Related Experiment Video
Updated: Jul 22, 2026

Utilizing High Resolution Ultrasound to Monitor Tumor Onset and Growth in Genetically Engineered Pancreatic Cancer Models
Published on: April 7, 2018
AI screening and molecular dynamic simulation-driven identification of novel inhibitors of TGFßR1 for pancreatic
Samvedna Singh1, Kiran Bharat Lokhande2, Aman Chandra Kaushik3
1School of Biotechnology, Gautam Buddha University, Greater Noida, UP, India.
Abstract:
Pancreatic cancer, with a 5-year survival rate below 10 %, is one of the deadliest malignancies. The TGF-ß pathway plays a crucial role in this disease, making it a key target for therapeutic intervention. Clinical trials targeting TGF-β have faced challenges of toxicity and limited efficacy, highlighting the need for more potent small molecule inhibitors. We selected TGFßR1 as the drug target to inhibit TGF-ß signaling in pancreatic cancer. A multi-faceted approach was employed, commencing with AI-driven screening techniques to rapidly identify potential TGFßR1 inhibitors from vast compound libraries, including the ZINC and ChEMBL databases. AI-screened compounds were further validated through structure-based high-throughput virtual screening (HTVS) to evaluate their binding affinity to TGFßR1. In addition to this, a dedicated library of anticancer compounds (65,000 compounds) and protein kinase inhibitors (36,324 compounds) were also used for HTVS. Subsequently, pharmacokinetic profiling narrowed the selection to 40 hit compounds. Five hit compounds were chosen based on binding affinity, non-bonded interactions, stereochemistry, and pharmacokinetic profiles for molecular dynamics (MD) simulations. Trajectory analysis showed that residues HIS283, ASP351, LYS232, SER280, ILE211, and LYS213 within TGFßR1's active site are crucial for ligand binding through hydrogen bonds and hydrophobic interactions. Principal component analysis (PCA) and Dynamic cross-correlation matrix (DCCM) analysis were used to evaluate the receptor's dynamic response to the hit compounds. The simulation data revealed that compounds 1, 2, 3, 4, and 5 formed stable complexes with TGFßR1. Notably, post-MDS MM-GBSA analysis showed that compounds 4 and 5 exhibited exceptionally strong binding energies of -81.0 kcal/mol and -85.5 kcal/mol, respectively. The comprehensive computational analysis confirms compounds 4 and 5 as promising TGFßR1 hits with potential therapeutic applications in development of new treatments for pancreatic cancer.
Insights
Researchers identified potent small molecule inhibitors for TGFßR1 to combat deadly pancreatic cancer. Compounds 4 and 5 showed exceptional binding, offering new therapeutic potential for pancreatic cancer treatment.
Area of Science:
- Computational chemistry and drug discovery
- Oncology and molecular biology
Background:
- Pancreatic cancer has a low survival rate, necessitating novel therapeutic strategies.
- The TGF-ß pathway is implicated in pancreatic cancer, making TGFßR1 a key drug target.
- Previous TGF-β targeting trials faced efficacy and toxicity issues, requiring improved inhibitors.
Purpose of the Study:
- To identify novel small molecule inhibitors targeting TGFßR1 for pancreatic cancer.
- To computationally evaluate the binding affinity and stability of potential inhibitors.
Main Methods:
- Utilized AI-driven screening and structure-based high-throughput virtual screening (HTVS) on compound libraries.
- Performed pharmacokinetic profiling, molecular dynamics (MD) simulations, and binding energy analysis (MM-GBSA).
- Analyzed key residues, receptor dynamics (PCA, DCCM), and ligand-protein interactions.
Main Results:
- Identified five hit compounds with stable complexes formed with TGFßR1.
- Compounds 4 and 5 demonstrated exceptionally strong binding energies (-81.0 kcal/mol and -85.5 kcal/mol, respectively).
- Key interactions involving residues HIS283, ASP351, LYS232, SER280, ILE211, and LYS213 were identified.
Conclusions:
- Compounds 4 and 5 are promising TGFßR1 inhibitors with potential for pancreatic cancer therapy.
- The study validates a computational approach for identifying effective small molecule inhibitors.
More Related Videos
07:32Screening and Identification of Small Peptides Targeting Fibroblast Growth Factor Receptor2 using a Phage Display Peptide Library
Published on: September 30, 2019
07:47Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
Published on: September 15, 2023
Related Concept Videos
Targeted Cancer Therapies
There are several types of targeted therapies against specific...
TGF - β Signaling Pathway