AAindexNC Bioinformatics database

The physicochemical properties of amino acid residues from the AAindex database are widely used as predictors in building models for predicting both protein structures and properties. It should be noted, however, that the AAindex database contains data only for the 20 canonical amino acids. Non-canonical amino acids, while less common, are not rare; the Protein Data Bank includes proteins with more than 1000 distinct non-canonical amino acids. Here we propose a database and a method to evaluate the physicochemical properties from the AAindex database for non-canonical amino acids.

Download: full database and source codes of programs to train and learn prediction:
• The complete AAindexNC database includes predictions for all 566 physicochemical properties from the AAindex database, along with quality assessments and the root mean square error (RMSE) for the each property.
• The list of all unique non-canonical amino acids (ncAAs) from the PDB, along with their OpenEye SMILES representations
• Learning models for all AAindex's physicochemical properties
• C++ program to predict physicochemical properties from the AAindex database from OpenEye's ISO SMILES
• AAindexNC manual

You can also search for predicted values using the 3-letter chemical compound code (e.g., HYP for 4-Hydroxyproline) or predict all AAindex properties for your non-canonical amino acid by entering its OpenEye SMILES representation:

Warning: the set of predictors applied for training the prediction system is limited to SMILES components and chemical elements that are present in 20 canonical amino acids. Thereby, our method cannot predict AAindex properties for non-canonical amino acids containing elements such as As, B, Br, Cl, F, I, P, and Se (ncAAs with these elements are present in the PDB).

Milchevsky YV, Kravatskaya GI, Kravatsky YV.
AAindexNC: Estimating the Physicochemical Properties of Non-canonical Amino Acids, Including Those Derived from the PDB and PDBeChem Databank.
Int. J. Mol. Sci. 2024, 25(23), 12555, DOI: 10.3390/ijms252312555, PMID: XXXXXX

This work is supported by RSCF grant no 24-24-00493