APR-Score: Identification and Scoring of Aggregation-prone Regions in Protein Sequences

Protein aggregation is a critical phenomenon associated with numerous neurodegenerative and systemic diseases. Understanding the propensity of proteins to aggregate is essential for unraveling the molecular basis of these disorders and for the design and engineering of novel proteins, as well as for modulating the activity/stability of enzymatic proteins. Here, we present APR-Score, a novel machine learning-based computational method designed to identify aggregation-prone regions within protein sequences. APR-Score leverages a combination of sequence-based features to predict regions of proteins that are prone to aggregation. It harnessed the information embedded in compiled sequence and structural features to achieve state-of-the-art accuracy. The method was evaluated through rigorous cross-validation on the training dataset and further validated on an independent test dataset. APR-Score demonstrated robustness and reliability in distinguishing aggregation-prone regions from non-aggregating ones, achieving a Matthews correlation coefficient (MCC) of 0.77, precision of 0.89, and F1-Score of 0.87. This tool offers significant value for researchers investigating protein aggregation-related diseases, as it can expedite the identification of aggregation-prone regions, thereby aiding the development of targeted therapies and diagnostic tools. Moreover, computational protein design and engineering efforts can be facilitated by APR-Score-based identification and screening of aggregation-prone protein sequences. APR-Score is available at https://github.com/TII-BRC/APR-Score.