SOLart: a structure-based method to predict protein solubility and aggregation
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Abstract
MOTIVATION: The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools. RESULTS: We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists. AVAILABILITY AND IMPLEMENTATION: The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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To predict the solubility and aggregation propensity of protein, several prediction tools have been developed, such as Camsol and SOLart, with SOLart currently holding state-of-the-art performance.
“To predict the solubility and aggregation propensity of protein, several prediction tools have been developed, such as the Camsol [25], and SOLart [26], with SOLart currently holding state-of-the-art performance.”
From Computational and artificial intelligence-based methods for antibody development (2023) · cites this paper as [26]
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SOuLMuSiC, a novel tool for predicting the impact of mutations on protein solubility
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Decoding Solubility Signatures from Amyloid Monomer Energy Landscapes
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Targeting Efficient Features of Urate Oxidase to Increase Its Solubility
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SurfSol: A Multimodal Surface-Based Deep Learning Framework for Protein Solubility Prediction
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Challenges in Peptide Solubilization – Amyloids Case Study
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Recent advances in the integration of protein mechanics and machine learning
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MMSol: Predicting Protein Solubility with an Antinoise Multimodal Deep Model
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Artificial Intelligence and Machine Learning Approaches in Designing Immunotherapy in Cancer
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Software Tools for Optimization of Biocatalysts
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SOuLMuSiC: A Computational Tool for Predicting the Impact of Mutations on Protein SolubilityPreprint
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SRPM-Sol: A Structure Robust Protein Multimodal Model for Solubility Prediction
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PInteract: Detecting Aromatic-Involving Motifs in Proteins and Protein-Nucleic Acid Complexes
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ProtSATT: An Advanced Protein Solubility Predictor Based on Attention Mechanism
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Machine learning in prediction of protein aggregation and/or condensation states
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- Authors
- Hou, Q., Kwasigroch, J. M., Rooman, M., & Pucci, F.
- Affiliations
- Université Libre de Bruxelles (BE)
- Corresponding author
- Rooman, Marianne
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-10-11
- Volume
- 36
- Issue
- 5
- Pages
- 1445-1452
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2979986977
- Cited by
- 86
- References
- 54
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 15 · 2023: 13 · 2022: 8 · 2021: 13 · 2020: 7 · 2019: 2
- FWCI
- 2.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion, other-oa)
- Funders
- FNRS
- John von Neumann Institute for Computing
- NIC
- 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
- protein solubility prediction, protein aggregation, residue-residue interactions, backbone torsion angles, solvent accessibility, random forest, folding free energy, Escherichia coli proteins, web server
- MeSH terms
- Solubility, Solvents, Computational Biology, Escherichia coli Proteins
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