Computational and Experimental Characterization of Patient Derived Mutations Reveal an Unusual Mode of Regulatory Spine Assembly and Drug Sensitivity in EGFR Kinase

The catalytic activation of protein kinases requires precise positioning of key conserved catalytic and regulatory motifs in the kinase core. The Regulatory Spine (RS) is one such structural motif that is dynamically assembled upon kinase activation. The RS is also a mutational hotspot in cancers; however, the mechanisms by which cancer mutations impact RS assembly and kinase activity are not fully understood. In this study, through mutational analysis of patient derived mutations in the RS of EGFR kinase, we identify an activating mutation, M766T, at the RS3 position. RS3 is located in the regulatory αC-helix, and a series of mutations at the RS3 position suggest a strong correlation between the amino acid type present at the RS3 position and ligand (EGF) independent EGFR activation. Small polar amino acids increase ligand independent activity, while large aromatic amino acids decrease kinase activity. M766T relies on the canonical asymmetric dimer for full activation. Molecular modeling and molecular dynamics simulations of WT and mutant EGFR suggest a model in which M766T activates the kinase domain by disrupting conserved autoinhibitory interactions between M766 and hydrophobic residues in the activation segment. In addition, a water mediated hydrogen bond network between T766, the conserved K745-E762 salt bridge, and the backbone amide of the DFG motif is identified as a key determinant of M766T-mediated activation. M766T is resistant to FDA approved EGFR inhibitors such as gefitinib and erlotinib, and computational estimation of ligand binding free energy identifies key residues associated with drug sensitivity. In sum, our studies suggest an unusual mode of RS assembly and oncogenic EGFR activation, and provide new clues for the design of allosteric protein kinase inhibitors

Top predicted unconfirmed mutations.

<p>Probability scores and rankings of the top predicted mutations. Scores were calculated with the multiple classifier trained on COSMIC v.50 data. Asterisks indicate the five mutations selected for cell-based assays.</p

Quantified tyrosine auto-phosphorylation levels of wild-type and mutant-type EGFR.

<p>Quantified phosphorylation levels are shown in the form of histograms. Quantification was done using Image J.</p

Prediction and Prioritization of Rare Oncogenic Mutations in the Cancer Kinome Using Novel Features and Multiple Classifiers

<div><p>Cancer is a genetic disease that develops through a series of somatic mutations, a subset of which drive cancer progression. Although cancer genome sequencing studies are beginning to reveal the mutational patterns of genes in various cancers, identifying the small subset of “causative” mutations from the large subset of “non-causative” mutations, which accumulate as a consequence of the disease, is a challenge. In this article, we present an effective machine learning approach for identifying cancer-associated mutations in human protein kinases, a class of signaling proteins known to be frequently mutated in human cancers. We evaluate the performance of 11 well known supervised learners and show that a multiple-classifier approach, which combines the performances of individual learners, significantly improves the classification of known cancer-associated mutations. We introduce several novel features related specifically to structural and functional characteristics of protein kinases and find that the level of conservation of the mutated residue at specific evolutionary depths is an important predictor of oncogenic effect. We consolidate the novel features and the multiple-classifier approach to prioritize and experimentally test a set of rare unconfirmed mutations in the epidermal growth factor receptor tyrosine kinase (EGFR). Our studies identify T725M and L861R as rare cancer-associated mutations inasmuch as these mutations increase EGFR activity in the absence of the activating EGF ligand in cell-based assays.</p></div

Feature values of selected mutations.

<p>Feature values of selected mutations.</p

Structural location of selected EGFR mutation sites.

<p>Protein crystal structure [PDB∶2JIU] shown as cartoon, with sites G724, T725, L858 and L861 shown as spheres. Structural regions highlighted in yellow are kinase subdomain I and the activation loop. The structure image was generated using PyMOL <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003545#pcbi.1003545-Delano1" target="_blank">[75]</a>.</p

Comparison of performance of individual and combined classifiers.

<p>Each algorithm trained using selected features and evaluated with 10-fold cross-validation. Values are average of the metrics evaluated with respect to the positive and negative classes.</p

Confusion matrix of individual classifier performance.

<p>All <i>in silico</i> experiments were evaluated with 10-fold cross-validation. TP means an instance in the positive set (COSMIC) was correctly classified as causative, TN means an instance in the negative set (dbSNP) was correctly classified as non-causative.</p

Best models of TbLBPK•drug complexes are consistent with affinity chromatography elution data.

<p>(<b><i>A</i></b>) Predicted ICM binding scores of lapatinib (red), AEE788 (green) and canertinib (magenta) in the binding pockets of the protein kinases. The dotted line represents a hypothetical cutoff for kinase elution by drug after an NAD+ wash of the affinity column. (<b><i>B</i></b>) Predicted binding poses for lapatinib (red), AEE788 (green) and canertinib (magenta) to their highest affinity protein kinases; TbLBPK1, TbLBPK2, and TbCBPK1, respectively. Kinase hinge region is shown in ribbon, ligand atom placement surface is represented by a wire mesh and colored according to its binding properties.</p

Drug elution of <i>T. brucei</i> protein kinases using an ATP-affinity resin.

<p>Proteins in total cell lysate from <i>T. brucei</i> were bound on ATP resin. Sepharose 4B resin was used as control. Unbound proteins (Flow through) were recovered and resin was washed sequentially with buffer A and buffer A containing 1<b> </b>M KCl (Panel A). Bound proteins were eluted with lapatinib (100 γM), or AEE788 (100 γM) or canertinib (100 γM) (Panel B, C and D, respectively). Proteins were visualized by silver staining. Matrix label, <b><i>A</i></b> is ATP-sepharose, and <b><i>S</i></b> is Sepharose 4B.</p