Figure 1.
Schematic view of the strategy used to derive predictive features and train and validate ELASPIC for the prediction of stability effects in domain core and domain-domain interfaces upon mutation.
Figure 2.
Correlation between predicted and experimental ΔΔG values for our curated ProTherm core dataset (A) and SKEMPI interface dataset (B). (C) Comparative histograms of the Pearson correlation among several state-of-the-art methods using three versions of ProTherm datasets for the core predictions, and SKEMPI dataset for the interface prediction.
Figure 3.
Feature importance for core and interface predictions.
Histogram representing the relative importance of the different features for core predictions (A) and interface prediction (B). To avoid cluttering, only features with a relative importance of 10% or larger were considered and coloured according to the three categories. Abbreviations: t: torsional, diS: disulfide, E: electrostatics, ion: ionization, dS: entropy, Hdipole: helix dipole, cb: covalent bond, sb: salt bridge, hb: hydrogen bond, cisb: cysteine bond, wb: water bridge, vdW: wan der Waals, mc: main chain, sc: side chain, if: interface, dm: domain, sasa: solvent accessibility, solv: solvation, ap: apolar, po: polar (see Table S1 for feature description).
Figure 4.
Summary of stability prediction of nsSNP mutations.
Predicted absolute ΔΔGDT box plots (right) are shown for (A) core and (B) interface mutations and the three types of mutations (Hapmap, OMIM and COSMIC driver/passenger).
Table 1.
Disease mutations calculated for domain cores and interfaces.