VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data.
10.1016/j.apsb.2025.03.028
- Author:
Liang ZHANG
1
;
Hua PANG
2
;
Chenghao ZHANG
3
;
Song LI
1
;
Yang TAN
1
;
Fan JIANG
1
;
Mingchen LI
1
;
Yuanxi YU
1
;
Ziyi ZHOU
1
;
Banghao WU
1
;
Bingxin ZHOU
1
;
Hao LIU
4
;
Pan TAN
1
;
Liang HONG
1
Author Information
1. School of Physics and Astronomy & Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai National Centre for Applied Mathematics (SJTU Center), MOE-LSC, Shanghai 200240, China.
2. iHuman Institute and School of Life Sciences and Technology, Shanghaitech University, Shanghai 201210, China.
3. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
4. Shanghai Matwings Technology Co., Ltd., 200240 Shanghai, China.
- Publication Type:Journal Article
- Keywords:
Activity;
Benchmark;
Binding affinity;
Mutation effect prediction;
Protein engineering;
Selectivity;
Small-scale experimental data;
Stability
- From:
Acta Pharmaceutica Sinica B
2025;15(5):2454-2467
- CountryChina
- Language:English
-
Abstract:
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.
