rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments
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Abstract
In the last decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed taking advantage of these new architectures. However, most methods are still heavily based pre-processing of the input data, as well as extraction and integration of multiple hand-picked, and manually designed features. Multiple Sequence Alignments (MSA) are the most common source of information in de novo prediction methods. Deep Networks that automatically refine the MSA and extract useful features from it would be immensely powerful. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering pre-calculated features such as sequence profiles and other features calculated from MSA obsolete. We showcased the rawMSA methodology on three different prediction problems: secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and benchmarked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on par with methods using more pre-calculated features in the inter-residue contact map prediction category in CASP12 and CASP13. Clearly demonstrating that rawMSA represents a promising development that can pave the way for improved methods using rawMSA instead of sequence profiles to represent evolutionary information in the coming years. Availability: datasets, dataset generation code, evaluation code and models are available at: https://bitbucket.org/clami66/rawmsa.
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“One of the first examples of end-to-X method was rawMSA [48], which leveraged embedding techniques from the field of NLP, to map the amino acid residues into a continuous space adaptively learned based on the sequence context ( Table 1 ).”
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flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins
Wang, K., Hu, G., Basu, S., & Kurgan, L. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168605 · Cited by 31 · see citing works
Deep learning-empowered crop breeding: intelligent, efficient and promising
Wang, X., Zeng, H., Lin, L., Huang, Y., Lin, H., & Que, Y. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1260089 · Cited by 31 · see citing works
Protein Function Analysis through Machine Learning
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InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network
Johansson-Åkhe, I., Mirabello, C., & Wallner, B. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.763102 · Cited by 28 · see citing works
Self-citationResources for computational prediction of intrinsic disorder in proteins
Kurgan, L. (2022) · Methods · doi:10.1016/j.ymeth.2022.03.018 · Cited by 28 · see citing works
DR-BERT: A protein language model to annotate disordered regions
Nambiar, A., Forsyth, J. M., Liu, S., & Maslov, S. (2024) · Structure · doi:10.1016/j.str.2024.04.010 · Cited by 28 · see citing works
Han, B., Ren, C., Wang, W., Li, J., & Gong, X. (2023) · Genes · doi:10.3390/genes14020432 · Cited by 26 · see citing works
tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence HomologsPreprint
Wu, J., Wu, F., Jiang, B., Liu, W., & Zhao, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.10.515918 · Cited by 26 · see citing works
Best practices for machine learning in antibody discovery and development
Wossnig, L., Furtmann, N., Buchanan, A. G., Kumar, S., & Greiff, V. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104025 · Cited by 25 · see citing works
Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction
Genc, A. G., & McGuffin, L. J. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4196-5_7 · Cited by 22 · see citing works
Mehdiabadi, M., Conte, A. D., Nugnes, M. V., Aspromonte, M. C., Tosatto, S. C. E., & Piovesan, D. (2025) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.70045 · Cited by 19 · see citing works
Villalobos-Alva, J., Ochoa-Toledo, L., Villalobos-Alva, M. J., Aliseda, A., Escamirosa, F. P., Altamirano-Bustamante, N. F., Ochoa-Fernández, F., Zamora-Solís, R., Villalobos-Alva, S., Revilla, M., et al. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.788300 · Cited by 19 · see citing works
Artificial intelligence for template-free protein structure prediction: a comprehensive review
Mufassirin, M. M. M., Newton, M. A. H., & Sattar, A. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10350-x · Cited by 19 · see citing works
An outlook on structural biology after A lpha F old: tools, limits and perspectives
Rosignoli, S., Pacelli, M., Manganiello, F., & Paiardini, A. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13902 · Cited by 18 · see citing works
Tawfiq, R., Niu, K., Hoehndorf, R., & Kulmanov, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-82956-w · Cited by 17 · see citing works
Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences
Basu, S., Jing, Y., Kihara, D., & Kurgan, L. (2024) · Briefings Bioinform. · doi:10.1093/bib/bbaf016 · Cited by 15 · see citing works
Large language models transform biological research: from architecture to utilization
Wang, T., & Luo, Z. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4466-3 · Cited by 14 · see citing works
Basu, S., Hegedűs, T., & Kurgan, L. (2023) · Journal of Molecular Biology · doi:10.1016/j.jmb.2023.168272 · Cited by 14 · see citing works
DisPredict3.0: Prediction of intrinsically disordered regions/proteins using protein language model
Kabir, M. W. U., & Hoque, M. T. (2024) · Applied Mathematics and Computation · doi:10.1016/j.amc.2024.128630 · Cited by 13 · see citing works
Ghualm, A., Lei, X., Zhang, Y., Cheng, S., & Guo, M. (2020) · IEEE Access · doi:10.1109/access.2020.3027887 · Cited by 13 · see citing works
Deep learning model with ensemble techniques to compute the secondary structure of proteins
AlGhamdi, R. A., Aziz, A., Alshehri, M., Pardasani, K. R., & Aziz, T. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03467-9 · Cited by 11 · see citing works
DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction
Kotowski, K., Roterman, I., & Stąpor, K. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109586 · Cited by 10 · see citing works
learnMSA: learning and aligning large protein families
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Kandathil, S. M., Greener, J. G., Lau, A. M., & Jones, D. T. (2020) · doi:10.1101/2020.11.27.401232 · Cited by 8 · see citing works
Basu, S., & Kurgan, L. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.059 · Cited by 7 · see citing works
Nature Inspired Algorithms for Solving Multiple Sequence Alignment Problem: A Review
Paruchuri, T., Kancharla, G. R., Dara, S., Yadav, R. K., Jadav, S. S., Dhamercherla, S., & Vidyarthi, A. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09769-w · Cited by 7 · see citing works
Tang, Y.-J., Yan, K., Zhang, X., Tian, Y., & Liu, B. (2023) · BMC Biology · doi:10.1186/s12915-023-01672-5 · Cited by 6 · see citing works
Assessment of Disordered Linker Predictions in the CAID2 Experiment
Wang, K., Hu, G., Wu, Z., Uversky, V. N., & Kurgan, L. (2024) · Biomolecules · doi:10.3390/biom14030287 · Cited by 5 · see citing works
Decoding disorder: Advances in predicting intrinsically disordered proteins
Chaurasiya, D., & Lahiri, T. (2025) · Current Proteomics · doi:10.1016/j.curpro.2025.100030 · Cited by 5 · see citing works
LoRA-DR-suite: adapted embeddings predict intrinsic and soft disorder from protein sequences
Lombardi, G., Seoane, B., & Carbone, A. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf185 · Cited by 4 · see citing works
flDPnn3: Fast and Accurate Prediction of Intrinsic Disorder in Protein Sequences
Wang, K., Hu, G., Basu, S., & Kurgan, L. (2026) · Journal of Molecular Biology · doi:10.1016/j.jmb.2026.169629 · Cited by 4 · see citing works
Artificial Intelligence for Plant Genomics and Crop Improvement
Hatem, Y., Hammad, G., & Safwat, G. (2022) · Egyptian Journal of Botany · doi:10.21608/ejbo.2022.83200.1731 · Cited by 4 · see citing works
Overview Update: Computational Prediction of Intrinsic Disorder in Proteins
Uversky, V. N., & Kurgan, L. (2023) · Current Protocols · doi:10.1002/cpz1.802 · Cited by 4 · see citing works
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Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn
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Cognate Transformer for Automated Phonological Reconstruction and Cognate Reflex Prediction
Akavarapu, V. S. D. S. M., & Bhattacharya, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.423 · Cited by 3 · see citing works
Protein 3D structure prediction Using Homology Method
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Prediction of protein structure and intrinsic disorder in the era of deep learning
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A Survey on Sequence Alignment Algorithms and State-of-the-Art Aligners
Prousalis, K. P., Georgiou, K., Καλογερόπουλος, Α., Ntalaperas, D., Konofaos, N., Angelis, L., Papalitsas, C., Stavropoulos, T. G., & Gariboldi, N. (2025) · ACM Computing Surveys · doi:10.1145/3763229 · Cited by 2 · see citing works
Effect of Sequence Padding on the Performance of Protein-Based Deep Learning ModelsPreprint
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A Review of Protein Structure Prediction using Deep Learning
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A Progress on Protein Structure Prediction using Various Soft Computing Techniques
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Retracted on December 4, 2025: Sequence Alignment with Q-Learning Based on the Actor-Critic Model
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Development and Application of Computational Models for Peptide-Protein Complexes
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Faculty Opinions recommendation of Highly accurate protein structure prediction with AlphaFold.
Arulanandam, A. J. (2021) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.740477161.793587548
Faculty Opinions recommendation of Highly accurate protein structure prediction with AlphaFold.
Chakraborty, S. (2021) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.740477161.793587538
Predicting Protein Interaction Sites Using PITHIA
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Bioinformatic analysis of disordered proteins
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Study of the Variability of the Native Protein Structure
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Protein Structure Prediction by Artificial Intelligence
Li, J., & Han, Y. (2025) · Wiley eBooks · doi:10.1002/9783527842346.ch03
DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions
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Residue Level Analysis of IDRs, MoRFs, and LLPS in IA-2 for Type 1 Diabetes
Chaurasiya, D. (2025) · International Conference on Computational Intelligence and Communication Networks (CICN) · doi:10.1109/cicn67655.2025.11368130
- Authors
- Mirabello, C., & Wallner, B.
- Affiliations
- Linköping University (SE)
- Corresponding author
- Mirabello, Claudio
- Wallner, Björn
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-08-15
- Volume
- 14
- Issue
- 8
- Pages
- e0220182
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6695225
- OpenAlex
- W2969180072
- Cited by
- 105
- References
- 66
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 17 · 2023: 18 · 2022: 26 · 2021: 14 · 2020: 10 · 2019: 2
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DiVA (Linkoping University) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Vetenskapsrådet (2016-05369)
- Stiftelsen Blanceflor Boncompagni Ludovisi, född Bildt
- Nvidia Corporation
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- multiple sequence alignment, protein structure prediction, secondary structure prediction, solvent accessibility, sequence profiles, position weight matrices, de novo protein structure prediction
- MeSH terms
- Deep Learning, Proteins, Sequence Alignment, Computational Biology
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.
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