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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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Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

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Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide

Ajay N Jain¹, Alexander C Brueckner², Christine Jorge²

¹Research and Development, BioPharmics LLC, Sonoma County, CA, USA. ajain@jainlab.org.

Journal of Computer-Aided Molecular Design

|August 3, 2023

Summary

Optimizing macrocyclic peptide ligands is challenging. This study presents computational methods using NMR data and molecular similarity to predict binding energy, improving drug lead optimization for clinical candidates.

Keywords:

ForceGen Ligand-strain Macrocycle NMR PD-L1 Surflex-Dock eSim

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Constructing Cyclic Peptides Using an On-Tether Sulfonium Center

Constructing Cyclic Peptides Using an On-Tether Sulfonium Center

Published on: September 28, 2022

Area of Science:

Medicinal Chemistry
Computational Chemistry
Structural Biology

Background:

Systematic optimization of macrocyclic peptide ligands presents significant challenges in drug discovery.
The programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) pathway is a key target for cancer immunotherapy.

Purpose of the Study:

To develop and validate computational approaches for the lead optimization of macrocyclic peptide ligands.
To demonstrate the utility of these methods using the PD-1/PD-L1 system as a case study.

Main Methods:

Deriving conformational restraints from Nuclear Magnetic Resonance (NMR) data to identify low-energy solution ensembles.
Employing molecular docking with conformational restraints to predict bound ligand poses and estimate binding energy.
Utilizing a ligand-based approach with molecular similarity optimization to predict bound poses.

Main Results:

Both NMR-restrained docking and ligand-based similarity approaches effectively prioritized lead compound analogs.
Small ligand modifications can disproportionately impact estimated strain energy, significantly affecting overall binding energy predictions.
Accurate conformational search of macrocycles is critical for reliable binding energy estimation.

Conclusions:

A multi-disciplinary approach combining biophysical data with computational methods enhances the productivity of macrocyclic peptide lead optimization.
These strategies facilitate the transition from initial lead compounds to viable clinical candidates.