The energy landscape analysis of cancer mutations in protein kinases

Abstract

The growing interest in quantifying the molecular basis of protein kinase activation and allosteric regulation by cancer mutations has fueled computational studies of allosteric signaling in protein kinases. In the present study, we combined computer simulations and the energy landscape analysis of protein kinases to characterize the interplay between oncogenic mutations and locally frustrated sites as important catalysts of allostetric kinase activation. While structurally rigid kinase core constitutes a minimally frustrated hub of the catalytic domain, locally frustrated residue clusters, whose interaction networks are not energetically optimized, are prone to dynamic modulation and could enable allosteric conformational transitions. The results of this study have shown that the energy landscape effect of oncogenic mutations may be allosteric eliciting global changes in the spatial distribution of highly frustrated residues. We have found that mutation-induced allosteric signaling may involve a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. The presented study has demonstrated that activation cancer mutations may affect the thermodynamic equilibrium between kinase states by allosterically altering the distribution of locally frustrated sites and increasing the local frustration in the inactive form, while eliminating locally frustrated sites and restoring structural rigidity of the active form. The energy landsape analysis of protein kinases and the proposed role of locally frustrated sites in activation mechanisms may have useful implications for bioinformatics-based screening and detection of functional sites critical for allosteric regulation in complex biomolecular systems.

Figure 1. The histogram of a residue-based frustration index values in the catalytic domain from a kinome-based analysis.

(A) Wild-type kinases and (B) Mutant kinases.

Figure 2. The histogram of a residue-based frustration index values in the kinase catalytic domain from a statistical analysis of dominant kinase oncogenes.

(A) Wild-type kinases and (B) Mutant kinases. A set of employed kinase oncogenes included ABL, EGFR, BTK, KIT, MET, BRAF, and RET kinases. The analysis included mutants of these kinase genes with high oncogenic potential according to the frequency profiles in the mutational samples (>5) obtained from the COSMIC repository .

Figure 3. The effect of oncogenic mutations on local frustration in the ABL kinase.

The residue-based frustration index values are shown for a set of oncogenic ABL kinase mutants in the inactive (A) and active forms (B). The frustration index values are shown in filled yellow bars for the wild-type kinase form and in red filled bars for the mutant forms. The analysis was performed on the unbound form of the crystal structures of ABL in the inactive form (PDB ID 1IEP) and active form (PDB ID 1M52) , .

Figure 4. The effect of oncogenic mutations on local frustration in the EGFR kinase.

The residue-based frustration index values are shown for a set of oncogenic EGFR kinase mutants in the inactive (A) and active forms (B). The frustration index values are shown in filled yellow bars for the wild-type kinase form and in red filled bars for the mutant forms. The analysis was performed on the unbound form of the crystal structures of EGFR in the inactive form (PDB ID 1XKK) and active form (PDB ID 2J6M) .

Figure 5. The effect of oncogenic mutation on spatial distribution of local frustration in the ABL kinase.

The spatial distribution of local frustration in the inactive forms of ABL-WT (A) and ABL-T315I (B). The color sliding scheme of local frustration ranges from minimally frustrated (shown in blue) to highly frustrated (shown red). The key functional regions of the protein kinase along with the respective range of protein residues are referred to by arrows. Structural mapping of local frustration on the ABL kinase catalytic core is shown for the inactive ABL-WT structure (PDB ID 1IEP) . The effect of T315I on the inactive ABL structure was evaluated via structural modeling detailed in the Materials and Methods section. The Pymol program was used for visualization of protein kinase structures and the local frustration mapping (The PyMOL Molecular Graphics System, Version 1.2r3pre, Schrödinger, and LLC).

Figure 6. The spatial distribution of highly oncogenic mutations and highly frustrated residues in the kinase catalytic core.

The kinase mutations with known high oncogenic potential were mapped onto kinase catalytic domain (A). Structurally conserved hotspots of kinase cancer mutations are annotated by large red spheres and their location is indicated by arrows in (A). The locally frustrated sites (FI<-2.0) mapped onto kinase catalytic domain and depicted as small red spheres (B). The crystal structure of EGFR-WT in the active form (PDB ID 2J6M) was used as a template for structural mapping. The Pymol program was used for visualization of protein kinase structures and the local frustration mapping (The PyMOL Molecular Graphics System, Version 1.2r3pre, Schrödinger, and LLC.).

Figure 7. The distribution of oncogenic mutations and locally frustrated sites at each structural region in the catalytic core.

The distribution of cancer mutations (A) and the distribution of local frustration (B) across catalytic core subdomains. The analysis is performed based on known oncogenic mutations in the ABL, EGFR, BTK, KIT, BRAF, MET, and RET kinase genes. The residue ranges of the kinase subdomains (SD) were determined based on the ABL kinase crystal structure (PDB ID 1IEP) as the reference and in accordance to our previous study : SDI:242-261(P-loop region); SD2:262-278; SD3:279-291(αC-helix); SD4:292-309; SD5:310-335 (hinge region); SD6A:336-356; SD6B357-374 (catalytic loop); SD7:375-393 (activation loop); SD8:394-416 (P+l loop); SD9:417-438; SD10:439-461; SD11:462-480; SD12:481-498. The C-terminal region encompasses SD8-SD12 subdomains.