Computational and Functional Analyses of HER2 Mutations Reveal Allosteric Activation Mechanisms and Altered Pharmacologic Effects.

Amplification of HER2 can drive the proliferation of cancer cells, and several inhibitors of HER2 have been successfully developed. Recent advances in next-generation sequencing now reveal that HER2 is subject to mutation, with over 2,000 unique variants observed in human cancers. Several examples of oncogenic HER2 mutations have been described, and these primarily occur at allosteric sites outside the ATP-binding site. To identify the full spectrum of oncogenic HER2 driver mutations aside from a few well-studied mutations, we developed mutation-allostery-pharmacology (MAP), an in silico prediction algorithm based on machine learning. By applying this computational approach to 820 single-nucleotide variants, a list of 222 known and potential driver mutations was produced. Of these 222 mutations, 111 were screened by Ba/F3-retrovirus proliferation assays; 37 HER2 mutations were experimentally determined to be driver mutations, comprising 15 previously characterized and 22 newly identified oncogenic mutations. These oncogenic mutations mostly affected allosteric sites in the extracellular domain (ECD), transmembrane domain, and kinase domain of HER2, with only a single mutation in the HER2 orthosteric ATP site. Covalent homodimerization was established as a common mechanism of activation among HER2 ECD allosteric mutations, including the most prevalent HER2 mutation, S310F. Furthermore, HER2 allosteric mutants with enhanced covalent homodimerization were characterized by altered pharmacology that reduces the activity of existing anti-HER2 agents, including the mAb trastuzumab and the tyrosine kinase inhibitor lapatinib. Overall, the MAP-scoring and functional validation analyses provided new insights into the oncogenic activity and therapeutic targeting of HER2 mutations in cancer. SIGNIFICANCE: This study identified new oncogenic HER2 allosteric mutations, including ECD mutations that share covalent dimerization as a mechanism of oncogenicity, suggesting the need for novel inhibitors to treat HER2-mutant cancers.

The Q61H mutation decouples KRAS from upstream regulation and renders cancer cells resistant to SHP2 inhibitors.

Cancer cells bearing distinct KRAS mutations exhibit variable sensitivity to SHP2 inhibitors (SHP2i). Here we show that cells harboring KRAS Q61H are uniquely resistant to SHP2i, and investigate the underlying mechanisms using biophysics, molecular dynamics, and cell-based approaches. Q61H mutation impairs intrinsic and GAP-mediated GTP hydrolysis, and impedes activation by SOS1, but does not alter tyrosyl phosphorylation. Wild-type and Q61H-mutant KRAS are both phosphorylated by Src on Tyr32 and Tyr64 and dephosphorylated by SHP2, however, SHP2i does not reduce ERK phosphorylation in KRAS Q61H cells. Phosphorylation of wild-type and Gly12-mutant KRAS, which are associated with sensitivity to SHP2i, confers resistance to regulation by GAP and GEF activities and impairs binding to RAF, whereas the near-complete GAP/GEF-resistance of KRAS Q61H remains unaltered, and high-affinity RAF interaction is retained. SHP2 can stimulate KRAS signaling by modulating GEF/GAP activities and dephosphorylating KRAS, processes that fail to regulate signaling of the Q61H mutant.

High-resolution cryo-EM structures of actin-bound myosin states reveal the mechanism of myosin force sensing.

Myosins adjust their power outputs in response to mechanical loads in an isoform-dependent manner, resulting in their ability to dynamically adapt to a range of motile challenges. Here, we reveal the structural basis for force-sensing based on near-atomic resolution structures of one rigor and two ADP-bound states of myosin-IB (myo1b) bound to actin, determined by cryo-electron microscopy. The two ADP-bound states are separated by a 25° rotation of the lever. The lever of the first ADP state is rotated toward the pointed end of the actin filament and forms a previously unidentified interface with the N-terminal subdomain, which constitutes the upper half of the nucleotide-binding cleft. This pointed-end orientation of the lever blocks ADP release by preventing the N-terminal subdomain from the pivoting required to open the nucleotide binding site, thus revealing how myo1b is inhibited by mechanical loads that restrain lever rotation. The lever of the second ADP state adopts a rigor-like orientation, stabilized by class-specific elements of myo1b. We identify a role for this conformation as an intermediate in the ADP release pathway. Moreover, comparison of our structures with other myosins reveals structural diversity in the actomyosin binding site, and we reveal the high-resolution structure of actin-bound phalloidin, a potent stabilizer of filamentous actin. These results provide a framework to understand the spectrum of force-sensing capacities among the myosin superfamily.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Binding Energy Distribution Analysis Method: Hamiltonian Replica Exchange with Torsional Flattening for Binding Mode Prediction and Binding Free Energy Estimation.

Molecular dynamics modeling of complex biological systems is limited by finite simulation time. The simulations are often trapped close to local energy minima separated by high energy barriers. Here, we introduce Hamiltonian replica exchange (H-REMD) with torsional flattening in the Binding Energy Distribution Analysis Method (BEDAM), to reduce energy barriers along torsional degrees of freedom and accelerate sampling of intramolecular degrees of freedom relevant to protein-ligand binding. The method is tested on a standard benchmark (T4 Lysozyme/L99A/p-xylene complex) and on a library of HIV-1 integrase complexes derived from the SAMPL4 blind challenge. We applied the torsional flattening strategy to 26 of the 53 known binders to the HIV Integrase LEDGF site found to have a binding energy landscape funneled toward the crystal structure. We show that our approach samples the conformational space more efficiently than the original method without flattening when starting from a poorly docked pose with incorrect ligand dihedral angle conformations. In these unfavorable cases convergence to a binding pose within 2-3 Å from the crystallographic pose is obtained within a few nanoseconds of the Hamiltonian replica exchange simulation. We found that torsional flattening is insufficient in cases where trapping is due to factors other than torsional energy, such as the formation of incorrect intramolecular hydrogen bonds and stacking. Work is in progress to generalize the approach to handle these cases and thereby make it more widely applicable.

Parameterization of an effective potential for protein-ligand binding from host-guest affinity data.

Force field accuracy is still one of the "stalemates" in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein-ligand binding, organic host-guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host-guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein-ligand binding in two drug targets against the HIV-1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics-based binding free energy models can be used to evaluate and optimize force fields for protein-ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.

Free-energy landscape and characteristic forces for the initiation of DNA unzipping.

DNA unzipping, the separation of its double helix into single strands, is crucial in modulating a host of genetic processes. Although the large-scale separation of double-stranded DNA has been studied with a variety of theoretical and experimental techniques, the minute details of the very first steps of unzipping are still unclear. Here, we use atomistic molecular-dynamics simulations, coarse-grained simulations, and a statistical-mechanical model to study the initiation of DNA unzipping by an external force. Calculation of the potential of mean force profiles for the initial separation of the first few terminal basepairs in a DNA oligomer revealed that forces ranging between 130 and 230 pN are needed to disrupt the first basepair, and these values are an order of magnitude larger than those needed to disrupt basepairs in partially unzipped DNA. The force peak has an echo of ∼50 pN at the distance that unzips the second basepair. We show that the high peak needed to initiate unzipping derives from a free-energy basin that is distinct from the basins of subsequent basepairs because of entropic contributions, and we highlight the microscopic origin of the peak. To our knowledge, our results suggest a new window of exploration for single-molecule experiments.

Asynchronous Replica Exchange Software for Grid and Heterogeneous Computing.

Parallel replica exchange sampling is an extended ensemble technique often used to accelerate the exploration of the conformational ensemble of atomistic molecular simulations of chemical systems. Inter-process communication and coordination requirements have historically discouraged the deployment of replica exchange on distributed and heterogeneous resources. Here we describe the architecture of a software (named ASyncRE) for performing asynchronous replica exchange molecular simulations on volunteered computing grids and heterogeneous high performance clusters. The asynchronous replica exchange algorithm on which the software is based avoids centralized synchronization steps and the need for direct communication between remote processes. It allows molecular dynamics threads to progress at different rates and enables parameter exchanges among arbitrary sets of replicas independently from other replicas. ASyncRE is written in Python following a modular design conducive to extensions to various replica exchange schemes and molecular dynamics engines. Applications of the software for the modeling of association equilibria of supramolecular and macromolecular complexes on BOINC campus computational grids and on the CPU/MIC heterogeneous hardware of the XSEDE Stampede supercomputer are illustrated. They show the ability of ASyncRE to utilize large grids of desktop computers running the Windows, MacOS, and/or Linux operating systems as well as collections of high performance heterogeneous hardware devices.