How Evolution Teaches Antibodies express
An antibody is a protein that catches viruses and bacteria. When an enemy enters the body, immune cells start trying out different antibody variants, as if testing different keys against a lock. Beneficial changes are kept, harmful ones are discarded. Over a few weeks, this process yields an antibody that fits its target perfectly.
Conventional computer models don't see this history. They look at finished antibodies and treat them as independent, like cards in a deck. But each antibody is a descendant of a previous one, and its structure records a path of trial and error. The new model CoSiNE reads this lineage. It represents antibody evolution as a chain of small steps, where each step is a single-letter substitution in the genetic code.
The model doesn't just replay the past: it can accelerate it. An embedded "guide" mechanism steers the search toward antibodies that bind more tightly to the desired target. It's like a plant breeder who doesn't wait for random mutations but directly plants seeds with desired traits. That's why CoSiNE outperforms even the most advanced language models, which predict protein properties but don't understand where those properties come from.
A single antibody can change its shape to grab two different targets, like a hand that adapts to different objects.