Poisson-Boltzmann-based machine learning model for electrostatic analysis.

Journal: Biophysical journal
PMID: 38356263

Abstract

Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation free energies for macromolecules due to the nonlinearity, dielectric jumps, charge singularity, and geometric complexity associated with the PB equation. The present work introduces a PB-based machine learning (PBML) model for biomolecular electrostatic analysis. Trained with the second-order accurate MIBPB solver, the proposed PBML model is found to be more accurate and faster than several eminent PB solvers in electrostatic analysis. The proposed PBML model can provide highly accurate PB electrostatic solvation free energy of new biomolecules or new conformations generated by molecular dynamics with much reduced computational cost.

Authors

  • Jiahui Chen
    Molecular Analytics and Photonics (MAP) Lab, Program of Polymer and Color Chemistry, Department of Textile Engineering, Chemistry and Science, North Carolina State University, 1020 Main Campus Drive, Raleigh, NC, 27606, USA.
  • Yongjia Xu
    Google LLC, Mountain View, California.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Zixuan Cang
    Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.
  • Weihua Geng
    Department of Mathematics, Southern Methodist University, Dallas, Texas. Electronic address: wgeng@smu.edu.
  • Guo-Wei Wei
    Department of Mathematics, Department of Electrical and Computer Engineering, Department of Biochemistry and Molecular Biology, Michigan State University, MI 48824, USA.

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