Heteronuclear transfers from labile protons in biomolecular NMR: Cross Polarization, revisited
Mihajlo Novakovic (1), Sundaresan Jayanthi (2), Adonis Lupulescu (3),, Maria Grazia Concilio (1), Jihyun Kim (1), David Columbus (1), Ilya Kuprov, (4), and Lucio Frydman (1) ((1) Department of Chemical, Biological, Physics, Weizmann Institute of Science, Rehovot

TL;DR
This paper revisits heteronuclear polarization transfer methods in biomolecular NMR, demonstrating that repeated projective operations and looped cross polarization improve transfer efficiency under fast solvent exchange conditions.
Contribution
It introduces Looped, Concatenated Cross Polarization (L-CCP), a novel method that enhances polarization transfer efficiency by combining repeated J-CP with algorithmic cooling, especially in challenging exchange environments.
Findings
Repeated J-CP improves transfer in exchange conditions.
L-CCP enhances polarization for 15N and 13C in proteins.
L-CCP enables high polarization in experiments on disordered proteins.
Abstract
INEPT- and HMQC-based pulse sequences are widely used to transfer polarization between heteronuclei, particularly in biomolecular spectroscopy: they are easy to setup and involve low power deposition. Still, these short-pulse polarization transfers schemes are challenged by fast solvent chemical exchange. An alternative to improve these heteronuclear transfers is J-driven cross polarization (J-CP), which transfers polarization by spin-locking the coupled spins under Hartmann-Hahn conditions. J-CP provides certain immunity against chemical exchange and other T2-like relaxation effects, a behavior that is here examined in depth by both Liouville-space numerical and analytical derivations describing the transfer efficiency. While superior to INEPT-based transfers, fast exchange may also slow down these J-CP transfers, hurting their efficiency. This study therefore explores the potential of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
