The FoldX web server: an online force field

By Joost Schymkowitz, Jesper Ferkinghoff-Borg, Francois Stricher, R. Nys, Frederic Rousseau, Luis Serrano. 2005

2,717 citations recorded by the source corpus.

FoldX is an empirical force field that was developed for the rapid evaluation of the effect of mutations on the stability, folding and dynamics of proteins and nucleic acids. The core functionality of FoldX, namely the calculation of the free energy of a macromolecule based on its high-resolution 3D structure, is now publicly available through a web server at . The current release allows the calculation of the stability of a protein, calculation of the positions of the protons and the prediction of water bridges, prediction of metal binding sites and the analysis of the free energy of complex formation. Alanine scanning, the systematic truncation of side chains to alanine, is also included. In addition, some reporting functions have been added, and it is now possible to print both the atomic interaction networks that constitute the protein, print the structural and energetic details of the interactions per atom or per residue, as well as generate a general quality report of the pdb structure. This core functionality will be further extended as more FoldX applications are developed.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Evolutionary context-integrated deep sequence modeling for protein engineering (2020, methodology): “Existing tools predict the conservation effects of mutations by considering each amino acid independently (e.g., PolyPhen-2 [40] and CADD [41] ) while others exploit structure information (e.g., FoldX [42] and OSPREY [4] ).”
Multi-Scale Representation Learning on Proteins (2022, methodology): “…2020, Jumper et al., 2021, AlphaFold] [Baek et al., 2021, RoseTTAFold] and protein structure determination methods such as cryo-electron microscopy [Callaway, 2020], and (ii.) by utilizing homology modeling algorithms on available wild type structures for mutant analysis [Schymkowitz et al., 2005].”
Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity (2016, neutral): “We used the FoldX software [35] to predict the difference in binding energy (ΔΔG) of the actual antibody with all the mutants obtained reverting each single somatic mutation to the original residue observed in the germline gene IGHV1-2.”
Elucidating modes of activation and herbicide resistance by sequence assembly and molecular modelling of the Acetolactate synthase complex in sugarcane. (2016, methodology): “These unsolvated were subject to correction of any final side-chain conformer issues with the error correction (RepairPDB) module of FoldX (Schymkowitz et al., 2005).”
Defining and searching for structural motifs using DeepView/Swiss-PdbViewer (2012, background): “According to FoldX (Version 2.5.1) [ 43,47] results, all residues belonging to the above mentioned motifs are expected to be important for the stability of the protein folds, with folding free energy changes upon alanine mutation ranging from 1.8 to 4.3 kcal/mol (Additional file 7, Additional file…”
Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking. (2013, methodology): “Based on this FoldX analysis, we suggest that residues receiving higher Antibody i-Patch scores not only have a higher likelihood of being in contact but also tend to be more energetically important.”
SPROUTS: a database for the evaluation of protein stability upon point mutation (2008, methodology): “Other methods exist but have been rejected due to some restrictions: CUPSAT (18) was not available in a stand-alone version and the current version of FoldX (19) only computes mutations to Alanine.”
Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, methodology): “These were consistent for machine learning based methods and FoldX, with I-Mutant showing the most significant performance deterioration on classification tasks (Figure S7) and no discernible trend for ENCoM and DDGun.”
Changing the Apoptosis Pathway through Evolutionary Protein Design (2019, methodology): “The binding potentials can be either physical [20] or evolutionary based [21,22].”