Elucidating structural variability in p53 conformers using combinatorial refinement strategies and molecular dynamics

• Published in: Cancer biology & therapy Vol. 25; no. 1; p. 2290732
• Main Authors: Parves, Md Rimon, Solares, Maria J, Dearnaley, William J, Kelly, Deborah F
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 31.12.2024; Taylor & Francis Group
• Subjects: cryo-electron microscopy (EM); molecular dynamics; p53; real-space refinement; Research Paper; simulated annealing; Tumor suppressor protein; real-space refinement; cryo-electron microscopy (EM); simulated annealing; molecular dynamics; Tumor suppressor protein; p53
• DOI: 10.1080/15384047.2023.2290732
• ISSN: 1538-4047

Low molecular weight proteins and protein assemblies can now be investigated using cryo-electron microscopy (EM) as a complement to traditional structural biology techniques. It is important, however, to not lose sight of the dynamic information inherent in macromolecules that give rise to their exquisite functionality. As computational methods continue to advance the field of biomedical imaging, so must strategies to resolve the minute details of disease-related entities. Here, we employed combinatorial modeling approaches to assess flexible properties among low molecular weight proteins (~100 kDa or less). Through a blend of rigid body refinement and simulated annealing, we determined new hidden conformations for wild type p53 monomer and dimer forms. Structures for both states converged to yield new conformers, each revealing good stereochemistry and dynamic information about the protein. Based on these insights, we identified fluid parts of p53 that complement the stable central core of the protein responsible for engaging DNA. Molecular dynamics simulations corroborated the modeling results and helped pinpoint the more flexible residues in wild type p53. Overall, the new computational methods may be used to shed light on other small protein features in a vast ensemble of structural data that cannot be easily delineated by other algorithms.