Conditionally Site-Independent Neural Evolution of Antibody Sequences
This work addresses antibody design for biomedical applications by integrating evolutionary information, offering a novel approach beyond incremental improvements.
The paper tackles the problem of antibody engineering by bridging deep learning and phylogenetic models to capture evolutionary dynamics and epistatic interactions, resulting in CoSiNE, which outperforms state-of-the-art language models in zero-shot variant effect prediction and enables efficient antibody affinity optimization.
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.