Sequential Monte Carlo Methods for Protein Folding

TL;DR

This paper introduces growth algorithms for predicting low energy states of heteropolymers, which are promising models for understanding protein folding, using a novel depth-first, population-controlled approach.

Contribution

It presents a new class of growth algorithms that efficiently find low energy configurations of heteropolymers, differing from traditional Markov chain methods by using a recursive, depth-first growth strategy.

Findings

Algorithms are highly efficient for lattice models.

Competitive performance on simple off-lattice models.

Effective in guiding polymer growth towards low energy states.

Abstract

We describe a class of growth algorithms for finding low energy states of heteropolymers. These polymers form toy models for proteins, and the hope is that similar methods will ultimately be useful for finding native states of real proteins from heuristic or a priori determined force fields. These algorithms share with standard Markov chain Monte Carlo methods that they generate Gibbs-Boltzmann distributions, but they are not based on the strategy that this distribution is obtained as stationary state of a suitably constructed Markov chain. Rather, they are based on growing the polymer by successively adding individual particles, guiding the growth towards configurations with lower energies, and using "population control" to eliminate bad configurations and increase the number of "good ones". This is not done via a breadth-first implementation as in genetic algorithms, but depth-first via recursive backtracking. As seen from various benchmark tests, the resulting algorithms are extremely efficient for lattice models, and are still competitive with other methods for simple off-lattice models.