Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
arXiv:2603.17247v1 Announce Type: new Abstract: We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obtain continuous embeddings, which are then transformed into compact binary latent representations. In this space, protein fitness is approximated using a quadratic unconstrained binary optimization (QUBO) model, enabling efficient combinatorial search via classical heuristics such as simulated annealing and genetic algorithms. On the ProteinGym benchmark, we demonstrate that Q-BIOLAT captures meaningful structure in protein fitness landscapes and enables the identification of high-fitness variants. Despite using a simple binarization scheme, our method consistently retrieves sequences whose nearest neighbors lie within the top fraction of the training fitness distribution, particularly under the strongest configurations. We further show that
arXiv:2603.17247v1 Announce Type: new Abstract: We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obtain continuous embeddings, which are then transformed into compact binary latent representations. In this space, protein fitness is approximated using a quadratic unconstrained binary optimization (QUBO) model, enabling efficient combinatorial search via classical heuristics such as simulated annealing and genetic algorithms. On the ProteinGym benchmark, we demonstrate that Q-BIOLAT captures meaningful structure in protein fitness landscapes and enables the identification of high-fitness variants. Despite using a simple binarization scheme, our method consistently retrieves sequences whose nearest neighbors lie within the top fraction of the training fitness distribution, particularly under the strongest configurations. We further show that different optimization strategies exhibit distinct behaviors, with evolutionary search performing better in higher-dimensional latent spaces and local search remaining competitive in preserving realistic sequences. Beyond its empirical performance, Q-BIOLAT provides a natural bridge between protein representation learning and combinatorial optimization. By formulating protein fitness as a QUBO problem, our framework is directly compatible with emerging quantum annealing hardware, opening new directions for quantum-assisted protein engineering. Our implementation is publicly available at: https://github.com/HySonLab/Q-BIOLAT
Executive Summary
This article proposes Q-BIOLAT, a novel framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Leveraging pretrained protein language models, Q-BIOLAT transforms continuous embeddings into compact binary representations, allowing for efficient combinatorial search via classical heuristics. The authors demonstrate Q-BIOLAT's effectiveness on the ProteinGym benchmark, showcasing its ability to capture meaningful structure in protein fitness landscapes and identify high-fitness variants. Furthermore, the framework's formulation as a quadratic unconstrained binary optimization (QUBO) problem facilitates compatibility with emerging quantum annealing hardware. As a result, Q-BIOLAT offers a promising approach for quantum-assisted protein engineering, with the potential to accelerate discovery in this field.
Key Points
- ▸ Q-BIOLAT transforms protein sequences into compact binary latent representations using pretrained protein language models.
- ▸ The framework approximates protein fitness using a QUBO model, enabling efficient combinatorial search via classical heuristics.
- ▸ Q-BIOLAT demonstrates robust performance on the ProteinGym benchmark, retrieving high-fitness variants and capturing meaningful structure in protein fitness landscapes.
Merits
Strength in Formulation
Q-BIOLAT's QUBO formulation enables direct compatibility with emerging quantum annealing hardware, opening new avenues for quantum-assisted protein engineering.
Robust Performance
The framework consistently retrieves high-fitness variants and captures meaningful structure in protein fitness landscapes, demonstrating its effectiveness on the ProteinGym benchmark.
Demerits
Simplistic Binarization Scheme
Q-BIOLAT's reliance on a simple binarization scheme may limit its ability to capture nuanced protein structure and fitness relationships.
Expert Commentary
Q-BIOLAT represents a significant advancement in the field of protein engineering, offering a novel framework for modeling and optimizing protein fitness landscapes in binary latent spaces. The authors' use of pretrained protein language models and QUBO formulation is particularly noteworthy, as it enables efficient combinatorial search and direct compatibility with emerging quantum annealing hardware. However, the simplicity of the binarization scheme may limit the framework's ability to capture nuanced protein structure and fitness relationships. Nevertheless, Q-BIOLAT's potential to accelerate discovery in protein engineering is substantial, and its development has the potential to drive significant advances in this field.
Recommendations
- ✓ Future research should focus on developing more sophisticated binarization schemes to improve the framework's ability to capture nuanced protein structure and fitness relationships.
- ✓ The development of Q-BIOLAT's quantum-assisted variants should be prioritized to fully leverage the potential of emerging quantum annealing hardware.
