A twist on folding: Predicting optimal sequences and optimal folds of simple protein models with the hidden-force algorithm

TL;DR

This paper introduces a dual optimization approach using the hidden-force Monte Carlo algorithm to predict both optimal sequences and folds of simple protein models, achieving lower energies and more compact cores than previous methods.

Contribution

It demonstrates the effectiveness of the hidden-force algorithm in optimizing protein sequences and folds, offering a new methodology for de novo protein design.

Findings

Lower energy folds than previous reports

Sequence optimization yields more compact protein cores

Method applicable to detailed protein models

Abstract

We propose a new way of looking at global optimization of off-lattice protein models. We present a dual optimization concept of predicting optimal sequences as well as optimal folds. We validate the utility of the recently introduced hidden-force Monte Carlo optimization algorithm by finding significantly lower energy folds for minimalist protein models than previously reported. Further, we also find the protein sequence that yields the lowest energy fold amongst all sequences for a given chain length and residue mixture. In particular, for protein models with a binary sequence, we show that the sequence-optimized folds form more compact cores than the lowest energy folds of the historically fixed, Fibonacci-series sequences of chain lengths of 13, 21, 34, 55, and 89. We emphasize that while the protein model we used is minimalist, the methodology is applicable to detailed protein models, and sequence optimization may yield novel folds and aid de novo protein design.