抗体序列的条件性位点无关神经进化
Conditionally Site-Independent Neural Evolution of Antibody Sequences
AI总结：
提出CoSiNE模型，用深度神经网络参数化的连续时间马尔可夫链桥接系统发育模型与深度学习，实现抗体序列进化建模，在零样本变异效应预测中优于现有语言模型，并引入引导吉莱斯皮采样优化抗体亲和力。
摘要：
常见的抗体工程深度学习方法侧重于建模序列的边缘分布。然而，这些方法将序列视为独立样本，忽略了亲和力成熟作为抗体探索潜在适应度景观的进化过程中丰富且很大程度上未开发的信息来源。相比之下，经典的系统发育模型明确表示进化动力学，但缺乏捕捉复杂上位相互作用的表达能力。我们通过CoSiNE（一种由深度神经网络参数化的连续时间马尔可夫链）弥合了这一差距。数学上，我们证明CoSiNE提供了难以处理的顺序点突变过程的一阶近似，以分支长度二次方的误差界捕捉上位效应。实验上，CoSiNE通过明确区分选择与上下文依赖的体细胞超突变，在零样本变异效应预测中优于最先进的语言模型。最后，我们引入了引导吉莱斯皮（Guided Gillespie），一种在推理时引导CoSiNE的分类器引导采样方案，从而实现对特定抗原的抗体结合亲和力的高效优化。
英文摘要：
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.
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。
