Paper List
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STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
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Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
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Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
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Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
用于量子退火优化的二元潜在蛋白质适应度景观
University of Alabama at Birmingham
30秒速读
IN SHORT: 通过将序列映射到二元潜在空间进行基于QUBO的适应度优化，桥接蛋白质表示学习和组合优化。
核心创新
- Methodology First framework to transform protein language model embeddings into binary latent representations for QUBO-based fitness modeling
- Methodology Enables direct compatibility with quantum annealing hardware through native QUBO formulation
- Biology Demonstrates that simple binary representations can capture meaningful structure in protein fitness landscapes
主要结论
- Q-BioLat在ProteinGym GFP数据集（10,000个样本，潜在维度32-64）上实现了0.385-0.413的Spearman相关性
- 优化后的序列始终检索到适应度百分位顶部的最近邻，模拟退火在代理分数上实现了1.529±的改进
- 遗传算法在更高维潜在空间（m=64）中优于其他方法，而局部搜索能更好地保持序列真实性
摘要: 我们提出了Q-BioLat，一个在二元潜在空间中建模和优化蛋白质适应度景观的框架。从蛋白质序列出发，我们利用预训练的蛋白质语言模型获得连续嵌入，然后将其转换为紧凑的二元潜在表示。在这个空间中，蛋白质适应度使用二次无约束二元优化（QUBO）模型进行近似，从而通过经典启发式方法（如模拟退火和遗传算法）实现高效的组合搜索。在ProteinGym基准测试中，我们证明Q-BioLat能够捕捉蛋白质适应度景观中的有意义结构，并能够识别高适应度变体。尽管使用了简单的二值化方案，我们的方法始终能检索到其最近邻位于训练适应度分布顶部的序列，特别是在最强配置下。我们进一步表明，不同的优化策略表现出不同的行为，进化搜索在更高维的潜在空间中表现更好，而局部搜索在保持真实序列方面仍具有竞争力。除了其经验性能外，Q-BioLat为蛋白质表示学习和组合优化之间提供了自然的桥梁。通过将蛋白质适应度表述为QUBO问题，我们的框架与新兴的量子退火硬件直接兼容，为量子辅助蛋白质工程开辟了新的方向。