Title:Peptidic Compound as DNA Binding Agent: In Silico Fragment-based
Design, Machine Learning, Molecular Modeling, Synthesis, and DNA
Binding Evaluation
Volume: 31
Issue: 4
Author(s): Dara Dastan, Shabnam Soleymanekhtiari and Ahmad Ebadi*
Affiliation:
- Department of Medicinal Chemistry, School of Pharmacy,
Medicinal Plants and Natural Products Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
Keywords:
Cancer, ct-DNA, peptide, fragment-based design, machine learning, molecular modeling.
Abstract:
Background: Cancer remains a global burden, with increasing mortality rates. Current
cancer treatments involve controlling the transcription of malignant DNA genes, either directly or
indirectly. DNA exhibits various structural forms, including the G-quadruplex (G4), a secondary
structure in guanine-rich regions. G4 plays a crucial role in cellular processes by regulating gene
expression and telomerase function. Researchers have recently identified G4-stabilizing binding
agents as promising anti-cancer compounds. Additionally, peptides have emerged as effective anticancer
pharmaceuticals due to their ability to form multiple hydrogen bonds, electrostatic interactions,
and van der Waals forces. These properties enable peptides to bind to specific areas of DNA
chains selectively. However, despite these advancements, designing G4-binding peptides remains
challenging due to a lack of comprehensive information.
Objective: In our present study, we employed an in silico fragment-based approach to design G4-
binding peptides. This innovative method combines machine learning classification, molecular
docking, and dynamics simulation.
Methods: : AutoDock Vina and Gromacs performed molecular docking and MD simulation, respectively.
The machine learning algorithm was implemented by Scikit-learn. Peptide synthesis
was performed using the SPPS method. The DNA binding affinity was measured by applying spectrophotometric
titration.
Results: As a result of this approach, we identified a high-scoring peptide (p10; sequence: YWRWR).
The association constant (Ka) between p10 and the ctDNA double helix chain was 4.45 ×
105 M-1. Molecular modeling studies revealed that p10 could form a stable complex with the G4
surface.
Conclusion: The obtained Ka value of 4.45 × 105 M-1 indicates favorable interactions. Our findings
highlight the role of machine learning and molecular modeling approaches in designing new
G4-binding peptides. Further research in this field could lead to targeted treatments that exploit
the unique properties of G4 structures.
