Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing

<div><p>Variants in the TGFBR2 kinase domain cause several human diseases and can increase propensity for cancer. The widespread application of next generation sequencing within the setting of Individualized Medicine (IM) is increasing the rate at which TGFBR2 kinase domain variants are being identified. However, their clinical relevance is often uncertain. Consequently, we sought to evaluate the use of molecular modeling and molecular dynamics (MD) simulations for assessing the potential impact of variants within this domain. We documented the structural differences revealed by these models across 57 variants using independent MD simulations for each. Our simulations revealed various mechanisms by which variants may lead to functional alteration; some are revealed energetically, while others structurally or dynamically. We found that the ATP binding site and activation loop dynamics may be affected by variants at positions throughout the structure. This prediction cannot be made from the linear sequence alone. We present our structure-based analyses alongside those obtained using several commonly used genomics-based predictive algorithms. We believe the further mechanistic information revealed by molecular modeling will be useful in guiding the examination of clinically observed variants throughout the exome, as well as those likely to be discovered in the near future by clinical tests leveraging next-generation sequencing through IM efforts.</p></div

Description of TGFBR2 variants using genomics-based and structure-based evaluations.

<p>The same data as is presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170822#pone.0170822.t001" target="_blank">Table 1</a> is shown graphically. Genomics-based predictors provide predictions of damaging, while structure-based predictions test for specific mechanistic alterations.</p

TGFBR2 kinase domain sequence diversity and pathogenic associations summarized along the linear sequence and our structural model.

<p><b>A)</b> The background color of the canonical sequence is shown, indicating extent of conservation across paralogs. Amino acid positions with known pathogenic mutations (n = 30) are marked by red circles and those with benign alterations (n = 4) in green. The protein secondary structure from our model is displayed above the sequence. <b>B)</b> Coloring the 3D structural model by sequence conservation is more informative than the linear sequence as the regions of conservation have spatial relationships. <b>C)</b> The kinase domain consists of two sub-domains; the N- and C-terminal lobes. The adenine binding site lies within a cleft between them. The locations of the 65 variants studied here are marked by spheres at each residue’s C^α position. Sites are colored red if the variant(s) at the site is annotated as pathogenic in ClinVar, HGMD, or UniProt. If it is annotated as benign by the same sources, or is manually chosen as a control, we color the site green. Sites with multiple annotations, or only disease phenotype associations, are colored orange. <b>D)</b> We validate the quality of our structural model using multiple algorithms (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170822#sec017" target="_blank">Methods</a>) including Ramachandran analysis; > 95% of residues within allowed regions. <b>E)</b> Overall model quality is evaluated on a per residue basis (e.g. Ramachandran outliers) by QMEAN with residues with a score of ≤ 1 colored in white and scaled linearly to red at a score of 5.8. <b>F)</b> Our TGFBR2 model adopts the typical kinase domain architecture. The N-lobe is primarily comprised of a sheet of 5 strands, while the C-lobe is mostly helical.</p

Ligand binding site characteristic for TGFBR2 and paralogs.

<p><b>A)</b> Our TGFBR2 kinase domain model is superimposed on the experimental structures of 3 paralogs (TGFBR1, ACVR2A, and ACVR2B), emphasizing the consistency of this structural domain across the family. Each is colored by secondary structure elements, and the active site loop (from the DFG to the MAP sequence motifs; see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170822#sec017" target="_blank">Methods</a>) in teal. The molecular surface of adenine from our TGFBR2 model is shown. <b>B)</b> Adenine binding site from our TGFBR2 model. Residues from both the N- and C-lobes make up the active site. Side chains closely interacting with the bound adenine are shown in detail. <b>C)</b> X-ray structure of TGFBR1 bound to an antitumor agent (3tzm). <b>D)</b> X-ray structure of ACVR2A with a different antitumor agent bound (3q4t). <b>E)</b> X-ray structure of ACVR2B with adenine bound. There are strong similarities to the core of the binding sites across paralogs.</p

Structure-based evaluations were used to evaluate benign (B) and pathogenic (P) mutations.

<p>In these comparisons, benign simulations (n = 5; 4 benign variants and WT) act as negative controls. Variants within each group are summarized by a combined boxplot and density plot where width smoothly scales by the number of variants at each level of the score. <b>A)</b> The increase in folding energy upon mutation, ΔΔG_fold, is greater for many pathogenic variants, compared to benign. <b>B)</b> Changes in the DFG structural motif tend to be larger in pathogenic variants, compared to benign and <b>C)</b> using the ligand binding site. <b>D)</b> A small number of variants lead to increased local fluctuations.</p

Ligand binding site and active site loop conformational dynamics.

<p>We choose representative sites on each side of the ligand-binding site. The distances between these sites are used as monitors of the conformation of each site. We analyzed the direct and allosteric effects of variants on these and other sites. <b>A)</b> The C^α atoms of residues around the ligand-binding site include F327 “above” the ligand, L386 below, and F255 “across from” the ligand, within the p-loop. <b>B)</b> We used C^α distances as summary metrics for the DFG conformation: N384, F398 in the center of the motif, and E290. <b>C)</b> For the active site distances, the three monitors give a point in a 3D space for each conformation. As the MD simulations progress, we generate a collection of such points, from which a 3D volume is generated that encompassed the densest region of data points, for each variant. The surfaces enclosing half of the sampled distances for our WT simulation, and an example pathogenic variant, C394W, are shown. The separation between the two indicates their conformational differences during our simulations. <b>D)</b> Benign variants have little effect on ligand binding site dynamics; the volumes spatially overlap each other and the WT simulation. <b>E)</b> Superposition of all pathogenic variants studied shows a wide range of conformational effects.</p

Canonical motions of the kinase domain architecture reveal sites important for functional motions.

<p>A) The first mode of motion, or the least energetically taxing way that the kinase domain moves, corresponds to a twisting of the lobes relative to one another. B) The second mode corresponds to a coupled twisting and hinging of the lobes. C) The mobility of each amino acid within the structure can be summarized by Mean Square Fluctuation (MSF), computed from the same model. We plot the MSF of each residue, indicating sites of pathogenic mutations (red points) and benign (green). The inset shows the MSF on the 3D structure.</p

Variants that are distant from the activation loop or the ligand binding site affect dynamics at these sites.

<p>Variants that resulted in increased dynamics either the activation loop or the ligand binding site are indicated by spheres at their C^α atom position. The activation loop and ligand binding site are highlighted as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170822#pone.0170822.g001" target="_blank">Fig 1</a>. We defined an increase by values greater than those observed in benign simulations. Residues that when mutated alter dynamics at these sites are distributed throughout the structure.</p

Application of structural metrics to simulations of observed variants with unknown functional consequences.

<p>Many variants of uncertain significance, with conflicting annotations, or individual reports of disease associations, show alterations in structural features.</p

Integrated Genomic Characterization Reveals Novel, Therapeutically Relevant Drug Targets in FGFR and EGFR Pathways in Sporadic Intrahepatic Cholangiocarcinoma

<div><p>Advanced cholangiocarcinoma continues to harbor a difficult prognosis and therapeutic options have been limited. During the course of a clinical trial of whole genomic sequencing seeking druggable targets, we examined six patients with advanced cholangiocarcinoma. Integrated genome-wide and whole transcriptome sequence analyses were performed on tumors from six patients with advanced, sporadic intrahepatic cholangiocarcinoma (SIC) to identify potential therapeutically actionable events. Among the somatic events captured in our analysis, we uncovered two novel therapeutically relevant genomic contexts that when acted upon, resulted in preliminary evidence of anti-tumor activity. Genome-wide structural analysis of sequence data revealed recurrent translocation events involving the <i>FGFR2</i> locus in three of six assessed patients. These observations and supporting evidence triggered the use of FGFR inhibitors in these patients. In one example, preliminary anti-tumor activity of pazopanib (<i>in vitro</i> FGFR2 IC₅₀≈350 nM) was noted in a patient with an <i>FGFR2-TACC3</i> fusion. After progression on pazopanib, the same patient also had stable disease on ponatinib, a pan-FGFR inhibitor (<i>in vitro</i>, FGFR2 IC₅₀≈8 nM). In an independent non-FGFR2 translocation patient, exome and transcriptome analysis revealed an allele specific somatic nonsense mutation (E384X) in <i>ERRFI1</i>, a direct negative regulator of <i>EGFR</i> activation. Rapid and robust disease regression was noted in this <i>ERRFI1</i> inactivated tumor when treated with erlotinib, an EGFR kinase inhibitor. <i>FGFR2</i> fusions and <i>ERRFI</i> mutations may represent novel targets in sporadic intrahepatic cholangiocarcinoma and trials should be characterized in larger cohorts of patients with these aberrations.</p></div