Mutexa: A Computational Ecosystem for Intelligent Protein Engineering
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Abstract
Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.
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Self-citationHaataja, T., Gado, J. E., Nutt, A., Anderson, N. T., Nilsson, M., Momeni, M. H., Isaksson, R., Väljamäe, P., JOHANSSON, G. G., Payne, C. M., et al. (2022) · FEBS Journal · doi:10.1111/febs.16602 · Cited by 12 · see citing works
Jiang, Y., Yan, B., Chen, Y., Juarez, R. J., & Yang, Z. J. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c00142 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
2 of the 207 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Unlocking the potential of enzyme engineering via rational computational design strategies
Zhou, L., Tao, C., Shen, X., Sun, X., Wang, J., & Yuan, Q. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108376 · Cited by 93 · see citing works
Son, A., Park, J., Kim, W., Yoon, Y., Lee, S., Park, Y., & Kim, H. (2024) · Molecules · doi:10.3390/molecules29194626 · Cited by 42 · see citing works
Ran, X., Jiang, Y., Shao, Q., & Yang, Z. J. (2023) · Chemical Science · doi:10.1039/d3sc02752j · Cited by 25 · see citing works
Self-citationShao, Q., Hollenbeak, A. C., Jiang, Y., Ran, X., Bachmann, B. O., & Yang, Z. J. (2025) · Chem Catalysis · doi:10.1016/j.checat.2025.101334 · Cited by 19 · see citing works
Self-citationLi, Y., Li, F., Duan, Z., Liu, R., Jiao, W., Wu, H., Zhu, F., & Xue, W. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae893 · Cited by 11 · see citing works
Finding the dark matter: Large language model‐based enzyme kinetic data extractor and its validation
Wei, G., Ran, X., AI‐Abssi, R., & Yang, Z. (2025) · Protein Science · doi:10.1002/pro.70251 · Cited by 10 · see citing works
Self-citationZheng, M., Zhu, X., Li, Y., Zhang, Q., Dong, W., & Wang, W. (2024) · ACS ES&T Engineering · doi:10.1021/acsestengg.4c00253 · Cited by 9 · see citing works
Computational Studies of Enzymes for C−F Bond Degradation and Functionalization
Cunningham, K. M., Shin, W., & Yang, Z. J. (2025) · ChemPhysChem · doi:10.1002/cphc.202401130 · Cited by 7 · see citing works
Self-citationOuyang, X., Liu, G., Guo, L., Wu, G., Xu, P., Zhao, Y., & Tang, H. (2024) · Applied and Environmental Microbiology · doi:10.1128/aem.02255-23 · Cited by 6 · see citing works
QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures
Kastner, D. W., Luo, W., Ho, W., Reinhardt, C. R., Keys, A. M., & Kulik, H. J. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03064 · Cited by 5 · see citing works
Enhancing Cold Adaptation of Bidomain Amylases by High‐Throughput Computational Engineering
Ding, N., Jiang, Y., Ge, R., Shao, Q., Shin, W., Ran, X., & Yang, Z. J. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202505991 · Cited by 5 · see citing works
Self-citationLinker‐mediated domain separation enhances cold adaptation in cellulases
Ge, R., Ding, N., Jiang, Y., & Yang, Z. J. (2025) · Protein Science · doi:10.1002/pro.70198 · Cited by 5 · see citing works
Self-citationGeronimo, B. D., Mandl, Š., Alonso‐Gil, S., Žagrović, B., Reibnegger, G. J., Nusshold, C., & Sánchez‐Murcia, P. A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02229 · Cited by 4 · see citing works
Xiong, J., Wu, S.-Z., Liang, Z.-Q., Fang, S., Tao, F.-Y., Gong, X., Wu, Q., Cui, J., Gao, K., Luo, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.31.630886 · Cited by 4 · see citing works
Iterative acylation on mature lasso peptides by widespread acetyltransferases
Xiong, J., Wu, S., Liang, Z.-Q., Fang, S., Tao, F.-Y., Gong, X., Wu, X., Wu, Q., Cui, J., Gao, K., et al. (2026) · Nature Chemical Biology · doi:10.1038/s41589-026-02149-6 · Cited by 3 · see citing works
Han, H., Zhang, Q., Chen, Y., Wang, Z.-X., Su, X.-Y., Zhu, Y., Wang, X., & Feng, Z.-X. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.152018 · Cited by 2 · see citing works
Liu, J., Mei, D., Zhang, X.-J., Zhang, W.-G., & Zhu, L.-B. (2026) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2026.01.003 · Cited by 1 · see citing works
Wappett, D. A., Cheng, Q., Summers, T. J., Santaloci, T. J., Agbaglo, D. A., Suhagia, T. A., Banos, M. P., & DeYonker, N. J. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70078 · Cited by 1 · see citing works
Pattani, V. B., Kaneriya, J. P., Joshi, K., & Sanghvi, G. V. (2026) · Results in Chemistry · doi:10.1016/j.rechem.2026.103712 · Cited by 1 · see citing works
Enhance Cold Adaptation of Bidomain Amylases via High-throughput Computational EngineeringPreprint
Ding, N., Jiang, Y., Ge, R., Ran, X., & Yang, Z. J. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-rstbz · Cited by 1 · see citing works
Self-citationSingh, A., Maheshwari, S., Prajapati, J. B., Akhtar, J., Hasan, S. S., Verma, A., Singh, S. K., & Prajapati, B. G. (2025) · doi:10.1002/9781394242474.ch12 · Cited by 1 · see citing works
Kumar, Y., & Kumar, S. (2024) · International Journal of Pharma Professional’s Research (IJPPR) · doi:10.69580/ijppr.15.3.2024.52-67
Why insulin aspart and insulin degludec exhibit distinct release mechanisms
Li, Z., Feng, Y. H., Jiao, J., Ju, X., Yu, L., Zhang, G. L., Yu, R., Chen, B. Z., & Guo, X. D. (2024) · AIChE Journal · doi:10.1002/aic.18609
Enhancing Cold Adaptation of Bidomain Amylases by High‐Throughput Computational Engineering
Ding, N., Jiang, Y., Ge, R., Shao, Q., Shin, W., Ran, X., & Yang, Z. J. (2025) · Angewandte Chemie · doi:10.1002/ange.202505991
Self-citationSpirocyclic β-lactone secondary metabolites modulate spliceosome function
Penton, K. E., Bates, S., Thirman, H. L., Cameron, C. D., Jurich, C. P., Hayes, M., Jiang, M., Savona, M. R., Yang, Z. J., Plate, L., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2518221123
Self-citationProtein Engineering and Directed Evolution
Abey, E. E., Mathew, A. E., Veettil, A. H. S., & Arivarasan, V. K. (2026) · Springer eBooks · doi:10.1007/978-981-95-3957-4_18
Liu, H. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109222
Residue-Position-Dependent Modulation of Urethanase Activity for Polyurethane Depolymerization
Zheng, M.-N., Liu, J.-W., Zhu, X.-M., Liang, C.-H., Li, Z.-Q., Zhang, Q.-Z., Zhang, W.-X., Wang, W.-X., Wei, R., Dong, W.-L., et al. (2026) · ACS Catalysis · doi:10.1021/acscatal.6c04525
- Authors
- Yang, Z. J., Shao, Q., Jiang, Y., Jurich, C., Ran, X., Juarez, R. J., Yan, B., Stull, S. L., Gollu, A., & Ding, N.
- Affiliations
- Vanderbilt University (US)
- Corresponding author
- Yang, Zhongyue J.
- ORCID
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-10-13
- Volume
- 19
- Issue
- 21
- Pages
- 7459-7477
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- PMC
- PMC10653112
- OpenAlex
- W4387600363
- Cited by
- 28
- References
- 207
- Citations by year
- 2026: 10 · 2025: 9 · 2024: 8 · 2023: 1
- FWCI
- 0.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Theory and Computation (publishedVersion, cc-by-nc-nd)
- ChemRxiv (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc-nd)
- Funders
- Vanderbilt University
- National Institute of General Medical Sciences (R35GM146982)
- Rosetta Commons
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Enzyme Catalysis and Immobilization
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein engineering, enzyme design, sequence-structure-function relationships, SAM-dependent methyltransferase, protein electrostatics, protein cavities, peptide therapeutics, diagnostic proteins, biocatalysts
- MeSH terms
- Proteins, Protein Engineering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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