A Binary Matrix Method to Enumerate, Hierarchically Order and Structurally Classify Peptide Aggregation

Abstract

ABSTRACTProtein aggregation is a common and complex phenomenon in biological processes, yet a robust analysis of this aggregation process remains elusive. The commonly used methods such as center-of-mass to center-of-mass (COM–COM) distance, the radius of gyration (Rg), hydrogen bonding (HB) and solvent accessible surface area (SASA) do not quantify the aggregation accurately. Herein, a new and robust method that uses an aggregation matrix (AM) approach to investigate peptide aggregation in a MD simulation trajectory is presented. A nxn two-dimensional aggregation matrix (AM) is created by using the inter-peptide Cα–Cα cut-off distances which are binarily encoded (0 or 1). These aggregation matrices are analyzed to enumerate, hierarchically order and structurally classify the aggregates. Comparison of the present AM method suggests that it is superior to the HB method since it can incorporate non-specific interactions and Rg, COM–COM methods since the cut-off distance is independent of the length of the peptide. More importantly, the present method can structurally classify the peptide aggregates, which the conventional Rg, COM– COM and HB methods fail. The unique selling point of this method is its ability to structurally classify peptide aggregates using two-dimensional matrices.Abstract Figure