Investigating the Structure and Dynamics of the PIK3CA Wild-Type and H1047R Oncogenic Mutant

Abstract

The PIK3CA gene is one of the most frequently mutated oncogenes in human cancers. It encodes p110α, the catalytic subunit of phosphatidylinositol 3-kinase alpha (PI3Kα), which activates signaling cascades leading to cell proliferation, survival, and cell growth. The most frequent mutation in PIK3CA is H1047R, which results in enzymatic overactivation. Understanding how the H1047R mutation causes the enhanced activity of the protein in atomic detail is central to developing mutant-specific therapeutics for cancer. To this end, Surface Plasmon Resonance (SPR) experiments and Molecular Dynamics (MD) simulations were carried out for both wild-type (WT) and H1047R mutant proteins. An expanded positive charge distribution on the membrane binding regions of the mutant with respect to the WT protein is observed through MD simulations, which justifies the increased ability of the mutated protein variant to bind to membranes rich in anionic lipids in our SPR experiments. Our results further support an auto-inhibitory role of the C-terminal tail in the WT protein, which is abolished in the mutant protein due to loss of crucial intermolecular interactions. Moreover, Functional Mode Analysis reveals that the H1047R mutation alters the twisting motion of the N-lobe of the kinase domain with respect to the C-lobe and shifts the position of the conserved P-loop residues in the vicinity of the active site. These findings demonstrate the dynamical and structural differences of the two proteins in atomic detail and propose a mechanism of overactivation for the mutant protein. The results may be further utilized for the design of mutant-specific PI3Kα inhibitors that exploit the altered mutant conformation.

Exploring a Non-ATP Pocket for Potential Allosteric Modulation of PI3Kα

“…966-974), which have been proposed to act as membrane binding regions. 55 Although the C-ter is an inherently flexible element of these protein structures, we observed that in hWT apo simulation, it blocks the entrance to the catalytic site and is proximal to the P-loop, while in the other protein structures (holo forms of mWT, hWT, hMUT and apo form of hMUT) the C-ter is coiled and does not interact with the active site, which is in agreement with our previous studies 54 . Moreover, we observe that the membrane binding loop 1 is strongly anti-correlated with the activation loop in the hMUT simulation (Figure 7), which may be important for the H1047R mutant PI3Kα overactivation mechanism as it has been suggested that this mutant acts by changing the kinase interaction with the membrane.…”

“…The inner product for the hinge-bending and twisting motions between trajectories of the same (uncomplexed) protein type is in the range of 0.6-0.7. 54 Collectively, all the above data suggest that binding of PIK-108 to the non-ATP pocket does not influence the catalytic site conformation.…”

“…54 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 each one in triplicates. PIK-108 was a kind gift of Elias Couladouros (Agricultural University of Athens, Greece).…”

Oncogenic G12D mutation alters local conformations and dynamics of K-Ras

“…G12 is the most frequently mutated residue (89%), which most prevalently mutates to aspartate (G12D, 36%) followed by valine (G12V, 23%) and cysteine (G12C, 14%) 3,10 . This residue is located at the protein active site, which consists of a phosphate binding loop (P-loop, residues 10-17) and switch I (SI, residues [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] and II (SII, residues 60-74) regions. The active site residues are bound to the phosphate groups of GTP and are responsible for the GTPase function of K-Ras.…”

“…Recent studies suggest that utilizing protein dynamics data is a successful approach for understanding the effects of mutations on the structure, dynamics and function of proteins [34][35][36] . Particularly in drug discovery, dynamics data from oncogenic proteins 22,[37][38][39][40] have helped identify cryptic or allosteric binding sites [41][42][43][44] . We hypothesized that dynamic regulatory mechanisms can best be explored by detailed analyses of their molecular dynamics (MD) simulation data, from which we can predict the regulatory relationships between residue pairs.…”