We present a new Matched Interface and Boundary (MIB) regularization method for treating charge singularity in solvated biomolecules whose electrostatics are described by the Poisson–Boltzmann (PB) equation. In a regularization method, by decomposing the potential function into two or three components, the singular component can be analytically represented by the Green’s function, while other components possess a higher regularity. Our new regularization combines the efficiency of two-component schemes with the accuracy of the three-component schemes. Based on this regularization, a new MIB finite difference algorithm is developed for solving both linear and nonlinear PB equations, where the nonlinearity is handled by using the inexact-Newton’s method. Compared with the existing MIB PB solver based on a three-component regularization, the present algorithm is simpler to implement by circumventing the work to solve a boundary value Poisson equation inside the molecular interface and to compute related interface jump conditions numerically. Moreover, the new MIB algorithm becomes computationally less expensive, while maintains the same second order accuracy. This is numerically verified by calculating the electrostatic potential and solvation energy on the Kirkwood sphere on which the analytical solutions are available and on a series of proteins with various sizes.
Shenggao ZhouSoochow UniversityR. G. WeissETH ZurichLi-Tien ChengUniversity of California, San DiegoJoachim DzubiellaUniversity of FreiburgJ. Andrew McCammonUniversity of California, San DiegoBo LiUniversity of California, San Diego
Numerical Analysis and Scientific ComputingData Analysis, Bio-Statistics, Bio-Mathematicsmathscidoc:2005.25001
Proceedings of the National Academy of Sciences of the United States of America, 116, (30), 14989–14994, 2019.7
Ligand-receptor binding and unbinding are fundamental biomolecular processes and particularly essential to drug efficacy. Environmental water fluctuations, however, impact the corresponding thermodynamics and kinetics and thereby challenge theoretical descriptions. Here, we devise a holistic, implicit-solvent, multi-method approach to predict the (un)binding kinetics for a generic ligand-pocket model. We use the variational implicit-solvent model (VISM) to calculate the solute-solvent interfacial structures and the corresponding free energies, and combine the VISM with the string method to obtain the minimum energy paths and transition states between the various metastable (“dry” and “wet”) hydration states. The resulting dry-wet transition rates are then used in a spatially-dependent multi-state continuous-time Markov chain Brownian dynamics simulations, and the related Fokker–Planck equation calculations, of the ligand stochastic motion, providing the mean first-passage times for binding and unbinding. We find the hydration transitions to significantly slow down the binding process, in semi-quantitative agreement with existing explicit-water simulations, but significantly accelerate the unbinding process. Moreover, our methods allow the characterization of non-equilibrium hydration states of pocket and ligand during the ligand movement, for which we find substantial memory and hysteresis effects for binding versus unbinding. Our study thus provides a significant step forward towards efficient, physics-based interpretation and predictions of the complex kinetics in realistic ligand-receptor systems.
Mean field games (MFG) and mean field control (MFC) are critical classes of multi-agent models for efficient
analysis of massive populations of interacting agents. Their areas of application span topics in economics, finance, game theory, industrial engineering, crowd motion, and more. In this paper, we provide a flexible machine learning framework for the
numerical solution of potential MFG and MFC models. State of-the-art numerical methods for solving such problems utilize
spatial discretization that leads to a curse-of-dimensionality. We approximately solve high-dimensional problems by combining
Lagrangian and Eulerian viewpoints and leveraging recent advances from machine learning. More precisely, we work with a
Lagrangian formulation of the problem and enforce the underlying Hamilton-Jacobi-Bellman (HJB) equation that is derived
from the Eulerian formulation. Finally, a tailored neural network parameterization of the MFG/MFC solution helps us avoid any
spatial discretization. Our numerical results include the approximate solution of 100-dimensional instances of optimal transport
and crowd motion problems on a standard work station and a validation using a Eulerian solver in two dimensions. These results open the door to much-anticipated applications of MFG and MFC models that were beyond reach with existing numerical methods.
We propose an extension of the computational fluid mechanics approach to the Monge-Kantorovich mass transfer problem, which was developed by Benamou-Brenier. Our extension allows optimal transfer of unnormalized and unequal masses. We obtain a one-parameter family of simple modifications of the formulation in . This leads us to a new Monge-Ampere type equation and a new Kantorovich duality formula. These can be solved efficiently by, for example, the Chambolle-Pock primal-dual algorithm. This solution to the extended mass transfer problem gives us a simple metric for computing the distance between two unnormalized densities. The L1 version of this metric was shown in  (which is a precursor of our work here) to have desirable properties.
Zhongjian WangDepartment of Mathematics, The University of Hong KongXue LuoSchool of Mathematical Sciences, Beihang University (Shahe campus)Stephen S.-T. YauDepartment of Mathematical Sciences, Tsinghua UniversityZhiwen ZhangDepartment of Mathematics, The University of Hong Kong
Numerical Analysis and Scientific ComputingOptimization and ControlProbabilitymathscidoc:2004.25001