Exploiting molecular dynamics in Nested Sampling simulations of small peptides

Nested Sampling (NS) is a parameter space sampling algorithm which can be used for sampling the equilibrium thermodynamics of atomistic systems. NS has previously been used to explore the potential energy surface of a coarse-grained protein model and has significantly outperformed parallel tempering when calculating heat capacity curves of Lennard-Jones clusters. The original NS algorithm uses Monte Carlo (MC) moves; however, a variant, Galilean NS, has recently been introduced which allows NS to be incorporated into a molecular dynamics framework, so NS can be used for systems which lack efficient prescribed MC moves. In this work we demonstrate the applicability of Galilean NS to atomistic systems. We present an implementation of Galilean NS using the Amber molecular dynamics package and demonstrate its viability by sampling alanine dipeptide, both in vacuo and implicit solvent. Unlike previous studies of this system, we present the heat capacity curves of alanine dipeptide, whose calculation provides a stringent test for sampling algorithms. We also compare our results with those calculated using replica exchange molecular dynamics (REMD) and find good agreement. We show the computational effort required for accurate heat capacity estimation for small peptides. We also calculate the alanine dipeptide Ramachandran free energy surface for a range of temperatures and use it to compare the results using the latest Amber force field with previous theoretical and experimental results.We acknowledge support from the Leverhulme Trust (Grant F/00 215/BL(NSB, CV and DLW)) and the EPSRC (Grants EP/J020281/1(DLW), EP/J010847/1 (GC) and a Doctoral Training Award (RJB)).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.cpc.2015.12.00

Constant-pressure nested sampling with atomistic dynamics

© 2017 American Physical Society. The nested sampling algorithm has been shown to be a general method for calculating the pressure-temperature-composition phase diagrams of materials. While the previous implementation used single-particle Monte Carlo moves, these are inefficient for condensed systems with general interactions where single-particle moves cannot be evaluated faster than the energy of the whole system. Here we enhance the method by using all-particle moves: either Galilean Monte Carlo or the total enthalpy Hamiltonian Monte Carlo algorithm, introduced in this paper. We show that these algorithms enable the determination of phase transition temperatures with equivalent accuracy to the previous method at 1/N of the cost for an N-particle system with general interactions, or at equal cost when single-particle moves can be done in 1/N of the cost of a full N-particle energy evaluation. We demonstrate this speed-up for the freezing and condensation transitions of the Lennard-Jones system and show the utility of the algorithms by calculating the order-disorder phase transition of a binary Lennard-Jones model alloy, the eutectic of copper-gold, the density anomaly of water, and the condensation and solidification of bead-spring polymers. The nested sampling method with all three algorithms is implemented in the pymatnest software

Exploiting molecular dynamics in Nested Sampling simulations of small peptides

© 2016 Elsevier B.V. All rights reserved. Nested Sampling (NS) is a parameter space sampling algorithm which can be used for sampling the equilibrium thermodynamics of atomistic systems. NS has previously been used to explore the potential energy surface of a coarse-grained protein model and has significantly outperformed parallel tempering when calculating heat capacity curves of Lennard-Jones clusters. The original NS algorithm uses Monte Carlo (MC) moves; however, a variant, Galilean NS, has recently been introduced which allows NS to be incorporated into a molecular dynamics framework, so NS can be used for systems which lack efficient prescribed MC moves. In this work we demonstrate the applicability of Galilean NS to atomistic systems. We present an implementation of Galilean NS using the Amber molecular dynamics package and demonstrate its viability by sampling alanine dipeptide, both in vacuo and implicit solvent. Unlike previous studies of this system, we present the heat capacity curves of alanine dipeptide, whose calculation provides a stringent test for sampling algorithms. We also compare our results with those calculated using replica exchange molecular dynamics (REMD) and find good agreement. We show the computational effort required for accurate heat capacity estimation for small peptides. We also calculate the alanine dipeptide Ramachandran free energy surface for a range of temperatures and use it to compare the results using the latest Amber force field with previous theoretical and experimental results

Determining pressure-temperature phase diagrams of materials

© 2016 American Physical Society. ©2016 American Physical Society. We extend the nested sampling algorithm to simulate materials under periodic boundary and constant pressure conditions, and show how it can be used to determine the complete equilibrium phase diagram for a given potential energy function, efficiently and in a highly automated fashion. The only inputs required are the composition and the desired pressure and temperature ranges, in particular, solid-solid phase transitions are recovered without any a priori knowledge about the structure of solid phases. We benchmark and showcase the algorithm on the periodic Lennard-Jones system, aluminum, and NiTi