STRUM: structure-based prediction of protein stability changes upon single-point mutation

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Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks (2020, methodology): “These 85 methods either rely on explicit biophysical modeling of amino acid interactions coupled with 86 conformational sampling of protein structures (7-11) or apply machine/statistical learning to 87 extract patterns from various types of amino acid sequence, evolutionary, and/or protein 88 struct”
EvoDesign: Designing protein-protein binding interactions using evolutionary interface profiles in conjunction with an optimized physical energy function (2019, unknown): “samples from 210 monomeric proteins taken from the FoldX and STRUM datasets [25, 26].”
Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, background): “Mutations on S2648 are mapped to protein structures solved by either Crystallography with X-ray diffraction or nuclear magnetic resonance (NMR), with 77% of mutations leading to a decrease in protein stability, as observed previously and in other datasets [49,71].”
US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes (2022, background): “(4) Alignments based on the superimposition of fragments with length Lmin/2 and Lmin/3, where Lmin is the minimal length between the two query chains.”