Exploring the Impact of Protein Chain Selection in Binding Energy Calculations with DFT

Area of Science:

• Computational Chemistry
• Structural Biology
• Drug Discovery

Background:

• Accurate calculation of protein-ligand binding free energies is crucial for computer-aided drug design.
• ABL1 is a key enzyme targeted in chronic myeloid leukemia treatment.

Purpose of the Study:

• To approximate binding energies of ABL1 with minimal models using density functional theory (DFT).
• To investigate the impact of selecting different protein chains from crystal structures on binding energy calculations.
• To identify key factors contributing to protein-ligand binding.

Main Methods:

• Utilized minimal models and DFT to estimate binding free energies for ABL1-drug complexes.
• Analyzed all available protein chains within crystal structures, not just a single one.
• Performed Energy Decomposition Analysis (EDA) and Non-covalent Interactions (NCI) analysis.

Main Results:

• Observed significant differences in binding energies between protein chains within the same crystal structure.
• Identified exchange, repulsion, and electrostatics as primary contributors to binding.
• Desolvation terms showed substantial variation (4.2–92.3 kcal/mol) across inhibitors.
• DFT functionals exhibited consistent patterns in EDA and ligand discrimination.
• NCI analysis elucidated differences between protein chains and functionals.

Conclusions:

• Minimal models of drug-binding sites are effective for assessing initial crystal structure suitability for further analysis like EDA.
• The choice of protein chain can influence binding energy calculations, necessitating careful selection.
• DFT methods provide reliable insights into binding mechanisms and ligand interactions.