Unraveling the molecular mechanism of interactions of the Rho GTPases Cdc42 and Rac1 with the scaffolding protein IQGAP2

Abstract

IQ motif-containing GTPase-activating protein (IQGAP) scaffolding proteins play central roles in cell-cell adhesion, polarity, and motility. The Rho GTPases Cdc42 and Rac1, in their GTPbound active forms, interact with all three human IQGAPs. The IQGAP-Cdc42 interaction promotes metastasis by enhancing actin polymerization. However, despite their high sequence identity, Cdc42 and Rac1 differ in their interactions with IQGAP. Two Cdc42 molecules can bind to the Exdomain and the RasGAP site of the GTPaseactivating protein (GAP)-related domain (GRD) of IQGAP and promote IQGAP dimerization. Only one Rac1 molecule might bind to the RasGAP site of GRD and may not facilitate the dimerization, and the exact mechanism of Cdc42 and Rac1 binding to IQGAP is unclear. Using all-atom molecular dynamics simulations, site-directed mutagenesis, and Western blotting, we unraveled the detailed mechanisms of Cdc42 and Rac1 interactions with IQGAP2. We observed that Cdc42 binding to the Ex-domain of GRD of IQGAP2 (GRD2) releases the Ex-domain at the C-terminal region of GRD2, facilitating IQGAP2 dimerization. Cdc42 binding to the Ex-domain promoted allosteric changes in the RasGAP site, providing a binding site for the second Cdc42 in the RasGAP site. Of note, the Cdc42 "insert loop" was important for the interaction of the first Cdc42 with the Ex-domain. By contrast, differences in Rac1 insert loop sequence and structure precluded its interaction with the Ex-domain. Rac1 could bind only to the RasGAP site of apo-GRD2 and could not facilitate IQGAP2 dimerization. Our detailed mechanistic insights help decipher how Cdc42 can stimulate actin polymerization in metastasis.The Rho GTPase family is a subfamily of the Ras GTPase superfamily. Rho GTPases regulate multiple cellular processes such as growth, survival, cell invasion, cell motility and vesicle trafficking by interacting with effector molecules (1-4). To date, the mammalian Rho GTPase family includes 22 members; Cdc42, Rac1, and RhoA are the most studied among them (5).Rho GTPases are distinguished from other GTPases by the presence of an "insert loop" which is rich in charged residues in an α-helical motif (Fig. http://www.jbc.org/cgi

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

“…Through this analysis, several key areas in the structure-functional depiction of controlled interactions and the center of the communication networks were identified. In another study, Ozdemir et al unraveled the atomistic interactions of the Rho GTPases, Cdc42 and Rac1, with the scaffolding protein IQ motif-containing GTPase-activating protein 2 (IQGAP2) using all-atom MD simulations, site-directed mutagenesis, and Western blotting [ 359 ]. They were able to decipher the underlying mechanism of the different stoichiometries involved in the binding of Rac1 and Cdc42 to GRD, and IQGAP2 dimerization.…”

Emerging roles of allosteric modulators in the regulation of protein‐protein interactions (PPIs): A new paradigm for PPI drug discovery

“…As exemplified by Protein Interactions by Structural Matching (PRISM), structural features, rather than gene sequences, are used for analysis and prediction of PPIs. [86][87][88]103,104 In addition, the application of machine learning methods such as the random forest model and deep learning algorithms has significantly improved the performance of bioinformatics software, 93 such as IntPred. 90 Importantly, prediction of PPIs through bioinformatics methods provides a starting point for drug discovery, for which high-throughput screening and other pharmacological research can be carried out.…”

“…Advances in structural biology also provide an avenue for the computational development of PPI prediction. As exemplified by Protein Interactions by Structural Matching (PRISM), structural features, rather than gene sequences, are used for analysis and prediction of PPIs . In addition, the application of machine learning methods such as the random forest model and deep learning algorithms has significantly improved the performance of bioinformatics software, such as IntPred .…”

The structural basis of the oncogenic mutant K-Ras4B homodimers

“…A total of four initial configurations, two α-homodimers and two β-homodimers of K-Ras4B-GTP, were subjected to the MD simulations. Our simulations closely followed the protocol reported in previous studies [21,22,[43][44][45]. All-atom additive CHARMM36 force field [46] was used, and simulations were performed by NAMD [47].…”