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

2025-02-27

AI Summary

This work addresses the NP-complete three-dimensional protein folding problem by proposing the first probabilistic computing framework based on p-bits. Methodologically, it introduces an Ising model encoding scheme driven by multi-body interactions, integrating a 3D cubic lattice representation of the hydrophobic-polar (H/P) chain with a customized energy function to significantly simplify the energy landscape. Crucially, it pioneers the application of probabilistic computation to biomolecular modeling, overcoming inherent limitations of deterministic algorithms. Experimental results demonstrate: a 50% reduction in ground-state energy for 6-residue sequences; approximately 100× speedup in solving 10-residue sequences; and successful prediction of the global-optimal fold for a 36-residue peptide. This work establishes a scalable, hardware-efficient computational paradigm for protein folding and related NP-hard biological optimization problems.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Search and Optimization: Evolutionary Computation
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

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.

Problem

• Probabilistic computing for protein folding
• Novel Ising model mapping method
• Efficient solution for NP-complete problems

Innovation

• P-bit-based probabilistic computing
• Many-body interaction encoding
• Ising model protein folding