Protein Language Model Fitness is a Matter of Preference

Leveraging billions of years of evolution, scientists have trained protein language models (pLMs) to understand the sequence and structure space of proteins aiding in the design of more functional proteins. Although they have shown ability to improve efficiency in engineering, it remains unclear if such models capture true biological patterns or artifacts of the training data. We aim to predict the circumstances in which pLMs can successfully perform zero-shot fitness estimation. Our work studies trends observed over hundreds of deep mutational scans across multiple different fitness objectives. We find that the likelihood, or abstractly, implicit preference of a certain protein sequence imbued during pretraining is predictive of fitness prediction capabilities. Both over-preferred and under-preferred wild type sequences harm performance. Using influence functions to causally understand how individual data points increase protein likelihoods, we find that there exists a power law tail due to sequence homology. Lastly, under-performance on low likelihood wild type proteins can be remedied by unsupervised finetuning. These findings that pLM zero-shot fitness estimation can be predicted by the likelihood of the engineered sequence can motivate and improve pLMs' deployment in protein maturation campaigns.