A Novel P-bit-based Probabilistic Computing Approach for Solving the 3-D Protein Folding Problem

TL;DR

This paper introduces a novel probabilistic computing approach using p-bits to efficiently solve the NP-complete protein folding problem by mapping it onto an Ising model, demonstrating significant acceleration and improved energy landscape simplification.

Contribution

It presents the first application of probabilistic computing with p-bits to protein folding, introducing a new encoding method that simplifies the energy landscape and accelerates solution finding.

Findings

Halves the energy levels for short peptides.

Achieves 100x speedup for 10-amino acid sequences.

Successfully predicts the ground state for 36-amino acid peptide.

Abstract

In the post-Moore era, the need for efficient solutions to non-deterministic polynomial-time (NP) problems is becoming more pressing. In this context, the Ising model implemented by the probabilistic computing systems with probabilistic bits (p-bits) has attracted attention due to the widespread availability of p-bits and support for large-scale simulations. This study marks the first work to apply probabilistic computing to tackle protein folding, a significant NP-complete problem challenge in biology. We represent proteins as sequences of hydrophobic (H) and polar (P) beads within a three-dimensional (3-D) grid and introduce a novel many-body interaction-based encoding method to map the problem onto an Ising model. Our simulations show that this approach significantly simplifies the energy landscape for short peptide sequences of six amino acids, halving the number of energy levels. Furthermore, the proposed mapping method achieves approximately 100 times acceleration for sequences consisting of ten amino acids in identifying the correct folding configuration. We predicted the optimal folding configuration for a peptide sequence of 36 amino acids by identifying the ground state. These findings highlight the unique potential of the proposed encoding method for solving protein folding and, importantly, provide new tools for solving similar NP-complete problems in biology by probabilistic computing approach.