De novo NMR pulse sequence design using Monte-Carlo optimization techniques

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

1H/13C/15N triple-resonance experiments for structure determinaton of membrane proteins by oriented-sample NMR

The benefits of triple-resonance experiments for structure determination of macroscopically oriented membrane proteins by solid-state NMR are discussed. While double-resonance ¹H/¹⁵N experiments are effective for structure elucidation of alpha-helical domains, extension of the method of oriented samples to more complex topologies and assessing side-chain conformations necessitates further development of triple-resonance (¹H/¹³C/¹⁵N) NMR pulse sequences. Incorporating additional spectroscopic dimensions involving ¹³C spin-bearing nuclei, however, introduces essential complications arising from the wide frequency range of the ¹H-¹³C dipolar couplings and ¹³C CSA (>20 ​kHz), and the presence of the ¹³C-¹³C homonuclear dipole-dipole interactions. The recently reported ROULETTE-CAHA pulse sequence, in combination with the selective z-filtering, can be used to evolve the structurally informative ¹H-¹³C dipolar coupling arising from the aliphatic carbons while suppressing the signals from the carbonyl and methyl regions. Proton-mediated magnetization transfer under mismatched Hartman-Hahn conditions (MMHH) can be used to correlate ¹³C and ¹⁵N nuclei in such triple-resonance experiments for the subsequent ¹⁵N detection. The recently developed pulse sequences are illustrated for n-acetyl Leucine (NAL) single crystal and doubly labeled Pf1 coat protein reconstituted in magnetically aligned bicelles. An interesting observation is that in the case of ¹⁵N-labeled NAL measured at ¹³C natural abundance, the triple (¹H/¹³C/¹⁵N) MMHH scheme predominantly gives rise to long-range intermolecular magnetization transfers from ¹³C to ¹⁵N spins; whereas direct Hartmann-Hahn ¹³C/¹⁵N transfer is entirely intramolecular. The presented developments advance NMR of oriented samples for structure determination of membrane proteins and liquid crystals.

Optimization of NQR excitation sequences using black-box techniques

Nuclear quadrupole resonance is a spectroscopic technique that is dependent on the measurement conditions and one of its challenges is to find the optimal excitation sequence parameters that ensure the highest signal-to-noise ratio. This is typically performed empirically and this paper proposes a different approach by using black-box optimization techniques. This work explores several optimization algorithms in a black-box environment, where no model is required for the spectrometer and measurement conditions. The techniques can be applied to any excitation sequence, they can adapt to the measurement conditions and enable the automatic and simultaneous optimization of multiple parameters. Single and multi-parameter optimization experiments are performed on sodium nitrite and the algorithms are shown to converge to similar optimal values with small standard deviations. The use of an optimized excitation sequence is shown to improve the signal-to-noise ratio by approx. 8 dB compared to the initial sequence. Further research directions are discussed and include the study of different acquisition functions and the use of reinforcement learning algorithms.

Computer-generated pulse sequences for 1H-15N and 1Hα-13Cα separated local-field experiments

High-resolution separated local field (SLF) experiments are employed in oriented-sample solid state NMR to measure angular-dependent heteronuclear dipolar couplings for structure determination. While traditionally these experiments have been designed analytically by determining cycles of pulses with specific phases and durations to achieve cancellation of the homonuclear dipolar terms in the average Hamiltonian, recent work has introduced a computational approach to optimizing linewidths of the ¹H-¹⁵N dipolar resonances. Accelerated by GPU processors, a computer algorithm searches for the optimal parameters by simulating numerous ¹H-¹⁵N NMR spectra. This approach, termed ROULETTE, showed promising results by developing a new pulse sequence (ROULETTE-1.0) exhibiting 18% sharper mean linewidths than SAMPI4 for an N-acetyl Leucine (NAL) crystal. Herein, we expand on this previous work to improve the performance of the ¹H-¹⁵N SLF experiment and extend the work beyond the original approach to new SLF experiments. The new algorithm, in addition to finding pulse durations and phases, now searches for the optimal on/off application scheme of radio frequency irradiation on each channel. This constitutes true de novo optimization, effectively optimizing every aspect of a pulse sequence instead of just phases and durations. With an improved ROULETTE algorithm, we have found a new ¹H-¹⁵N pulse sequence, termed ROULETTE-2.0, yielding 32% sharper mean linewidths than SAMPI4 for NAL crystal at 500 MHz ¹H frequency. Whereas both SAMPI4 and ROULETTE-1.0 have a window where the rf power on the I-channel is turned off, the new pulse sequence is entirely windowless. Furthermore, the reliability of the algorithm has been greatly improved in terms of avoiding false positives, i.e. well-performing pulse sequences in silica that fail to render narrow resonances in experiment. The program has been extended to the ¹³C_α-¹H_α SLF experiments, using a 6 subdwell architecture similar to the ¹H-¹⁵N optimization. Compared to the PISEMA pulse sequence, the mean ¹³C_α-¹H_α linewidth is 17% sharper for the new pulse sequence, termed ROULETTE-CAHA. In addition to superior performance, the work demonstrates the broad applicability of the algorithm and its adaptability to different NMR experiments and spin systems.