A review on computational models for predicting protein solubility.

Teerapat Pimtawong, Jun Ren, Jingyu Lee, Hyang-Mi Lee, Dokyun Na

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In one paragraph

Review in Journal of microbiology (Seoul, Korea), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Teerapat PimtawongDepartment of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.

Jun RenDepartment of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.

Jingyu LeeDepartment of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.

Hyang-Mi LeeDepartment of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.

Dokyun NaDepartment of Biomedical Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.

Funding

Chung-Ang University Young Scientist ScholarshipNational Research Foundation of Korea 2022M3 A9B6082687National Research Foundation of Korea 2023R1A2C1008156

6 · The paper itself

Abstract

Protein solubility is a critical factor in the production of recombinant proteins, which are widely used in various industries, including pharmaceuticals, diagnostics, and biotechnology. Predicting protein solubility remains a challenging task due to the complexity of protein structures and the multitude of factors influencing solubility. Recent advances in computational methods, particularly those based on machine learning, have provided powerful tools for predicting protein solubility, thereby reducing the need for extensive experimental trials. This review provides an overview of current computational approaches to predict protein solubility. We discuss the datasets, features, and algorithms employed in these models. The review aims to bridge the gap between computational predictions and experimental validations, fostering the development of more accurate and reliable solubility prediction models that can significantly enhance recombinant protein production.

Indexed as

Computational BiologyProteinsRecombinant ProteinsAlgorithmsComputer SimulationMachine LearningProtein ConformationSolubilityProteinsRecombinant ProteinsBiotechnologyMachine learningProtein solubilityRecombinant proteinSolubility prediction

Identifiers

PMID39895070

DOI10.71150/jm.2408001

PMCPMC13576662

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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.