Magre‑II: Predicting Aggregation Region in Proteins with Machine Learning, Based on Tertiary Structure

A better understanding of the problem of protein aggregation encouraged the development of computational tools to help study this phenomenon. Some studies indicate that short segments of amino acids found in amyloid precursor proteins may be involved in the formation of these aggregations. The creation of precise techniques for identifying regions that are prone to aggregation is one of the present issues facing the bioinformatics community. In our earlier work, we developed Magre-I using machine learning techniques. Using a consensus model for classification, this approach made it possible to predict aggregation regions based on the primary amino acid sequence of proteins. We now present an improved method, called Magre-II, which makes use of the three-dimensional (3D) structure of proteins and experimental annotations of aggregation by using a neighborhood sphere analysis model for training and predictions. We show that Magre-II has a high potential for predicting regions that are prone to protein aggregation by comparing it with other commonly used predictors. The obtained scores were as follows: Magre-II (78.2%), Aggrescan3d (76.7%), Waltz (67.4%), and Tango (73.1%) for alpha-synuclein.