Abstract
In this study, we focus on developing computational methods to predict protein-ligand binding affinities, with applications in peptide drug discovery. Molecular Dynamics (MD) simulations can capture the complex conformational behavior of proteins, but their high computational cost limits their efficiency. MELD, or Modeling Employing Limited Data, is a Bayesian approach that integrates external information to accelerate sampling of low- energy, high-probability conformations. Building on previous work by Morrone et al., which successfully applied MELD to P53-MDM2 complexes, we hypothesize that we can effectively compute the relative binding affinities while reducing steric clashes and mitigating the effect of slowed diffusion on simulation convergence time. We present optimized MELD protocols that reproduce Morrone’s results within 1% of the target value, supporting the method’s accuracy and efficiency for peptide-based drug discovery.
Library of Congress Subject Headings
Molecular dynamics--Computer simulation; Ligand binding (Biochemistry); Protein binding
Publication Date
5-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Materials Science and Engineering (MS)
College
College of Science
Advisor
Emiliano Brini
Advisor/Committee Member
Scott Williams
Advisor/Committee Member
Lea Michel
Recommended Citation
Ciko, Maria, "Developing MELD-accelerated Molecular Dynamics Protocols to Simulate the Binding of the P53-Derived Ligand to the MDM-2, X Protein" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12156
Campus
RIT – Main Campus
Plan Codes
MSENG-MS
