{"title":"通过人工智能筛选和分子动态模拟鉴定用于胰腺癌治疗的新型 TGFßR1 抑制剂。","authors":"Samvedna Singh , Kiran Bharat Lokhande , Aman Chandra Kaushik , Ashutosh Singh , Shakti Sahi","doi":"10.1016/j.compbiolchem.2024.108262","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108262"},"PeriodicalIF":2.6000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AI screening and molecular dynamic simulation-driven identification of novel inhibitors of TGFßR1 for pancreatic cancer therapy\",\"authors\":\"Samvedna Singh , Kiran Bharat Lokhande , Aman Chandra Kaushik , Ashutosh Singh , Shakti Sahi\",\"doi\":\"10.1016/j.compbiolchem.2024.108262\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":10616,\"journal\":{\"name\":\"Computational Biology and Chemistry\",\"volume\":\"113 \",\"pages\":\"Article 108262\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Biology and Chemistry\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1476927124002500\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Biology and Chemistry","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1476927124002500","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
AI screening and molecular dynamic simulation-driven identification of novel inhibitors of TGFßR1 for pancreatic cancer therapy
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.
期刊介绍:
Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.
Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered.
Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.