Users praise the CoSiNE model release for antibody maturation and design, calling the work amazing and expressing happiness at seeing related tools like OASis used in practice.
The resulting samples remain structurally plausible and human-like.
We think this is a promising step toward controllable evolutionary protein design: not just generating sequences de novo, but guiding the processes that produce them. (7/8)
Traditional antibody language modeling assumes sequences are i.i.d. — ignoring the time-dependent process of affinity maturation.
To address this, CoSiNE explicitly models transitions: how likely is a mature antibody y to arise from a germline precursor x over time t? (2/8)
Huge thanks to my coauthors @aakarshv1 , @koheisanno , @jiarlu , @ematsen , @milind_jagota , and @yun_s_song ! We will be presenting at ICML.
Preprint: https://arxiv.org/abs/2602.18982 Code: https://github.com/thematrixmaster/cosine Blog: https://songlab-cal.github.io/cosine (8/8)
A key challenge is that antibody evolution mixes two signals.
Some mutations are frequent because they are likely under somatic hypermutation. Others occur because the antibodies carrying them are favored by selection.
For VEP & design, we want to separate these effects. (3/8)
CoSiNE achieves this by comparing two likelihoods:
How likely is a mutation under the learned maturation model?
How likely is it under neutral SHM?
The difference gives a selection score: enrichment beyond mutation bias. This improves VEP over transition likelihood. (4/8)
We also explore CoSiNE as a design model.
With predictor guidance sampling, we steer simulated maturation trajectories toward desired properties at inference time, biasing evolution toward antibodies with higher predicted affinity for specific antigens. (6/8)
Importantly, our selection score beats strong antibody and protein LM baselines on zero-shot antibody VEP across binding and expression datasets.
This suggests that learning germline-to-mature evolution adds signal beyond antibody-likeness alone. (5/8)
@stephenzlu @aakarshv1 Amazing workkk
@stephenzlu Always happy to see OASis in the wild! Btw check out promb for a high throughput implementation if you haven’t already
@stephenzlu https://github.com/MSDLLCpapers/promb