ArticlePLoS computational biology2020
Insight into the protein solubility driving forces with neural attention.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 64 citations in OpenAlex.
- Integrating multimodal features with deep learning for protein solubility prediction.Journal of cheminformatics · 2026Article
- Strategies for enhancing protein solubility: methods, applications, and prospects.NPJ science of food · 2026Review
- Deep learning on protein language model embeddings unlocks accurate prediction of protein solubility.Frontiers in microbiology · 2026Article
- SOuLMuSiC, a novel tool for predicting the impact of mutations on protein solubility.Scientific reports · 2025Article
- FINCHES: A Computational Framework for Predicting Intermolecular Interactions in Intrinsically Disordered Proteins.International journal of molecular sciences · 2025Review
- GRACE: Generative Redesign in Artificial Computational Enzymology.ACS synthetic biology · 2024Article
- DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024Article
- PLM_Sol: predicting protein solubility by benchmarking multiple protein language models with the updated Escherichia coli protein solubility dataset.Briefings in bioinformatics · 2024Article
- Stability of Protein Pharmaceuticals: Recent Advances.Pharmaceutical research · 2024Review
- Advanced computational approaches to understand protein aggregation.Biophysics reviews · 2024Review
- CysPresso: a classification model utilizing deep learning protein representations to predict recombinant expression of cysteine-dense peptides.BMC bioinformatics · 2023Article
- A Novel Strategy to Identify Endolysins with Lytic Activity against Methicillin-ResistantInternational journal of molecular sciences · 2023Article
- Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE.BMC biology · 2023Article
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
- From genotype to phenotype in Arabidopsis thaliana: in-silico genome interpretation predicts 288 phenotypes from sequencing data.Nucleic acids research · 2022Article
- PyUUL provides an interface between biological structures and deep learning algorithms.Nature communications · 2022Article
- Editorial: Towards genome interpretation: Computational methods to model the genotype-phenotype relationship.Frontiers in bioinformatics · 2022Article
- Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.BMC biology · 2021Article
- Review
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein solubility is a key aspect for many biotechnological, biomedical and industrial processes, such as the production of active proteins and antibodies. In addition, understanding the molecular determinants of the solubility of proteins may be crucial to shed light on the molecular mechanisms of diseases caused by aggregation processes such as amyloidosis. Here we present SKADE, a novel Neural Network protein solubility predictor and we show how it can provide novel insight into the protein solubility mechanisms, thanks to its neural attention architecture. First, we show that SKADE positively compares with state of the art tools while using just the protein sequence as input. Then, thanks to the neural attention mechanism, we use SKADE to investigate the patterns learned during training and we analyse its decision process. We use this peculiarity to show that, while the attention profiles do not correlate with obvious sequence aspects such as biophysical properties of the aminoacids, they suggest that N- and C-termini are the most relevant regions for solubility prediction and are predictive for complex emergent properties such as aggregation-prone regions involved in beta-amyloidosis and contact density. Moreover, SKADE is able to identify mutations that increase or decrease the overall solubility of the protein, allowing it to be used to perform large scale in-silico mutagenesis of proteins in order to maximize their solubility.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.