用神经网络模拟抗体进化，精准预测突变影响并优化结合亲和力。
Conditionally Site-Independent Neural Evolution of Antibody Sequences
- 基于深度神经网络构建连续时间马尔可夫链，建模抗体序列进化过程。
- 零样本预测突变效果优于当前顶级语言模型，误差随分支长度平方增长。
- 引入引导采样法，可高效生成高亲和力抗体序列，适合药物设计应用。
主流抗体工程的深度学习方法通常仅建模序列的边缘分布，将序列视为独立样本，忽略了亲和力成熟作为揭示抗体探索适应度景观演化过程的重要信息来源。而传统系统发育模型虽显式表达演化动态，却难以捕捉复杂的上位性相互作用。本文提出CoSiNE，一种由深度神经网络参数化的连续时间马尔可夫链，数学上证明其对不可解析的逐点突变过程提供一阶近似，上位性效应误差在分支长度上为二次关系。实验表明，CoSiNE在零样本变异效应预测任务中超越现有最先进语言模型，通过显式分离选择与上下文依赖的体细胞超突变实现。最后，提出引导吉尔斯皮（Guided Gillespie）采样方案，在推理阶段引导演化路径，实现针对特定抗原的抗体结合亲和力高效优化。
原文摘要 · Abstract (English)
Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these methods overlook affinity maturation as a rich and largely untapped source of information about the evolutionary process by which antibodies explore the underlying fitness landscape. In contrast, classical phylogenetic models explicitly represent evolutionary dynamics but lack the expressivity to capture complex epistatic interactions. We bridge this gap with CoSiNE, a continuous-time Markov chain parameterized by a deep neural network. Mathematically, we prove that CoSiNE provides a first-order approximation to the intractable sequential point mutation process, capturing epistatic effects with an error bound that is quadratic in branch length. Empirically, CoSiNE outperforms state-of-the-art language models in zero-shot variant effect prediction by explicitly disentangling selection from context-dependent somatic hypermutation. Finally, we introduce Guided Gillespie, a classifier-guided sampling scheme that steers CoSiNE at inference time, enabling efficient optimization of antibody binding affinity toward specific antigens.
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