CHESPA/CHESCA-SPARKY: automated NMR data analysis plugins for SPARKY to map protein allostery.

MOTIVATION: Correlated Nuclear Magnetic Resonance (NMR) chemical shift changes identified through the CHEmical Shift Projection Analysis (CHESPA) and CHEmical Shift Covariance Analysis (CHESCA) reveal pathways of allosteric transitions in biological macromolecules. To address the need for an automated platform that implements CHESPA and CHESCA and integrates them with other NMR analysis software packages, we introduce here integrated plugins for NMRFAM-SPARKY that implement the seamless detection and visualization of allosteric networks. AVAILABILITY AND IMPLEMENTATION: CHESCA-SPARKY and CHESPA-SPARKY are available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers to the SBGrid (https://sbgrid.org). The assigned spectra involved in this study and tutorial videos using this dataset are available at https://sites.google.com/view/chescachespa-sparky. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics Online.

Simultaneous detection of intra- and inter-molecular paramagnetic relaxation enhancements in protein complexes.

Paramagnetic relaxation enhancement (PRE) measurements constitute a powerful approach for detecting both permanent and transient protein-protein interactions. Typical PRE experiments require an intrinsic or engineered paramagnetic site on one of the two interacting partners; while a second, diamagnetic binding partner is labeled with stable isotopes (15N or 13C). Multiple paramagnetic labeled centers or reversed labeling schemes are often necessary to obtain sufficient distance restraints to model protein-protein complexes, making this approach time consuming and expensive. Here, we show a new strategy that combines a modified pulse sequence (1HN-Γ2-CCLS) with an asymmetric labeling scheme to enable the detection of both intra- and inter-molecular PREs simultaneously using only one sample preparation. We applied this strategy to the non-covalent dimer of ubiquitin. Our method confirmed the previously identified binding interface for the transient di-ubiquitin complex, and at the same time, unveiled the internal structural dynamics rearrangements of ubiquitin upon interaction. In addition to reducing the cost of sample preparation and speed up PRE measurements, by detecting the intra-molecular PRE this new strategy will make it possible to measure and calibrate inter-molecular distances more accurately for both symmetric and asymmetric protein-protein complexes.

AI-designed RF pulses enable fast pulsing heteronuclear multiple quantum coherence NMR experiment at high and ultra-high magnetic fields.

We present a new AI-optimized 2D heteronuclear multiple quantum coherence (RAPID-HMQC) pulse sequence for NMR spectroscopy. RAPID-HMQC is a longitudinal 1H relaxation-optimized experiment with new AI-designed band-selective pulses to accelerate the analysis of organic compounds, metabolites, biopolymers, and real-time monitoring of dynamic processes at high- and ultra-high magnetic fields.

The αC-ß4 loop controls the allosteric cooperativity between nucleotide and substrate in the catalytic subunit of protein kinase A.

Allosteric cooperativity between ATP and substrates is a prominent characteristic of the cAMP-dependent catalytic subunit of protein kinase A (PKA-C). This long-range synergistic action is involved in substrate recognition and fidelity, and it may also regulate PKA's association with regulatory subunits and other binding partners. To date, a complete understanding of this intramolecular mechanism is still lacking. Here, we integrated NMR(Nuclear Magnetic Resonance)-restrained molecular dynamics simulations and a Markov State Model to characterize the free energy landscape and conformational transitions of PKA-C. We found that the apoenzyme populates a broad free energy basin featuring a conformational ensemble of the active state of PKA-C (ground state) and other basins with lower populations (excited states). The first excited state corresponds to a previously characterized inactive state of PKA-C with the αC helix swinging outward. The second excited state displays a disrupted hydrophobic packing around the regulatory (R) spine, with a flipped configuration of the F100 and F102 residues at the αC-ß4 loop. We validated the second excited state by analyzing the F100A mutant of PKA-C, assessing its structural response to ATP and substrate binding. While PKA-CF100A preserves its catalytic efficiency with Kemptide, this mutation rearranges the αC-ß4 loop conformation, interrupting the coupling of the two lobes and abolishing the allosteric binding cooperativity. The highly conserved αC-ß4 loop emerges as a pivotal element to control the synergistic binding of nucleotide and substrate, explaining how mutations or insertions near or within this motif affect the function and drug sensitivity in homologous kinases.

High-fidelity control of spin ensemble dynamics via artificial intelligence: from quantum computing to NMR spectroscopy and imaging.

High-fidelity control of spin ensemble dynamics is essential for many research areas, spanning from quantum computing and radio-frequency (RF) engineering to NMR spectroscopy and imaging. However, attaining robust and high-fidelity spin operations remains an unmet challenge. Using an evolutionary algorithm and artificial intelligence (AI), we designed new RF pulses with customizable spatial or temporal field inhomogeneity compensation. Compared with the standard RF shapes, the new AI-generated pulses show superior performance for bandwidth, robustness, and tolerance to field imperfections. As a benchmark, we constructed a spin entanglement operator for the weakly coupled two-spin-1/2 system of 13CHCl3, achieving high-fidelity transformations under multiple inhomogeneity sources. We then generated band-selective and ultra-broadband RF pulses typical of biomolecular NMR spectroscopy. When implemented in multipulse NMR experiments, the AI-generated pulses significantly increased the sensitivity of medium-size and large protein spectra relative to standard pulse sequences. Finally, we applied the new pulses to typical imaging experiments, showing a remarkable tolerance to changes in the RF field. These AI-generated RF pulses can be directly implemented in quantum information, NMR spectroscopy of biomolecules, magnetic resonance imaging techniques for in vivo and materials sciences.