CEM 2.0: Co-evolution-guided mining of superior extremophilic proteins via a curated proteome database and protein language model.
- 1The State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- 2Carbon-Negative Synthetic Biology for Biomaterial Production from CO2 (CNSB), Asia Pacific Graduate Institute of Shanghai Jiao Tong University, Campus for Research Excellence and Technological Enterprise (CREATE), 1 CREATE Way, 138602, Singapore.
- 3The State Key Laboratory of Synergistic Chem-Bio Synthesis, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
Abstract
Extremophilic proteins offer immense potential for diverse applications; however, their discovery is hindered by the reliance on predefined templates and domain-specific knowledge. Therefore, new methods are required to mine extremophilic proteins without relying on existing templates, enabling a comprehensive exploration of functional extremophilic proteins across the entire protein universe. Here, we propose a co-evolution-guided protein mining framework, CEM 2.0 (Co-Evolution Mining 2.0), comprising three synergistic components: (1) an extensive and diverse extremophilic proteome database covering six extremophilic features, (2) a multidimensional evaluator for six extremophilic features, leveraging the protein language model ProteinBERT, and (3) an integrated and interactive mining pipeline equipped with advanced tools for computing >20 properties of proteins. This framework was implemented on an interactive web platform (https://cem.sjtu.edu.cn/) equipped with intuitive visualization tools. We conducted wet-lab validation, performed a series of computational validations using published studies, and made recommendations for novel PETases, thereby demonstrating the effectiveness and potential of CEM 2.0 for the template-independent discovery of extremophilic proteins. CEM 2.0 is expected to mine functional extremophilic proteins across the protein universe in terms of evolutionary distance and serve as a valuable tool for protein engineering and synthetic biology.
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BibTeX
@article{zhang2026coevolutionguided,
title = {{CEM 2.0: Co-evolution-guided mining of superior extremophilic proteins via a curated proteome database and protein language model.}},
author = {Zhendong Zhang and Lina Ma and Mingyang Ding and Ping Xu and Fei Tao},
journal = {Engineering microbiology},
year = {2026},
month = aug,
doi = {10.1016/j.engmic.2026.100301},
url = {https://europepmc.org/article/MED/42910295},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - CEM 2.0: Co-evolution-guided mining of superior extremophilic proteins via a curated proteome database and protein language model.
AU - Zhendong Zhang
AU - Lina Ma
AU - Mingyang Ding
AU - Ping Xu
AU - Fei Tao
PY - 2026
DA - 2026/08/21
JO - Engineering microbiology
DO - 10.1016/j.engmic.2026.100301
UR - https://europepmc.org/article/MED/42910295
AB - Extremophilic proteins offer immense potential for diverse applications; however, their discovery is hindered by the reliance on predefined templates and domain-specific knowledge. Therefore, new methods are required to mine extremophilic proteins without relying on existing templates, enabling a comprehensive exploration of functional extremophilic proteins across the entire protein universe. Here, we propose a co-evolution-guided protein mining framework, CEM 2.0 (Co-Evolution Mining 2.0), comprising three synergistic components: (1) an extensive and diverse extremophilic proteome database covering six extremophilic features, (2) a multidimensional evaluator for six extremophilic features, leveraging the protein language model ProteinBERT, and (3) an integrated and interactive mining pipeline equipped with advanced tools for computing >20 properties of proteins. This framework was implemented on an interactive web platform (https://cem.sjtu.edu.cn/) equipped with intuitive visualization tools. We conducted wet-lab validation, performed a series of computational validations using published studies, and made recommendations for novel PETases, thereby demonstrating the effectiveness and potential of CEM 2.0 for the template-independent discovery of extremophilic proteins. CEM 2.0 is expected to mine functional extremophilic proteins across the protein universe in terms of evolutionary distance and serve as a valuable tool for protein engineering and synthetic biology.
SN - 2667-3703
ER -