Subject Area
Chemistry
Abstract
Molecular dynamics (MD) simulation is a powerful and versatile computational tool, that can be used in a wide range of applications from studying complex biological processes such as allostery to investigating the process of the protein-ligand binding in different computer-aided drug design implementations. In this work, various structural and drug discovery projects were performed by combining the state-of-art techniques molecular dynamics simulations, virtual screening, and binding free energy calculations with the innovative Markov State Model analysis and the machine learning techniques.
In the first project, the role of A’a helix as a key player in the transition between the light and the dark states in the allosteric mechanism of Avena Sativa phototropin 1 (AsLOV2) was studied using various computational tools as: 1.5 μs molecular dynamics simulations for each configuration, Markov state model, different machine learning techniques, and community analysis. The impact of the A’a helix was studied on the atomistic level by introducing two groups of mutations, helicity enhancing mutations (T406A and T407A) and helicity disrupting mutations (L408D and R410P), as well as on the overall secondary structure by using the community analysis.
In the second part of the dissertation, we focus on developing anticancer drugs against Forkhead box protein C2 (FOXC2), which was found to be common in several types of tumors due to its essential role in the initiation and maintenance of the epithelial-mesenchymal transition (EMT) process. Augmenting ligand-based drug design (LBDD) by searching for similar analogs for MC-1-F2 (the only identified experimental inhibitor FOXC2) against 15 million compounds from ChEMBL and ZINC databases with structure-based drug design (SBDD) and de novo structure-based drug design by developing a three-dimensional (3D) structure of the full-length FOXC2 using homology modeling led to the identification of eight promising lead compounds.
In the third project, multi-layered filtration based on physicochemical properties and docking scores was implied for the initiation of the virtual screening of two different peptidomimetics databases against S1 and S2 subunits of the trimeric spike protein in the Wild type (WT) and the Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2). Guided by the interaction of the developed monoclonal neutralizing antibodies, 4 peptidomimetics compounds were found to be effective against both the WT and the Omicron S1 subunit with minimum binding free energy of -25 kcal/mol and 5 peptidomimetics compounds were found to be effective against the S2 subunit with minimum binding free energy of -19 kcal/mol.
Lastly, we investigated the effect of the ligands’ binding on the allosteric communication between transcriptional enhanced associate domain 4 (TEAD4) and yes-associated protein 1 (YAP1). In the Hippo-off state, overexpression of YAP1 was found to be common in several types of solid tumors. This overexpression is harnessed by the protein-protein interaction between TEAD4 and YAP1. Inspired by the structure of TED-347 inhibitor, computational modification was performed for the original inhibitors, identified from the screening of DNA-encoding library against the central pocket of TEAD4, by replacing the secondary methyl amide functional group with chloromethyl ketone moiety.
Degree Date
Spring 5-13-2023
Document Type
Dissertation
Degree Name
Ph.D.
Department
Chemistry
Advisor
Peng Tao
Number of Pages
223
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Mohamed, Mayar, "Protein Dynamics in Allostery and Computer-Aided Drug Design" (2023). Chemistry Theses and Dissertations. 38.
https://scholar.smu.edu/hum_sci_chemistry_etds/38
