Computational Design of Myristoylated Cell-Penetrating Peptides Targeting Oncogenic K-Ras.G12D at the Effector-Binding Membrane Interface

Quantitative Paramagnetic NMR-Based Analysis of Protein Orientational Dynamics on Membranes: Dissecting the KRas4B-Membrane Interactions

Peripheral membrane proteins can adopt distinct orientations on the surfaces of lipid bilayers that are often short-lived and challenging to characterize by conventional experimental methods. Here we describe a robust approach for mapping protein orientational landscapes through quantitative interpretation of paramagnetic relaxation enhancement (PRE) data arising from membrane mimetics with spin-labeled lipids. Theoretical analysis, followed by experimental verification, reveals insights into the distinct properties of the PRE observables that are generally distorted in the case of stably membrane-anchored proteins. To suppress the artifacts, we demonstrate that undistorted Γ₂ values can be obtained via transient membrane anchoring, based on which a computational framework is established for deriving accurate orientational ensembles obeying Boltzmann statistics. Application of the approach to KRas4B, a classical peripheral membrane protein whose orientations are critical for its functions and drug design, reveals four distinct orientational states that are close but not identical to those reported previously. Similar orientations are also found for a truncated KRas4B without the hypervariable region (HVR) that can sample a broader range of orientations, suggesting a confinement role of the HVR geometrically prohibiting severe tilting. Comparison of the KRas4B Γ₂ rates measured using nanodiscs containing different types of anionic lipids reveals identical Γ₂ patterns for the G-domain but different ones for the HVR, indicating only the latter is able to selectively interact with anionic lipids.

DREAMM: a web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design

SUMMARY

The allosteric modulation of peripheral membrane proteins (PMPs) by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web server. DREAMM works in the back end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of PMPs. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM.

AVAILABILITY AND IMPLEMENTATION

DREAMM web server is accessible via https://dreamm.ni4os.eu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning

Abnormal protein–membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein–membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein–membrane interface would shed light into the accessibility of membrane–protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein–membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.

Monoubiquitination of KRAS at Lysine104 and Lysine147 Modulates Its Dynamics and Interaction with Partner Proteins

KRAS, a 21 kDa guanine nucleotide-binding protein that functions as a molecular switch, plays a key role in regulating cellular growth. Dysregulation of this key signaling node leads to uncontrolled cell growth, a hallmark of cancer cells. KRAS undergoes post-translational modification by monoubiquitination at various locations, including at lysine104 (K104) and lysine147 (K147). Previous studies have suggested that K104 stabilizes helix-2/helix-3 interactions and K147 is involved in nucleotide binding. However, the impact of monoubiquitination at these residues on the overall structure, dynamics, or function of KRAS is not fully understood. In this study, we examined KRAS monoubiquitination at these sites using data from extensive (12 μs aggregate time) molecular dynamics simulations complemented by nuclear magnetic resonance spectroscopy data. We found that ubiquitin forms dynamic nonspecific interactions with various regions of KRAS and that ubiquitination at both sites modulates conformational fluctuations. In both cases, ubiquitin samples a broad range of conformational space and does not form long-lasting noncovalent contacts with KRAS but it adopts several preferred orientations relative to KRAS. To examine the functional impact of these preferred orientations, we performed a systematic comparison of the dominant configurations of the ubiquitin/KRAS simulated complex with experimental structures of KRAS bound to regulatory and effector proteins as well as a model membrane. Results from these analyses suggest that conformational selection and population shift may minimize the deleterious effects of KRAS ubiquitination at K104 and K147 on binding to some but not all interaction partners. Our findings thus provide new insights into the steric effects of ubiquitin and suggest a potential avenue for therapeutic targeting.

Mutation-Induced Impacts on the Switch Transformations of the GDP- and GTP-Bound K-Ras: Insights from Multiple Replica Gaussian Accelerated Molecular Dynamics and Free Energy Analysis

Mutations yield significant effect on the structural flexibility of two switch domains, SW1 and SW2, in K-Ras, which is considered as an important target of anticancer drug design. To unveil a molecular mechanism with regard to mutation-mediated tuning on the activity of K-Ras, multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations followed by analysis of free energy landscapes (FELs) are performed on the GDP- and GTP-bound wild-type (WT), G12V, and D33E K-Ras. The results suggest that G12V and D33E not only evidently change the flexibility of SW1 and SW2 but also greatly affect correlated motions of SW1 and SW2 separately relative to the P-loop and SW1, which exerts a certain tuning on the activity of K-Ras. The information stemming from the analyses of FELs reveals that the conformations of SW1 and SW2 are in high disorders in the GDP- and GTP-associated WT and mutated K-Ras, possibly producing significant effect on binding of guanine nucleotide exchange factors or effectors to K-Ras. The interaction networks of GDP and GTP with K-Ras are identified and the results uncover that the instability in hydrogen-bonding interactions of SW1 with GDP and GTP is mostly responsible for conformational disorder of SW1 and SW2 as well as tunes the activity of oncogenic K-Ras.

NMR in integrated biophysical drug discovery for RAS: past, present, and future

Mutations in RAS oncogenes occur in ~ 30% of human cancers, with KRAS being the most frequently altered isoform. RAS proteins comprise a conserved GTPase domain and a C-terminal lipid-modified tail that is unique to each isoform. The GTPase domain is a 'switch' that regulates multiple signaling cascades that drive cell growth and proliferation when activated by binding GTP, and the signal is terminated by GTP hydrolysis. Oncogenic RAS mutations disrupt the GTPase cycle, leading to accumulation of the activated GTP-bound state and promoting proliferation. RAS is a key target in oncology, however it lacks classic druggable pockets and has been extremely challenging to target. RAS signaling has thus been targeted indirectly, by harnessing key downstream effectors as well as upstream regulators, or disrupting the proper membrane localization required for signaling, by inhibiting either lipid modification or 'carrier' proteins. As a small (20 kDa) protein with multiple conformers in dynamic equilibrium, RAS is an excellent candidate for NMR-driven characterization and screening for direct inhibitors. Several molecules have been discovered that bind RAS and stabilize shallow pockets through conformational selection, and recent compounds have achieved substantial improvements in affinity. NMR-derived insight into targeting the RAS-membrane interface has revealed a new strategy to enhance the potency of small molecules, while another approach has been development of peptidyl inhibitors that bind through large interfaces rather than deep pockets. Remarkable progress has been made with mutation-specific covalent inhibitors that target the thiol of a G12C mutant, and these are now in clinical trials. Here we review the history of RAS inhibitor development and highlight the utility of NMR and integrated biophysical approaches in RAS drug discovery.