ESM ‐scan—A tool to guide amino acid substitutions
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Abstract
Protein structure prediction and (re)design have gone through a revolution in the last 3 years. The tremendous progress in these fields has been almost exclusively driven by readily available machine learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site‐specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge, mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are hard to capture with computational models. Here, we describe the implementation and use of ESM‐Scan, which uses the ESM zero‐shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in silico deep mutational scanning experiments. We benchmark ESM‐Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally testing the tool's performance on a challenging test case of a blue‐light‐activated diguanylate cyclase from Methylotenera species ( Ms LadC), where it accurately predicted the importance of a highly conserved residue in a region involved in allosteric product inhibition. Our experimental results show that the ESM‐zero shot model is capable of inferring the effects of a set of amino acid substitutions in their correlation between predicted fitness and experimental results. ESM‐Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp .
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Vide, U., Kasapović, D., Fuchs, M., Heimböck, M. P., Totaro, M. G., Zenzmaier, E., & Winkler, A. (2023) · Science Advances · doi:10.1126/sciadv.adh4721 · Cited by 16 · see citing works
Self-citationZhang, M., Case, D. A., & Peng, J. W. (2018) · Structure · doi:10.1016/j.str.2018.07.014 · Cited by 15 · see citing works
Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins
Yamaguchi, H., & Saitō, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab234 · Cited by 15 · see citing works
Smirnova, A., Miroshnichenkova, A. M., Olshanskaya, Y. V., Maschan, M. A., Lebedev, Y. B., Chudakov, D. M., Mamedov, I. Z., & Komkov, A. Y. (2023) · eLife · doi:10.7554/elife.69157 · Cited by 11 · see citing works
Searching similar local 3D micro-environments in protein structure databases with MicroMiner
Sieg, J., & Rarey, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad357 · Cited by 9 · see citing works
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
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From traditional to AI-driven: The evolution of intelligent enzyme engineering for biocatalysis
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Click, Compute, Create: A Review of Web‐based Tools for Enzyme Engineering
Tripp, A., Braun, M., Wieser, F., Oberdorfer, G., & Lechner, H. (2024) · ChemBioChem · doi:10.1002/cbic.202400092 · Cited by 15 · see citing works
Self-citationZhao, S., Xie, X., Yang, Y., Yu, H. B., Wang, J., Luo, L., & Zhou, X. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c05803 · Cited by 3 · see citing works
Li, J., Wang, Y., Hu, H., Guo, F., Jiang, W., Jiang, Y., Xin, F., Wang, Y., Zhang, W., & Min, J. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.5c06887 · Cited by 3 · see citing works
Li, W., Chen, J., Liu, C., Wang, Q., Wang, Y., Clark, A. G., & Lü, J. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117642 · Cited by 2 · see citing works
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Self-citationEder, O. M., Totaro, M. G., Minnich, S. M., Oberdorfer, G., & Winkler, A. (2025) · Structure · doi:10.1016/j.str.2025.07.018 · Cited by 1 · see citing works
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Sharma, V., Sharma, R., & Moliszewska, E. B. (2026) · Springer eBooks · doi:10.1007/978-981-95-4941-2_8
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Expanding the galactose oxidase substrate repertoire via a multi-modal computational strategy
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- Authors
- Totaro, M. G., Vide, U., Zausinger, R., Winkler, A., & Oberdorfer, G.
- Affiliations
- Graz University of Technology (AT)
- Corresponding author
- Oberdorfer, Gustav
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2024-11-20
- Volume
- 33
- Issue
- 12
- Pages
- e5221-e5221
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC11577456
- OpenAlex
- W4404536882
- Cited by
- 19
- References
- 98
- Citations by year
- 2026: 11 · 2025: 6 · 2024: 2
- FWCI
- 2.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Protein Science (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- H2020 European Research Council (802217)
- Austrian Science Fund
- Amt der Steiermärkischen Landesregierung
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- deep mutational scanning, amino acid substitutions, protein stability prediction, protein function prediction, site-directed mutagenesis, diguanylate cyclase, conserved residues, fitness prediction
- MeSH terms
- Machine Learning, Algorithms, Bacterial Proteins, Models, Molecular, Protein Conformation, Software, Amino Acid Substitution, Protein Stability
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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