VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915189414166528 |
|---|---|
| author | Zhang, Liang Pang, Hua Zhang, Chenghao Li, Song Tan, Yang Jiang, Fan Li, Mingchen Yu, Yuanxi Zhou, Ziyi Wu, Banghao Zhou, Bingxin Liu, Hao Tan, Pan Hong, Liang |
| author_facet | Zhang, Liang Pang, Hua Zhang, Chenghao Li, Song Tan, Yang Jiang, Fan Li, Mingchen Yu, Yuanxi Zhou, Ziyi Wu, Banghao Zhou, Bingxin Liu, Hao Tan, Pan Hong, Liang |
| contents | In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (~10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data Zhang, Liang Pang, Hua Zhang, Chenghao Li, Song Tan, Yang Jiang, Fan Li, Mingchen Yu, Yuanxi Zhou, Ziyi Wu, Banghao Zhou, Bingxin Liu, Hao Tan, Pan Hong, Liang Quantitative Methods In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (~10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial. |
| title | VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2503.04851 |