Fig. 4: Guiding evolution without explicitly modeling fitness.
From: Efficient evolution of human antibodies from general protein language models
a, The same strategy and language models that we use to affinity mature antibodies can also recommend high-fitness changes across a diversity of selection pressures and protein families, as identified experimentally using high-throughput scanning mutagenesis assays8,48 (described in Supplementary Table 13). ‘Fraction positive’ indicates the percentage of high-fitness amino acid substitutions within either the set of substitutions recommended by the language model (LM guided) or the set of all single-residue substitutions (Background). A large portion of language-model-guided substitutions have high fitness, which, in many cases, is significantly enriched compared to the background percentage; also see Extended Data Figs. 4–6, and see Supplementary Table 13 for the exact one-sided hypergeometric P values and sample sizes. ADRB2, adrenoreceptor beta 2; β-la., β-lactamase; Env, envelope glycoprotein; infA, translation initiation factor 1; MAPK1, mitogen-activated protein kinase 1; PafA, phosphate-irrepressible alkaline phosphatase. b, Conceptually, the prior information encoded by evolutionary plausibility is represented in this cartoon by the rainbow road, where ascending corresponds to improving fitness and descending corresponds to lowering fitness. Moving in any direction (for example, via random or brute force mutagenesis) would most likely decrease fitness or have a high chance of being a detrimental change (represented by the green ball). However, if evolutionary plausibility is an efficient prior (Fig. 1b), then movement that is constrained to the plausible regime (for example, when guided by a language model) substantially increases the chance of improving fitness (represented by the red ball).
