Protein language models and the long tail of functional diversity

Abstract

Protein language model performance on downstream tasks depends on the pretraining data, motivating recent efforts to combine genomic- and metagenomic-derived protein sequences into large-scale atlases. Because these datasets are highly redundant, sequences are typically clustered by similarity and sampled during training. Sequences that do not belong to any cluster, known as “singletons”, are typically excluded from training and evaluation because they are considered to be artifacts. However, singletons represent the long tail of functional diversity and are abundant in many large-scale atlases: nearly 43% of the 3.34 billion sequences in the joint genomic-metagenomic dataset GigaRef are singletons. Here, we characterize singletons derived from UniRef and GigaRef by assessing whether clustering missed homologs, how much their exclusion affects protein language model (PLM) training, and which biological domains they contain. We find that many GigaRef singletons belong to a cluster under alternative parameter settings, suggesting that genomic and metagenomic datasets may require dataset-specific clustering configurations. We also show that singletons share mutual information with clustered sequences, making them learnable by PLMs and useful for training. Finally, metagenomic singletons carry denser, more diverse domain content than clustered sequences, including domain-level homology that sequence-identity clustering misses. Together, these results support including singletons in PLM training and call for closer examination of data curation in large-scale integrated sequence atlases.