ArticlePLoS computational biology2014
Stability curve prediction of homologous proteins using temperature-dependent statistical potentials.
Article in PLoS computational biology, 2014. 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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19 citing papers in PubMed.
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Predicting absolute protein folding stability using generative models.Protein science : a publication of the Protein Society · 2025Article
- High-throughput system for the thermostability analysis of proteins.Protein science : a publication of the Protein Society · 2024Article
- Living in trinity of extremes: Genomic and proteomic signatures of halophilic, thermophilic, and pH adaptation.Current research in structural biology · 2024Article
- Multi-epitope vaccine candidates based on mycobacterial membrane protein large (MmpL) proteins againstOpen biology · 2023Article
- ProTstab2 for Prediction of Protein Thermal Stabilities.International journal of molecular sciences · 2022Article
- Intrinsically disordered protein, DNA binding with one finger transcription factor (Frontiers in plant science · 2022Article
- Identification of potential candidate vaccines againstFrontiers in immunology · 2022Article
- Characterization of a Stable Form of Carboxypeptidase G2 (Glucarpidase), a Potential Biobetter Variant, From Acinetobacter sp. 263903-1.Molecular biotechnology · 2021Article
- Structural and thermodynamic analysis of factors governing the stability and thermal folding/unfolding of SazCA.PloS one · 2021Article
- A sequence embedding method for enzyme optimal condition analysis.BMC bioinformatics · 2020Article
- Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine.Computational and structural biotechnology journal · 2020Review
- Considerations on the Rational Design of Covalently Conjugated Cell-Penetrating Peptides (CPPs) for Intracellular Delivery of Proteins: A Guide to CPP Selection Using Glucarpidase as the Model Cargo Molecule.Molecules (Basel, Switzerland) · 2019Article
- ProTstab - predictor for cellular protein stability.BMC genomics · 2019Article
- Proteome-wide Analysis of Protein Thermal Stability in the Model Higher PlantMolecular & cellular proteomics : MCP · 2019Article
- Computational analysis of the amino acid interactions that promote or decrease protein solubility.Scientific reports · 2018Article
- A quantitative model for oxygen uptake and release in a family of hemeproteins.Bioinformatics (Oxford, England) · 2016Article
- Predicting protein thermal stability changes upon point mutations using statistical potentials: Introducing HoTMuSiC.Scientific reports · 2016Article
- The hydrophobic temperature dependence of amino acids directly calculated from protein structures.PLoS computational biology · 2015Article
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2 authors.
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Abstract
The unraveling and control of protein stability at different temperatures is a fundamental problem in biophysics that is substantially far from being quantitatively and accurately solved, as it requires a precise knowledge of the temperature dependence of amino acid interactions. In this paper we attempt to gain insight into the thermal stability of proteins by designing a tool to predict the full stability curve as a function of the temperature for a set of 45 proteins belonging to 11 homologous families, given their sequence and structure, as well as the melting temperature (Tm) and the change in heat capacity (ΔCP) of proteins belonging to the same family. Stability curves constitute a fundamental instrument to analyze in detail the thermal stability and its relation to the thermodynamic stability, and to estimate the enthalpic and entropic contributions to the folding free energy. In summary, our approach for predicting the protein stability curves relies on temperature-dependent statistical potentials derived from three datasets of protein structures with targeted thermal stability properties. Using these potentials, the folding free energies (ΔG) at three different temperatures were computed for each protein. The Gibbs-Helmholtz equation was then used to predict the protein's stability curve as the curve that best fits these three points. The results are quite encouraging: the standard deviations between the experimental and predicted Tm's, ΔCP's and folding free energies at room temperature (ΔG25) are equal to 13° C, 1.3 kcal/(mol° C) and 4.1 kcal/mol, respectively, in cross-validation. The main sources of error and some further improvements and perspectives are briefly discussed.
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