Judging Category
Basic or Experimental Research
Student Rank
Senior
College
Sciences and Mathematics
Faculty Sponsor
Hideya Koizumi hkoizumi@astate.edu
Description
Understanding how proteins bind small molecules is essential for accurate computational modeling of biological systems. However, calculating binding free energy (delta G), enthalpy (delta H), and entropy (delta S) becomes unreliable when proteins are flexible, and ligands are between multiple binding positions. This motion causes traditional computational methods to produce inconsistent thermodynamic values.
A computational workflow was implemented that included docking to predict likely binding positions, molecular dynamics simulations to sample how the protein and ligand move over time, and statistical mechanics analysis to calculate thermodynamic quantities. A new theoretical framework was developed and implemented in a custom program to compute delta G, delta H, and delta S in a way that reduces errors caused by structural flexibility and ligand motion.
The method was applied to a kinase-ligand system known to challenge conventional approaches. Direct comparison showed that standard calculations produced inconsistent thermodynamic values, whereas the statistical mechanics-based method generated stable and physically meaningful results. The calculated parameters were consistent with experimental crystallographic data.
These findings demonstrate a more reliable strategy for estimating thermodynamic properties in flexible protein-ligand systems and may improve future computational modeling studies.
Disciplines
Computational Chemistry | Physical Chemistry
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Dulaney, Mitchell, "Robust Thermodynamic Analysis of Flexible Protein-Ligand Binding Using Statistical Mechanics" (2026). Create@State. 41.
https://arch.astate.edu/evn-createstate/2026/posters/41
Robust Thermodynamic Analysis of Flexible Protein-Ligand Binding Using Statistical Mechanics
Understanding how proteins bind small molecules is essential for accurate computational modeling of biological systems. However, calculating binding free energy (delta G), enthalpy (delta H), and entropy (delta S) becomes unreliable when proteins are flexible, and ligands are between multiple binding positions. This motion causes traditional computational methods to produce inconsistent thermodynamic values.
A computational workflow was implemented that included docking to predict likely binding positions, molecular dynamics simulations to sample how the protein and ligand move over time, and statistical mechanics analysis to calculate thermodynamic quantities. A new theoretical framework was developed and implemented in a custom program to compute delta G, delta H, and delta S in a way that reduces errors caused by structural flexibility and ligand motion.
The method was applied to a kinase-ligand system known to challenge conventional approaches. Direct comparison showed that standard calculations produced inconsistent thermodynamic values, whereas the statistical mechanics-based method generated stable and physically meaningful results. The calculated parameters were consistent with experimental crystallographic data.
These findings demonstrate a more reliable strategy for estimating thermodynamic properties in flexible protein-ligand systems and may improve future computational modeling studies.
