Monte Carlo Procedure for Protein Design

TL;DR

This paper introduces a novel multisequence Monte Carlo method for protein sequence optimization that improves thermodynamic folding properties and enables efficient exploration of large sequence spaces.

Contribution

It presents a new Monte Carlo approach based on maximizing conditional probabilities for protein design, enhancing efficiency and scalability.

Findings

Successfully applied to 2D HP model with chains up to length 32.

Ensures designed sequences are thermodynamically good folders.

Introduces a bootstrap procedure for large sequence space exploration.

Abstract

A new method for sequence optimization in protein models is presented. The approach, which has inherited its basic philosophy from recent work by Deutsch and Kurosky [Phys. Rev. Lett. 76, 323 (1996)] by maximizing conditional probabilities rather than minimizing energy functions, is based upon a novel and very efficient multisequence Monte Carlo scheme. By construction, the method ensures that the designed sequences represent good folders thermodynamically. A bootstrap procedure for the sequence space search is devised making very large chains feasible. The algorithm is successfully explored on the two-dimensional HP model with chain lengths N=16, 18 and 32.