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¹H NMR of Conformationally Flexible Molecules: Temporal Resolution00:52

¹H NMR of Conformationally Flexible Molecules: Temporal Resolution

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At room temperature, the chair conformer of cyclohexane undergoes rapid ring flipping between two equivalent chair conformers at a rate of approximately 105 times per second. These two chair conformers are in equilibrium. The rapid ring flipping results in the interconversion of the axial proton to an equatorial proton and an equatorial to the axial proton. Such interconversions are too rapid and cannot be detected on the NMR timescale. Hence, the NMR spectrometer cannot distinguish between the...
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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
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¹H NMR: Complex Splitting01:13

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A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
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Assisting and accelerating NMR assignment with restrained structure prediction.

Sirui Liu1, Haotian Chu2, Yuhao Xie3

  • 1Changping Laboratory, Beijing, China. liusirui@cpl.ac.cn.

Communications Biology
|July 18, 2025
PubMed
Summary
This summary is machine-generated.

We developed AI tools (RASP and FAAST) that integrate experimental data for faster, more accurate protein structure prediction and analysis, crucial for drug design.

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Biophysics

Background:

  • Accurate protein structures are vital for drug design.
  • NMR spectroscopy provides structural and dynamic information but is labor-intensive.
  • Current AI predictions may not align with experimental data.

Purpose of the Study:

  • To develop an integrative approach combining AI and experimental data for protein structure determination.
  • To improve the accuracy and efficiency of AI-driven protein structure prediction.
  • To facilitate the analysis of experimental data, such as NMR spectra.

Main Methods:

  • Developed a Restraint Assisted Structure Predictor (RASP) model for improved AI-based structure prediction.
  • Created an iterative Folding Assisted peak ASsignmenT (FAAST) pipeline for rapid NMR NOESY analysis.
  • Integrated experimental restraints directly into AI models for enhanced accuracy.

Main Results:

  • RASP enhances structure prediction accuracy, particularly for challenging proteins (multi-domain, few-MSA).
  • FAAST pipeline significantly reduces NMR data analysis time to hours, producing high-quality structures.
  • Achieved high consistency between predicted structures and experimental restraints.

Conclusions:

  • The developed RASP and FAAST methods enable efficient and accurate protein structure determination.
  • This integrative strategy enhances AI-assisted structure prediction by leveraging experimental data.
  • The approach is adaptable for incorporating other sparse experimental information in structure prediction.