Evaluating Pretrained Protein Language Model Embeddings as Proxies for Functional Similarity.

ABSTRACT: Protein Language Models (PLMs) have emerged as powerful tools for representing protein sequences. We explore how embeddings (numeric vector representations) from pretrained PLMs can serve as direct numeric proxies for protein structure and function without requiring additional training or fine-tuning. In a proof-of-concept study of 22 cross-species complementation triplets-a gold standard for functional similarity where genes from one species are tested for their ability to rescue gene deletions in another species-we find that ESM-C 600 M embeddings summarized into pooled sliced-Wasserstein embeddings achieved high discrimination of subtle functional differences. This pooling method captures distributional properties of amino acid embeddings by comparing them against reference points using

SUBMITTER: Shaw R 

PROVIDER: S-EPMC12756192 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature