Molecular bowls for inclusion complexation of toxic anticancer drug methotrexate.

We describe the preparation and study of novel cavitands, molecular bowls 16+ and 26+, as good binders of the anticancer drug methotrexate (MTX). Molecular bowls are comprised of a curved tribenzotriquinacene (TBTQ) core conjugated to three macrocyclic pyridinium units at the top. The cavitands are easily accessible via two synthetic steps from hexabromo-tribenzotriquinacene in 25% yield. As amphiphilic molecules, bowls 16+ and 26+ self-associate in water by the nucleation-to-aggregation pathway (NMR). The bowls are preorganized, having a semi-rigid framework comprising a fixed bottom with a wobbling pyridinium rim (VT NMR and MD). Further studies, both experimental (NMR) and computational (DFT and MCMM), suggested that a folded MTX occupies the cavity of bowls wherein it forms π-π, C-H-π, and ion pairing intermolecular contacts but also undergoes desolvation to give stable binary complexes (µM) in water. Moreover, a computational protocol is introduced to identify docking pose(s) of MTX inside molecular bowls from NMR shielding data. Both molecular bowls have shown in vitro biocompatibility with liver and kidney cell lines (MTS assay). As bowl 26+ is the strongest binder of MTX reported to date, we envision it as an excellent candidate for further studies on the way toward developing an antidote capable of removing MTX from overdosed cancer patients.

Thermodynamic coupling of the tandem RRM domains of hnRNP A1 underlie its pleiotropic RNA binding functions.

The functional properties of RNA binding proteins (RBPs) require allosteric regulation through interdomain communication. Despite the importance of allostery to biological regulation, only a few studies have been conducted to describe the biophysical nature by which interdomain communication manifests in RBPs. Here, we show for hnRNP A1 that interdomain communication is vital for the unique stability of its amino-terminal domain, which consists of two RNA recognition motifs (RRMs). These RRMs exhibit drastically different stability under pressure. RRM2 unfolds as an individual domain but remains stable when appended to RRM1. Variants that disrupt interdomain communication between the tandem RRMs show a significant decrease in stability. Carrying these mutations over to the full-length protein for in vivo experiments revealed that the mutations affected the ability of the disordered carboxyl-terminal domain to engage in protein-protein interactions and influenced the protein's RNA binding capacity. Collectively, this work reveals that thermodynamic coupling between the tandem RRMs of hnRNP A1 accounts for its allosteric regulatory functions.

NMR 1H, 13C, 15N backbone resonance assignments of wild-type human K-Ras and its oncogenic mutants G12D and G12C bound to GTP.

Human K-Ras protein, which is a member of the GTPase Ras family, hydrolyzes GTP to GDP and concomitantly converts from its active to its inactive state. It is a key oncoprotein, because several mutations, particularly those at residue position 12, occur with a high frequency in a wide range of human cancers. The K-Ras protein is therefore an important target for developing therapeutic anti-cancer agents. In this work we report the almost complete sequence-specific resonance assignments of wild-type and the oncogenic G12C and G12D mutants in the GTP-complexed active forms, including the functionally important Switch I and Switch II regions. These assignments serve as the basis for a comprehensive functional dynamics study of wild-type K-Ras and its G12 mutants.

DEEP Picker1D and Voigt Fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra.

The quantitative deconvolution of 1D-NMR spectra into individual resonances or peaks is a key step in many modern NMR workflows as it critically affects downstream analysis and interpretation. Depending on the complexity of the NMR spectrum, spectral deconvolution can be a notable challenge. Based on the recent deep neural network DEEP Picker and Voigt Fitter for 2D NMR spectral deconvolution, we present here an accurate, fully automated solution for 1D-NMR spectral analysis, including peak picking, fitting, and reconstruction. The method is demonstrated for complex 1D solution NMR spectra showing excellent performance also for spectral regions with multiple strong overlaps and a large dynamic range whose analysis is challenging for current computational methods. The new tool will help streamline 1D-NMR spectral analysis for a wide range of applications and expand their reach toward ever more complex molecular systems and their mixtures.

Dynamic and Assembly Characteristics of Deep-Cavity Basket Acting as a Host for Inclusion Complexation of Mitoxantrone in Biotic and Abiotic Systems.

We describe the preparation, dynamic, assembly characteristics of vase-shaped basket 13- along with its ability to form an inclusion complex with anticancer drug mitoxantrone in abiotic and biotic systems. This novel cavitand has a deep nonpolar pocket consisting of three naphthalimide sides fused to a bicyclic platform at the bottom while carrying polar glycines at the top. The results of 1 H Nuclear Magnetic Resonance (NMR), 1 H NMR Chemical Exchange Saturation Transfer (CEST), Calorimetry, Hybrid Replica Exchange Molecular Dynamics (REMD), and Microcrystal Electron Diffraction (MicroED) measurements are in line with 1 forming dimer [12 ]6- , to be in equilibrium with monomers 1(R) 3- (relaxed) and 1(S) 3- (squeezed). Through simultaneous line-shape analysis of 1 H NMR data, kinetic and thermodynamic parameters characterizing these equilibria were quantified. Basket 1(R) 3- includes anticancer drug mitoxantrone (MTO2+ ) in its pocket to give stable binary complex [MTO⊂1]- (Kd =2.1 µM) that can be precipitated in vitro with UV light or pH as stimuli. Both in vitro and in vivo studies showed that the basket is nontoxic, while at a higher proportion with respect to MTO it reduced its cytotoxicity in vitro. With well-characterized internal dynamics and dimerization, the ability to include mitoxantrone, and biocompatibility, the stage is set to develop sequestering agents from deep-cavity baskets.

Thermodynamic Coupling of the tandem RRM domains of hnRNP A1 underlie its Pleiotropic RNA Binding Functions.

The functional properties of RNA-binding proteins (RBPs) require allosteric regulation through inter-domain communication. Despite the foundational importance of allostery to biological regulation, almost no studies have been conducted to describe the biophysical nature by which inter-domain communication manifests in RBPs. Here, we show through high-pressure studies with hnRNP A1 that inter-domain communication is vital for the unique stability of its N- terminal domain containing a tandem of RNA Recognition Motifs (RRMs). Despite high sequence similarity and nearly identical tertiary structures, the two RRMs exhibit drastically different stability under pressure. RRM2 unfolds completely under high-pressure as an individual domain, but when appended to RRM1, it remains stable. Variants in which inter-domain communication is disrupted between the tandem RRMs show a large decrease in stability under pressure. Carrying these mutations over to the full-length protein for in vivo experiments revealed that the mutations affected the ability of the disordered C-terminus to engage in protein-protein interactions and more importantly, they also influenced the RNA binding capacity. Collectively, this work reveals that thermodynamic coupling between the tandem RRMs of hnRNP A1 accounts for its allosteric regulatory functions.

Excited-state observation of active K-Ras reveals differential structural dynamics of wild-type versus oncogenic G12D and G12C mutants.

Despite the prominent role of the K-Ras protein in many different types of human cancer, major gaps in atomic-level information severely limit our understanding of its functions in health and disease. Here, we report the quantitative backbone structural dynamics of K-Ras by solution nuclear magnetic resonance spectroscopy of the active state of wild-type K-Ras bound to guanosine triphosphate (GTP) nucleotide and two of its oncogenic P-loop mutants, G12D and G12C, using a new nanoparticle-assisted spin relaxation method, relaxation dispersion and chemical exchange saturation transfer experiments covering the entire range of timescales from picoseconds to milliseconds. Our combined experiments allow detection and analysis of the functionally critical Switch I and Switch II regions, which have previously remained largely unobservable by X-ray crystallography and nuclear magnetic resonance spectroscopy. Our data reveal cooperative transitions of K-Ras·GTP to a highly dynamic excited state that closely resembles the partially disordered K-Ras·GDP state. These results advance our understanding of differential GTPase activities and signaling properties of the wild type versus mutants and may thus guide new strategies for the development of therapeutics.

ARCHE-NOAH: NMR supersequence with five different CEST experiments for studying protein conformational dynamics.

An NMR NOAH-supersequence is presented consisting of five CEST experiments for studying protein backbone and side-chain dynamics by 15N-CEST, carbonyl-13CO-CEST, aromatic-13Car-CEST, 13Cα-CEST, and methyl-13Cmet-CEST. The new sequence acquires the data for these experiments in a fraction of the time required for the individual experiments, saving over four days of NMR time per sample.

NOAH-(15N/13C)-CEST NMR supersequence for dynamics studies of biomolecules.

An NMR supersequence is introduced for the rapid acquisition of 15N-CEST and methyl-13C-CEST experiments in the same pulse sequence for applications to proteins. The high sensitivity and accuracy allows the simultaneous quantitative characterization of backbone and side-chain dynamics on the millisecond timescale ideal for routine screening for alternative protein states.

Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra.

Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.