Three-dimensional feature engineering from molecular properties. Initial properties that can be determined only with an atomic resolution structure, in the context of an amino acid sequence, or that depend only on amino acid identity are denoted at left. This property list is a non-redundant abbreviated set of all properties considered (see Appendix Table S4 and Materials and Methods for full detail). Columns of the feature matrix at right are alternating property sums and means at spatial scales denoted below matrix. p = a molecular property; i = RKPT residue; k = neighbor residues of i; r = radius length. See also Fig EV3.   Sequence-homology-based features for machine learning were derived by performing sequence alignments of all RKPT sites (+/- 10 residues) anchored at the central residue to compute alignment scores that were then reduced to a computationally manageable number of features by principal component analysis (PCA).   