BiologybioRxiv
Ranked by formulaVerdict: NotableProtify: A Benchmarking Platform Revealing Task Dependence and Homology Leakage in Protein Language Model Evaluation
Protein Language Models (pLMs) have become essential tools for computational protein analysis, and the rapid proliferation of architectures, training objectives, and pretraining corpora has produced a diverse and…
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
Protein Language Models (pLMs) have become essential tools for computational protein analysis, and the rapid proliferation of architectures, training objectives, and pretraining corpora has produced a diverse and expanding model landscape. Evaluation practices, however, remain fragmented: studies use different datasets, preprocessing pipelines, and reporting conventions, so results rarely transfer between publications. Here we introduce Protify, an open-source platform that standardizes pLM benchmarking across zero-shot, supervised, and transferlearning settings through a unified, low-code interface. Protify currently supports 35 pretrained models and 61 curated datasets and it is easy to add custom versions of each. Leveraging Protify's high throughput pipelines, we benchmarked 23 models on ProteinGym, discovering that DPLM3B is currently the top-performing self-supervised encoder. Another large-scale supervised evaluation across 32 diverse tasks, spanning enzyme classification, gene ontology annotation, protein-protein interaction, subcellular localization, and other functional properties, found that no single pLM dominated: 14 different models took the top score on at least one dataset, and the margins between the best and second-best models fell within run-to-run variability. On enzyme optimal pH prediction, a general-purpose probe pipeline matched or exceeded EpHod, a specialized model selected from 11,550 training runs, after 50 hyperparameter trials (R2 = 0.482 versus 0.425). Under a homology-controlled split of the same data, every pooled-embedding probe collapsed to near-zero R2, and only a transformer probe over full residue embeddings recovered appreciable signal (R2 = 0.248). Model rankings therefore depend as much on evaluation design as on the models themselves. Protify makes those design choices explicit, controlled, and replayable, and is freely available at https://github.com/Gleghorn-Lab/Protify.
The editor's rubric
- Leverage5 / 5
- A new primitive or platform that reorganizes fields (the Transformer, CRISPR-Cas9, the mRNA-LNP platform).
- Magnitude3 / 5
- Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
- Evidence3 / 5
- Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
- Novelty2 / 5
- A new combination of known ideas.
- Trajectory3 / 5
- A clear path to scale.
- Stakes2 / 5
- Benefits a professional community (practitioners, clinicians, engineers).
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose); breadth (general-purpose, many tasks, zero/few-shot); gains (versus baseline, outperforms); verification (multiple benchmarks, code released); scale (scalable).
How the score was computed
Desks place stronger verdicts first, then use this score to order work within the same verdict.
- Merit
- 6.4 / 10
- Adjusted merit
- 5.1 / 10
- Attention
- 0%
- Freshness
- 88%