Exact and efficient calculation of Lagrange multipliers in constrained biological polymers: Proteins and nucleic acids as example cases

TL;DR

This paper introduces an exact, non-iterative method to efficiently compute Lagrange multipliers for constraints in molecular dynamics simulations of biological polymers, significantly reducing computational complexity.

Contribution

It presents a novel linear algebra approach exploiting the banded structure of constraint matrices in biological polymers, enabling faster, exact calculation of Lagrange multipliers.

Findings

Method reduces computational complexity from O(Nc^3) to O(Nc).

Applicable to proteins, nucleic acids, and model molecules.

Validated with polyalanine peptides in AMBER simulations.

Abstract

In order to accelerate molecular dynamics simulations, it is very common to impose holonomic constraints on their hardest degrees of freedom. In this way, the time step used to integrate the equations of motion can be increased, thus allowing, in principle, to reach longer total simulation times. The imposition of such constraints results in an aditional set of Nc equations (the equations of constraint) and unknowns (their associated Lagrange multipliers), that must be solved in one way or another at each time step of the dynamics. In this work it is shown that, due to the essentially linear structure of typical biological polymers, such as nucleic acids or proteins, the algebraic equations that need to be solved involve a matrix which is banded if the constraints are indexed in a clever way. This allows to obtain the Lagrange multipliers through a non-iterative procedure, which can be considered exact up to machine precision, and which takes O(Nc) operations, instead of the usual O(Nc3) for generic molecular systems. We develop the formalism, and describe the appropriate indexing for a number of model molecules and also for alkanes, proteins and DNA. Finally, we provide a numerical example of the technique in a series of polyalanine peptides of different lengths using the AMBER molecular dynamics package.