Yesterday we read the genome; last Friday we designed proteins from scratch. Today’s verb is evolve — using machine learning to improve the proteins nature already gave us. Directed evolution won a Nobel; adding ML lets it ‘predict how sequence maps to function in a data-driven manner without requiring a detailed model of the underlying physics.’ DeepSequence showed you can score mutations ‘in an unsupervised manner solely on the basis of sequence information,’ beating prior methods across deep mutational scans. UniRep distilled proteins into a representation that’s ‘structurally, evolutionarily and biophysically grounded,’ buying ’two orders of magnitude efficiency improvement.’ Low-N built ‘an accurate virtual fitness landscape’ from ‘as few as 24 functionally assayed mutant sequences’ and screened ten million in silico. Hsu et al. showed a plain ridge regression ‘is competitive with, and on average outperforms more sophisticated methods’ — and preached ’the importance of systematic evaluations and sufficient baselines.’ And at the bench, ML-guided evolution engineered a new-to-nature enzyme to ‘93% and 79% ee.’ Design–build–test–learn, made real.
