将蛋白质序列转化为二值潜在空间，用量子退火优化工具设计高活性蛋白。
Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
- 用预训练语言模型提取序列嵌入，再转为紧凑二值表示。
- 在ProteinGym上成功找到接近训练集最优的高活性变体。
- 兼容量子退火硬件，适合想尝试量子计算的蛋白质工程研究者。
我们提出Q-BIOLAT框架，将蛋白质序列映射到二值潜在空间中建模与优化蛋白适应度景观。基于蛋白质序列，利用预训练蛋白语言模型获取连续嵌入，再转换为紧凑的二值潜在表示。在此空间中，蛋白适应度通过无约束二次二值优化（QUBO）模型近似，从而可借助经典启发式算法（如模拟退火、遗传算法）高效进行组合搜索。在ProteinGym基准测试中，Q-BIOLAT有效捕捉了蛋白适应度景观的有意义结构，并成功识别出高适应度变异体。尽管采用简单二值化方案，该方法始终能检索出其最近邻位于训练适应度分布前分数部分的序列，尤其在最强配置下表现更优。我们还发现不同优化策略行为各异：进化搜索在高维潜空间中表现更佳，而局部搜索在保持序列真实性方面更具竞争力。除实证性能外，Q-BIOLAT自然连接了蛋白表示学习与组合优化。通过将蛋白适应度建模为QUBO问题，本框架可直接适配新兴量子退火硬件，为量子辅助蛋白工程开辟新路径。代码已公开于：https://github.com/HySonLab/Q-BIOLAT
原文摘要 · Abstract (English)
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
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