深度学习引导的进化优化用于蛋白质设计
Deep learning-guided evolutionary optimization for protein design
AI总结：
BoGA结合进化搜索与贝叶斯优化，高效探索蛋白质序列空间，用于设计具有特定功能的蛋白质，提升生物技术应用效率。
摘要：
设计具有所需特性的新型蛋白质仍然是一个重大挑战，由于序列空间庞大以及序列-功能关系的复杂性。高效探索该空间以识别满足特定设计标准的序列对于推进治疗学和生物技术至关重要。本文提出BoGA（贝叶斯优化遗传算法），一种结合进化搜索与贝叶斯优化的框架，以高效导航序列空间。通过将遗传算法作为随机提案生成器整合到代理建模循环中，BoGA根据先前评估和代理模型预测优先选择候选者，从而实现数据高效的优化。我们通过在序列和结构设计任务上的基准测试，随后在设计针对肺炎链球菌关键致病因子肺炎溶血素的肽结合体时验证了BoGA的实用性。BoGA加速了高置信度结合体的发现，展示了在多样化目标下高效蛋白质设计的潜力。该算法在BoPep套件中实现，并可通过GitHub（https://github.com/ErikHartman/bopep）以MIT许可证获取。
英文摘要：
Designing novel proteins with desired characteristics remains a significant challenge due to the large sequence space and the complexity of sequence-function relationships. Efficient exploration of this space to identify sequences that meet specific design criteria is crucial for advancing therapeutics and biotechnology. Here, we present BoGA (Bayesian Optimization Genetic Algorithm), a framework that combines evolutionary search with Bayesian optimization to efficiently navigate the sequence space. By integrating a genetic algorithm as a stochastic proposal generator within a surrogate modeling loop, BoGA prioritizes candidates based on prior evaluations and surrogate model predictions, enabling data-efficient optimization. We demonstrate the utility of BoGA through benchmarking on sequence and structure design tasks, followed by its application in designing peptide binders against pneumolysin, a key virulence factor of \textit{Streptococcus pneumoniae}. BoGA accelerates the discovery of high-confidence binders, demonstrating the potential for efficient protein design across diverse objectives. The algorithm is implemented within the BoPep suite and is available under an MIT license at \href{https://github.com/ErikHartman/bopep}{GitHub}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
