Aggrescan4D
for predicting aggregation propensities based on protein structures and pH of
the environment
Aggrescan4D (A4D) is aimed to predict the aggregation propensities of proteins in their folded states. Towards this aim A4D uses as input protein 3D-structures, derived from X-ray diffraction, solution NMR or modelling approaches in PDB format. The structures are energetically minimized before their analysis. The method exploits an experimentally derived intrinsic aggregation propensity scale for natural amino acids and projects this scale in the protein 3D structure. In the A4D method the intrinsic aggregation propensity of each particular amino acid in the structure is modulated by its specific structural context. Aggregation propensity is calculated for spherical regions centred on every residue Cα carbon. This provides a unique structurally corrected aggregation value (A4D score) for each amino acid in the structure.
A4D considers environmental pH for the prediction of protein aggregation in 3D structures. The server makes use of an structurally corrected pH-dependent lipophilicity scale of amino acids and incorporate pKa-ANI for the prediction of structural pKa values. By including pH, users are able to compare the structural aggregation propensity of the same protein under 11 different pH conditions (from pH 4.0 to pH 9.0, step size 0.5), effectively obtaining difference in protein solubility without the need to mutate residues.
Aggrescan4D: structure-informed analysis of pH-dependent protein aggregation (submitted) 2024
A3D 2.0 update for the prediction and optimization of protein solubility, Methods in Molecular Biology (biorxiv preprint), 2021
Aggrescan3D (A3D) 2.0: prediction and engineering of protein solubility, Nucleic Acids Research, gkz321, 2019
Aggrescan3D standalone package for structure-based prediction of protein aggregation properties Bioinformatics, btz143, 2019
AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures, Nucleic Acids Research, 43, W306-W313, 2015
Combining Structural Aggregation Propensity and Stability Predictions To Redesign Protein Solubility, Molecular Pharmaceutics, 10.1021/acs.molpharmaceut.8b00341, 2018