Data for 'VespaG: Expert-guided protein language models enable accurate and blazingly fast fitness prediction'

Celine Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine

bioRxiv 2024.04.24.590982; doi: https://doi.org/10.1101/2024.04.24.590982

 

For more information on data usage and generation please see https://github.com/JSchlensok/VespaG.

 

Abstract:

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 single amino acid variant effect predictor, leveraging embeddings of protein Language Models as input to a minimal deep learning model. 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. Assessed against the ProteinGym Substitution Benchmark (217 multiplex assays of variant effect with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 +/- 0.01, matching state-of-the-art methods such as GEMME, TranceptEVE, PoET, AlphaMissense, and VESPA. VespaG reached its top-level performance several orders of magnitude faster, predicting all mutational landscapes of the human proteome in 30 minutes on a consumer laptop (12-core CPU, 16 GB RAM).

 

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