Antibody LMs are worse for expression prediction than generic PLMs

Summary

Antibody-specific protein language models are worse for antibody expression prediction than generic PLMs (1).

Figures

Model	Binding N = 422 (Shanehsazzadeh et al., 2023)	Binding N = 2048 (Warszawski et al., 2019)	Binding N = 4275 (Koenig et al., 2017)	Expression N = 4275 (Koenig et al., 2017)
AbLang (Olsen et al., 2022b)	0.293 ± 0.117	0.246 ± 0.038	0.244 ± 0.034	0.439 ± 0.027
AntiBERTy (Ruffolo et al., 2021)	0.239 ± 0.102	0.217 ± 0.056	0.199 ± 0.025	0.401 ± 0.032
ProtBert (Elnaggar et al., 2022)	0.200 ± 0.106	0.149 ± 0.024	0.101 ± 0.017	0.491 ± 0.029
IgBert-unpaired	0.278 ± 0.094	0.181 ± 0.040	0.177 ± 0.018	0.347 ± 0.023
IgBert	0.306 ± 0.114	0.131 ± 0.047	0.174 ± 0.032	0.400 ± 0.023
ProtT5 (Elnaggar et al., 2022)	0.290 ± 0.105	0.186 ± 0.037	0.206 ± 0.029	0.697 ± 0.02
IgT5-unpaired	0.299 ± 0.119	0.245 ± 0.049	0.179 ± 0.014	0.567 ± 0.025
IgT5	0.274 ± 0.070	0.297 ± 0.057	0.25 ± 0.019	0.548 ± 0.067

Ref (1)

See also

• Antibody LMs outperform generic PLMs on intrafamily thermostability prediction
• Random splits overestimate protein language model generalization on antibody expression prediction

1.

Kenlay H, Dreyer FA, Kovaltsuk A, Miketa D, Pires D, Deane CM. Large scale paired antibody language models. PLOS Computational Biology. 2024;20(12):e1012646. Available from: https://doi.org/10.1371/journal.pcbi.1012646