Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations

Guerois, Raphael; Nielsen, Jens Erik; Serrano, Luis

doi:10.1016/s0022-2836(02)00442-4

Cited by 1,728Protein Structure and DynamicsRNA and protein synthesis mechanismsEnzyme Structure and Function

Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations

Guerois, Raphael, Nielsen, Jens Erik, Serrano, Luis·2002·Journal of Molecular Biology

DOI 10.1016/s0022-2836(02)00442-4

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Abstract

We have developed a computer algorithm, FOLDEF (for FOLD-X energy function), to provide a fast and quantitative estimation of the importance of the interactions contributing to the stability of proteins and protein complexes. The predictive power of FOLDEF was tested on a very large set of point mutants (1088 mutants) spanning most of the structural environments found in proteins. FOLDEF uses a full atomic description of the structure of the proteins. The different energy terms taken into account in FOLDEF have been weighted using empirical data obtained from protein engineering experiments. First, we considered a training database of 339 mutants in nine different proteins and optimised the set of parameters and weighting factors that best accounted for the changes in stability of the mutants. The predictive power of the method was then tested using a blind test mutant database of 667 mutants, as well as a database of 82 protein-protein complex mutants. The global correlation obtained for 95 % of the entire mutant database (1030 mutants) is 0.83 with a standard deviation of 0.81 kcal mol(-1) and a slope of 0.76. The present energy function uses a minimum of computational resources and can therefore easily be used in protein design algorithms, and in the field of protein structure and folding pathways prediction where one requires a fast and accurate energy function. FOLDEF is available via a web-interface at http://fold-x.embl-heidelberg.de

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Empirical energy-based methods usually take linear forms, often with only energy-based terms, and fail to exploit evolution information, which can limit their ability to capture mutation effects on binding affinity.
“They usually take a form of linear functions, often with only energy‐based terms, and fail to exploit evolution information, which can limit their ability to capture mutation effects on binding affinity.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [6]

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Authors: Guerois, R., Nielsen, J. E., & Serrano, L.
Affiliations: Howard Hughes Medical Institute (US)
European Molecular Biology Laboratory (DE)
ORCID: Nielsen, Jens Erik
Serrano, Luis
Venue: Journal of Molecular Biology
Publisher: Elsevier BV
Published: 2002-07
Volume: 320
Issue: 2
Pages: 369-387
ISSN: 0022-2836 (ISSN-L)
DOI: 10.1016/s0022-2836(02)00442-4
OpenAlex: W2092387745
Cited by: 1,728
References: 95
Citations by year: 2026: 68 · 2025: 85 · 2024: 61 · 2023: 78 · 2022: 74 · 2021: 90 · 2020: 98 · 2019: 82 · 2018: 90 · 2017: 84 · 2016: 94 · 2015: 93 · 2014: 90 · 2013: 102 · 2012: 87
FWCI: 12.09
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Molecular Biology (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
Keywords: protein stability prediction, point mutations, protein-protein complex, parameter optimization, protein design, protein folding pathways
MeSH terms: Algorithms, Hydrogen Bonding, Models, Genetic, Mutation, Protein Conformation, Proteins, Software, Thermodynamics, Water, Models, Statistical, Point Mutation, Protein Structure, Secondary, Protein Folding, Databases as Topic

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Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More Than 1000 Mutations

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