in SARS-CoV-2 spike protein

    Hisham M Dokainish, Suyong Re, Takaharu Mori, Chigusa Kobayashi, Jaewoon Jung, and Yuji Sugita
    Spike (S) protein is the primary antigenic target for neutralization and vaccine development for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It decorates the virus surface and undergoes large motions of its receptor binding domains (RBDs) to enter the host cell. Here, we observe Down, one-Up, one-Open, and two-Up-like structures in enhanced molecular dynamics simulations, and characterize the transition pathways via inter-domain interactions. Transient salt-bridges between RBDA and RBDC and the interaction with glycan at N343B support RBDA motions from Down to one-Up. Reduced interactions between RBDA and RBDB in one-Up induce RBDB motions toward two-Up. The simulations overall agree with cryo-electron microscopy structure distributions and FRET experiments and provide hidden functional structures, namely, intermediates along Down-to-one-Up transition with druggable cryptic pockets as well as one-Open with a maximum exposed RBD. The inherent flexibility of S-protein thus provides essential information for antiviral drug rational design or vaccine development.
  • Weight average approaches for predicting dynamical properties of biomolecules

    Kiyoshi Yagi, Suyong Re, Takaharu Mori, and Yuji Sugita
    Recent advances in atomistic molecular dynamics (MD) simulations of biomolecules allow us to explore their conformational spaces widely, observing large-scale conformational fluctuations or transitions between distinct structures. To reproduce or refine experimental data using MD simulations, structure ensembles, which are characterized by multiple structures and their statistical weights on the rugged free-energy landscapes, are often used. Here, we summarize weight average approaches for various experimental measurements. Weight average approaches are now applied to hybrid quantum mechanics/molecular mechanics MD simulations to predict fast vibrational motions in a protein with a high accuracy for better understanding of molecular functions from atomic structures.