physics Module

The physics module provides high-performance physics-based refinement for synthetic protein structures using the OpenMM engine.

Overview

While the generator creates structures based on geometric rules, the physics module ensures these structures are physically plausible by resolving steric clashes and optimizing bond lengths and angles through energy minimization.

Main Classes

::: synth_pdb.physics.EnergyMinimizer options: show_root_heading: true show_source: true members: - init - minimize - equilibrate - add_hydrogens_and_minimize - calculate_energy

Usage Examples

Energy Minimization

Refine a PDB structure to resolve steric clashes and regularize geometry.

from synth_pdb.physics import EnergyMinimizer

minimizer = EnergyMinimizer(solvent_model="obc2")
success = minimizer.minimize("input.pdb", "output_minimized.pdb")

Thermal Equilibration (MD)

Run a short Molecular Dynamics (MD) simulation to "heat" the protein to 300K, allowing it to settle into a stable dynamic average.

minimizer.equilibrate("input.pdb", "output_equilibrated.pdb", steps=1000)

Handling Metal Coordination

Automatically apply harmonic constraints to coordinate metal ions like Zinc (Zn2+).

# Metal-ion coordination logic is handled automatically when calling minimize
# with the appropriate parameters derived from the generator or cofactors module.
minimizer.minimize("input.pdb", "output.pdb", coordination=[("ZN", [10, 15, 20, 25])])

Educational Notes

What is Energy Minimization?

Proteins fold into specific 3D shapes to minimize their "Gibbs Free Energy". A generated structure often has "clashes" where atoms are too close (high Van der Waals repulsion) or bond angles are strained.

Energy Minimization is like rolling a ball down a hill. The "Energy Landscape" represents the potential energy of the protein as a function of all its atom coordinates. The algorithm moves atoms slightly to reduce this energy, finding a local minimum where the structure is physically relaxed.

Anatomy of a Forcefield

A forcefield (like Amber14) approximates the potential energy ($U$) of a molecule as a sum of four main terms:

$$U = U_{bond} + U_{angle} + U_{torsion} + [U_{vdw} + U_{elec}]$$

  1. Bonded Terms: Atoms behave like balls on springs. Pushing them away from ideal lengths/angles costs energy.
  2. Non-Bonded Terms:
    • Van der Waals (Lennard-Jones): Models steric repulsion and London dispersion.
    • Electrostatics (Coulomb): Interaction between point charges (e.g., salt bridges).

Implicit vs. Explicit Solvent

  1. Explicit Solvent (TIP3P): Every water molecule ($H_2O$) is simulated. This captures the full enthalpic and entropic costs of solvation but is computationally expensive.
  2. Implicit Solvent (OBC2): Also known as "Born Solvation". The water is treated as a continuous dielectric field. The OBC2 (Onufriev-Bashford-Case) model is a refined version that parameterizes atomic radii to match explicit solvent behavior closely while being 10-100x faster.

References

  • OpenMM: Eastman, P., et al. (2017). "OpenMM 7: Rapid development of high performance algorithms for molecular dynamics." PLOS Computational Biology. DOI: 10.1371/journal.pcbi.1005659
  • OBC2 Solvent: Onufriev, A., Bashford, D., & Case, D. A. (2004). "Exploring protein native states and large-scale conformational changes with a modified generalized born model." Proteins: Structure, Function, and Bioinformatics. DOI: 10.1002/prot.20154
  • Amber Forcefield: Case, D. A., et al. (2005). "The Amber biomolecular simulation programs." Journal of Computational Chemistry. DOI: 10.1002/jcc.20290
  • Lipari-Szabo Formalism: Lipari, G., & Szabo, A. (1982). "Model-free approach to the interpretation of nuclear magnetic resonance relaxation in macromolecules." Journal of the American Chemical Society. DOI: 10.1021/ja00381a009

See Also