Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark
This work addresses the problem of evaluating protein prediction models in data-scarce settings for researchers in computational biology, but it is incremental as it builds on existing benchmarks and models.
The study assessed large protein language models like ESM-2 and SaProt on the FLIP benchmark, which focuses on constrained evaluation scenarios with limited data, to determine if recent advances improve performance in specialized protein fitness prediction tasks.
In this study, we expand upon the FLIP benchmark-designed for evaluating protein fitness prediction models in small, specialized prediction tasks-by assessing the performance of state-of-the-art large protein language models, including ESM-2 and SaProt on the FLIP dataset. Unlike larger, more diverse benchmarks such as ProteinGym, which cover a broad spectrum of tasks, FLIP focuses on constrained settings where data availability is limited. This makes it an ideal framework to evaluate model performance in scenarios with scarce task-specific data. We investigate whether recent advances in protein language models lead to significant improvements in such settings. Our findings provide valuable insights into the performance of large-scale models in specialized protein prediction tasks.