Computational investigation of Moringa oleifera phytochemicals targeting EGFR: molecular docking, molecular dynamics simulation and density functional theory studies

Epidermal growth factor receptor (EGFR) is a prominent target for anticancer therapy due to its role in activating several cell signaling cascades. Clinically approved EGFR inhibitors are reported to show treatment resistance and toxicity, this study, therefore, investigates Moringa oleifera phytochemicals to find potent and safe anti-EGFR compounds. For that, phytochemicals were screened based on drug-likeness and molecular docking analysis followed by molecular dynamics simulation, density functional theory analysis and ADMET analysis to identify the effective inhibitors of EGFR tyrosine kinase (EGFR-TK) domain. Known EGFR-TK inhibitors (1-4 generations) were used as control. Among 146 phytochemicals, 136 compounds showed drug-likeness, of which Delta 7-Avenasterol was the most potential EGFR-TK inhibitor with a binding energy of -9.2 kcal/mol followed by 24-Methylenecholesterol (-9.1 kcal/mol), Campesterol (-9.0 kcal/mol) and Ellagic acid (-9.0 kcal/mol). In comparison, the highest binding affinity from control drugs was displayed by Rociletinib (-9.0 kcal/mol). The molecular dynamics simulation (100 ns) exhibited the structural stability of native EGFR-TK and protein-inhibitor complexes. Further, MM/PBSA computed the binding free energies of protein complex with Delta 7-Avenasterol, 24-Methylenecholesterol, Campesterol and Ellagic acid as -154.559 ± 18.591 kJ/mol, -139.176 ± 19.236 kJ/mol, -136.212 ± 17.598 kJ/mol and -139.513 ± 23.832 kJ/mol, respectively. Non-polar interactions were the major contributors to these energies. The density functional theory analysis also established the stability of these inhibitor compounds. ADMET analysis depicted acceptable outcomes for all top phytochemicals without displaying any toxicity. In conclusion, this report has identified promising EGFR-TK inhibitors to treat several cancers that can be further investigated through laboratory and clinical tests

In-silico prediction of TGF-β1 non-synonymous variants and their impact on binding affinity to Fresolimumab

TGF-β1 is a potent immunoregulatory cytokine that plays diverse roles in development, bone healing, fibrosis, and cancer. However, characterizing TGF-β1 gene variants is challenging because the structural and functional consequences of these variants are still undetermined. In this study, we aimed to perform an in-silico analysis of TGF-β1 non-synonymous variants and their pathogenic effects on the TGF-β1 protein. A total of 10,252 TGF-β1 SNPs were collected from the NCBI dbSNP database and in-silico tools (SIFT, PROVEAN, Mutation Taster, ClinVar, PolyPhen-2, CScape, MutPred, and ConSurf) were used. The in-silico predicted potential variants were further investigated for their binding to the TGF-β1 targeting drug “Fresolimumab”. Molecular docking was performed using HADDOCK and confirmed by PRODIGY and PDBsum. The in-silico analysis predicted four potential TGF-β1 nsSNPs: E47G in the LAP domain of the propeptide and I22T, L28F, and E35D in the mature TGF-β1 peptide. HADDOCK and molecular dynamics simulations revealed that the I22T and E35D variants have higher binding affinity for Fresolimumab as compared to the wild type and L28F variants. Molecular dynamics simulations (100 ns) and principal component analysis showed that TGF-β1 variants influenced the protein structure and caused variations in the internal dynamics of protein complexes with the antibody. Among them, the E35D variant significantly destabilized the TGF-β1 protein structure, resulting in rearrangement in the binding site and affecting the interactions with the Fresolimumab. This study identified four variants that can affect the TGF-β1 protein structure and result in functional consequences such as impaired response to Fresolimumab.: TGF-β1 is a potent immunoregulatory cytokine that plays diverse roles in development, bone healing, fibrosis, and cancer. However, characterizing TGF-β1 gene variants is challenging because the structural and functional consequences of these variants are still undetermined. In this study, we aimed to perform an in-silico analysis of TGF-β1 non-synonymous variants and their pathogenic effects on the TGF-β1 protein. A total of 10,252 TGF-β1 SNPs were collected from the NCBI dbSNP database and in-silico tools (SIFT, PROVEAN, Mutation Taster, ClinVar, PolyPhen-2, CScape, MutPred, and ConSurf) were used. The in-silico predicted potential variants were further investigated for their binding to the TGF-β1 targeting drug "Fresolimumab". Molecular docking was performed using HADDOCK and confirmed by PRODIGY and PDBsum. The in-silico analysis predicted four potential TGF-β1 nsSNPs: E47G in the LAP domain of the propeptide and I22T, L28F, and E35D in the mature TGF-β1 peptide. HADDOCK and molecular dynamics simulations revealed that the I22T and E35D variants have higher binding affinity for Fresolimumab as compared to the wild type and L28F variants. Molecular dynamics simulations (100 ns) and principal component analysis showed that TGF-β1 variants influenced the protein structure and caused variations in the internal dynamics of protein complexes with the antibody. Among them, the E35D variant significantly destabilized the TGF-β1 protein structure, resulting in rearrangement in the binding site and affecting the interactions with the Fresolimumab. This study identified four variants that can affect the TGF-β1 protein structure and result in functional consequences such as impaired response to Fresolimumab.Communicated by Ramaswamy H. Sarma