ArticleBioinformatics (Oxford, England)2018
PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- Accurate Prediction for Antibody Resistance of Clinical HIV-1 Isolates.Scientific reports · 2019Trial
- Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 2026Review
- Strategies for enhancing protein solubility: methods, applications, and prospects.NPJ science of food · 2026Review
- MiLaSol: modeling protein solubility by mixing up multiple protein language models.Bioinformatics advances · 2026Article
- Deep learning on protein language model embeddings unlocks accurate prediction of protein solubility.Frontiers in microbiology · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks.ACS omega · 2025Article
- Benchmarking protein language models for protein crystallization.Scientific reports · 2025Article
- A review on computational models for predicting protein solubility.Journal of microbiology (Seoul, Korea) · 2025Review
- ProSol-multi: Protein solubility prediction via amino acids multi-level correlation and discriminative distribution.Heliyon · 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
- VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction.Briefings in bioinformatics · 2024Article
- SignalP: The Evolution of a Web Server.Methods in molecular biology (Clifton, N.J.) · 2024Article
- HybridGCN for protein solubility prediction with adaptive weighting of multiple features.Journal of cheminformatics · 2023Article
- AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for monkeypox virus.Journal of infection and public health · 2023Article
- Serverless Prediction of Peptide Properties with Recurrent Neural Networks.Journal of chemical information and modeling · 2023Article
- An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling.Computational and structural biotechnology journal · 2023Review
- Accelerating therapeutic protein design with computational approaches toward the clinical stage.Computational and structural biotechnology journal · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Motivation: Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought. Results: In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthew's correlation coefficient, with an overall accuracy of 74% and Matthew's correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability. Availability and implementation: PaRSnIP software is available for download under GitHub (https://github.com/RedaRawi/PaRSnIP). Contact: gwo-yu.chuang@nih.gov. Supplementary information: Supplementary data are available at Bioinformatics online.
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Registered trials
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