Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY

Area of Science:

• Biophysics
• Structural Biology
• Computational Chemistry

Background:

• NMR-based drug screening relies on chemical shift mapping to identify protein-drug binding sites.
• Manual mapping is inefficient for high-throughput screening due to spectral complexities and errors.
• Existing methods struggle with accurate one-to-one peak assignment in large proteins.

Purpose of the Study:

• To develop an automated and accurate method for chemical shift mapping in NMR-based drug screening.
• To improve the efficiency of identifying drug-binding residues on target proteins.
• To enable high-throughput drug screening using limited NMR data and structural information.

Main Methods:

• Developed PeakWalker, a novel peak walking algorithm for fast-exchange systems, explicitly modeling errors and performing many-to-one mapping.
• Introduced PeakAssigner, a structure-based backbone resonance and NOE assignment algorithm utilizing ¹⁵N-NOESY.
• Modeled chemical shift mapping as a graph problem using a mathematical programming approach.

Main Results:

• PeakWalker achieved over 95% average accuracy on hBclXL, UbcH5B, and histone H1 proteins.
• PeakAssigner resolved ambiguities for one-to-one mapping with 94% average accuracy, avoiding TOCSY and ¹³C-labeling.
• The algorithms demonstrated efficiency and accuracy for identifying drug-binding sites.

Conclusions:

• The developed mathematical programming approach offers a time- and cost-effective solution for NMR-based drug screening.
• These algorithms facilitate high-throughput screening by automating complex chemical shift mapping and assignment processes.
• The methods are particularly valuable when using limited NMR data and homologous 3D structures.