AAindex: Amino Acid index database

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Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks (2018, background): “To further search for the underlying mechanism, we correlate the accuracy of secondary structure for individual amino acids to >550 one-dimensional structure properties collected in AAindex (Kawashima and Kanehisa, 2000).”
mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019, methodology): “In addition, we included three protein contact potentials scores from the AAindex database (49) (Supplementary Table S3).”
DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations (2020, methodology): “Changes in Gibbs Free energy of folding can occur due to a myriad of factors related and in order to incorporate these properties, we used Arpeggio(40) to calculate the number of hydrophobic contacts involving the wild-type residue and contact potential scores from AAINDEX database.(64)”
High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder (2015, methodology): “1 of the AAindex database (40) was reduced to 159 indices that are predictive and dissimilar with each other (see Supplementary Materials for details).”