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Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic

Zheng Cheng^1,2, Hangrui Bi^1,3, Siyuan Liu³

¹School of Mathematical Sciences, Peking University, Beijing 100871, China.

Journal of Chemical Theory and Computation

|June 18, 2024

Summary

This study presents a new database and E(3) neural network (E3NN) for accurately predicting long-range interactions in molecular dynamics (MD) simulations. This advances the development of next-generation protein force fields.

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Area of Science:

Computational chemistry
Biophysics
Machine learning

Background:

Accurately describing long-range interactions is crucial for molecular dynamics (MD) simulations of proteins.
High-quality long-range potentials are essential for range-separated machine learning force fields.

Purpose of the Study:

To develop a comprehensive database of asymptotic parameters for protein fragments.
To create an accurate and transferable E(3) neural network (E3NN) model for predicting these parameters.
To enable next-generation protein force fields capable of describing long-range interactions.

Main Methods:

Construction of an asymptotic parameter database using active learning, covering atomic multipole moments, polarizabilities, and dispersion coefficients.
Development of an E(3) neural network (E3NN) to predict asymptotic parameters directly from local protein fragment geometry.
Validation against symmetry-adapted perturbation theory (SAPT) for electrostatic and dispersion energies.

Main Results:

The database effectively represents protein fragments' conformational diversity with 78,000 data points.
E3NN models achieved an R-squared value of 0.999 for predicting asymptotic parameters across protein fragments and dipeptide test sets.
Predicted long-range electrostatic and dispersion energies showed minimal error (0.07 and 0.02 kcal/mol, respectively) compared to SAPT.

Conclusions:

The developed force fields accurately describe long-range interactions in proteins.
The E3NN models demonstrate high accuracy and transferability for asymptotic parameter prediction.
This work paves the way for more accurate and efficient protein simulations using next-generation force fields.