Unraveling the Significance of Mg2+ Dependency and Nucleotide Binding Specificity of H-RAS.

RAS is a small GTPase and acts as a binary molecular switch; the transition from its active to inactive state plays a crucial role in various cell signaling processes. Molecular dynamics simulations at the atomistic level suggest that the absence of cofactor Mg2+ ion generally leads to pronounced structural changes in the Switch-I than Switch-II regions and assists GTP binding. The presence of the Mg2+ ion also restricts the rotation of ϒ phosphate and enhances the hydrolysis rate of GTP. Further, the simulations reveal that the stability of the protein is almost uncompromised when Mg2+ is replaced with Zn2+ and not the Ca2+ ion. The specificity of H-RAS to GTP was evaluated by substituting with ATP and CTP, which indicates that the binding pocket tolerates purine bases over pyrimidine bases. However, the D119 residue specifically interacts with the guanine base and serves as one of the primary interactions that leads to the selectivity of GTP over ATP. The ring displacement of 32Y serves as gate dynamics in H-RAS which are important for its interaction with GAP for the nucleotide exchange and is restricted in the presence of ATP. Finally, the point mutations 61, 16, and 32 influence the structural changes, specifically in the Switch-II region, which are expected to impact the GTP hydrolysis and thus are termed oncogenic mutations.

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

Protein 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 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. An nxn two-dimensional AM is created by using the interpeptide Cα-Cα cutoff 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 nonspecific interactions and the Rg and COM-COM methods since the cutoff 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 to do. The unique selling point of this method is its ability to structurally classify peptide aggregates using two-dimensional matrices.