Expert-guided protein language models enable accurate and blazingly fast fitness prediction.

ABSTRACT:

Motivation

Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. We introduce VespaG, a blazingly fast missense amino acid variant effect predictor, leveraging protein language model (pLM) embeddings as input to a minimal deep learning model.

Results

To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. This setup increases interpretability compared to the baseline pLM and is easily retrainable with novel or updated pLMs. Assessed against the ProteinGym benchmark (217 multiplex assa

SUBMITTER: Marquet C 

PROVIDER: S-EPMC11588025 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature