ProTherm, version 4.0: thermodynamic database for proteins and mutants

By K. Bava, M. Michael Gromiha, H. Uedaira, K. Kitajima, Akinori Sarai. 2004

387 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. David B. Ascher
2. Douglas E. V. Pires
3. Jesse D. Bloom
4. Matthew J. Glassman
5. Emidio Capriotti
6. Rita Casadio
7. Piero Fariselli
8. Petras J. Kundrotas

In Molecular Biology

1. David B. Ascher
2. Douglas E. V. Pires
3. Emidio Capriotti
4. Jesse D. Bloom
5. Matthew J. Glassman
6. Piero Fariselli
7. Rita Casadio
8. Petras J. Kundrotas

Citations in the corpus

• Predicting protein stability changes from sequences using support vector machines (2005, positive): “Other relevant thermodynamic parameters in mutagenesis are experimental conditions such as pH and temperature (Bava et al., 2004).”
• PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality (2011, methodology): “The data was originally extracted from the the ProTherm database [39], and thoroughly checked to correct or eliminate erroneous inputs.”
• Fast and flexible design of novel proteins using graph neural networks (2019, result): “…to reconstruct protein sequences, show a significant correlation with experimentally measured changes in protein stability reported in Protherm ( 31 ) (Spearman ⍴: 0.44; p < 0.001) and, in fact, show a stronger correlation than predictions made using Rosetta's fixbb and ddg_monomer protocols…”
• ProTherm and ProNIT: thermodynamic databases for proteins and protein–nucleic acid interactions (2005, background): “0 contains 17 113 entries from 771 proteins, retrieved from 1497 scientific articles, which is 20% increase in data from the last version (4).”
• TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions (2017, methodology): “All thermodynamic data are obtained from the ProTherm database.(132) A comparison of the performance of various methods is summarized in Table 4.”
• I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure (2005, methodology): “0 was trained and tested with a cross-validation procedure on a data set derived from the current release (December 2004) of the Thermodynamic Database for Proteins and Mutants [ProTherm, (7)].”
• Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, methodology): “The dataset used in this work was derived from ProTherm [64], and links experimentally measured thermodynamics effects of missense mutations to a diverse set of protein structures.”
• Pi-Pi contacts are an overlooked protein feature relevant to phase separation (2018, background): “…of mutations, the DDG of unfolding, using both FOLDX (Schymkowitz et al., 2005) and Rosetta (Kellogg et al., 2011), shows decreased prediction accuracy at positions involved in pi-contacts (Appendix 1—figure 3C-F), based on comparison to a reference set of DDG measurements (Bava et al., 2004).”
• Inferring Stabilizing Mutations from Protein Phylogenies: Application to Influenza Hemagglutinin (2009, methodology): “We examined the ProTherm [82] database, and found that the proteins with the most DDG values were bacteriophage T4 lysozyme, sperm whale myoglobin, Bacillus amyloliquefaciens barnase, Bacillus subtilis cold shock protein, Escherichia coli ribonuclease HI, and E. coli thioredoxin.”
• Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, methodology): “The set is derived from the ProTherm database (65).”