Adjusting the Energy Profile for CH-O Interactions Leads to Improved Stability of RNA Stem-Loop Structures in MD Simulations.

The role of ribonucleic acid (RNA) in biology continues to grow, but insight into important aspects of RNA behavior is lacking, such as dynamic structural ensembles in different environments, how flexibility is coupled to function, and how function might be modulated by small molecule binding. In the case of proteins, much progress in these areas has been made by complementing experiments with atomistic simulations, but RNA simulation methods and force fields are less mature. It remains challenging to generate stable RNA simulations, even for small systems where well-defined, thermostable structures have been established by experiments. Many different aspects of RNA energetics have been adjusted in force fields, seeking improvements that are transferable across a variety of RNA structural motifs. In this work, the role of weak CH···O interactions is explored, which are ubiquitous in RNA structure but have received less attention in RNA force field development. By comparing data extracted from high-resolution RNA crystal structures to energy profiles from quantum mechanics and force field calculations, it is shown that CH···O interactions are overly repulsive in the widely used Amber RNA force fields. A simple, targeted adjustment of CH···O repulsion that leaves the remainder of the force field unchanged was developed. Then, the standard and modified force fields were tested using molecular dynamics (MD) simulations with explicit water and salt, amassing over 300 µs of data for multiple RNA systems containing important features such as the presence of loops, base stacking interactions as well as canonical and noncanonical base pairing. In this work and others, standard force fields lead to reproducible unfolding of the NMR-based structures. Including a targeted CH···O adjustment in an otherwise identical protocol dramatically improves the outcome, leading to stable simulations for all RNA systems tested.

Accurately Modeling RNA Stem-Loops in an Implicit Solvent Environment.

Ribonucleic acid (RNA) molecules can adopt a variety of secondary and tertiary structures in solution, with stem-loops being one of the more common motifs. Here, we present a systematic analysis of 15 RNA stem-loop sequences simulated with molecular dynamics simulations in an implicit solvent environment. Analysis of RNA cluster ensembles showed that the stem-loop structures can generally adopt the A-form RNA in the stem region. Loop structures are more sensitive, and experimental structures could only be reproduced with modification of CH···O interactions in the force field, combined with an implicit solvent nonpolar correction to better model base stacking interactions. Accurately modeling RNA with current atomistic physics-based models remains challenging, but the RNA systems studied herein may provide a useful benchmark set for testing other RNA modeling methods in the future.

Mass Spectrometry and Pharmacological Approaches to Measuring Cooption and Reciprocal Activation of Receptor Tyrosine Kinases.

Receptor tyrosine kinases (RTKs) can show extensive crosstalk, directly and indirectly. Elucidating RTK crosstalk remains an important goal in the clinical combination of anti-cancer therapies. Here, we present mass spectrometry and pharmacological approaches showing the hepatocyte growth factor receptor (MET)-promoting tyrosine phosphorylation of the epidermal growth factor receptor (EGFR) and other membrane receptors in MET-amplified H1993 NSCLC cells. Conversely, in H292 wt-EGFR NSCLC cells, EGFR promotes the tyrosine phosphorylation of MET. Reciprocal regulation of the EGFR and insulin receptor (IR) was observed in the GEO CRC cells, where inhibition of the EGFR drives tyrosine phosphorylation of the insulin receptor. Similarly, in platelet-derived growth factor receptor (PDGFR)-amplified H1703 NSCLC cells, inhibition of the EGFR promotes the tyrosine phosphorylation of the PDGFR. These RTK interactions are used to illustrate basic principles applicable to other RTK signaling networks. More specifically, we focus on two types of RTK interaction: (1) co-option of one RTK by another and (2) reciprocal activation of one receptor following the inhibition of a distinct receptor.