“…In order to determine the conformation of the peptide, the values are plotted against one another, and the angular spectrum ranges between 180 and +180 degrees on both the x- and y-axes. The Ramachandran outlier is a depiction of those amino acids that reside in areas of the plot that are not favorable [ 44 ].…”
Polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) are widespread across the globe, existing in the environment in complex mixtures potentially capable of initiating respiratory illnesses. Here, we use an in silico approach to evaluate the potential pro-inflammatory effects of various carcinogenic PAHs and VOCs through their binding affinity towards the human toll-like receptor 4 (TLR4). For receptors and ligands, RCSB Protein Data Bank and PubChem were used in obtaining their 3D structures, respectively. Autodock Vina was utilized to obtain the best docking poses and binding affinities of each PAH and VOC. Out of the 14 PAHs included in this study, indeno(1,2,3-cd)pyrene, benzo(ghi)perylene, and benzo[a]pyrene had the highest binding affinity values of −10, −9, and −8.9 kcal/mol, respectively. For the VOCs, out of the 10 compounds studied, benzene, 1,4-dichlorobenzene, and styrene had the highest binding affinity values of −3.6, −3.9, and −4.6 kcal/mol, respectively. Compounds with higher affinity than LPS (−4.1 kcal/com) could potentially induce inflammation, while compounds with lower affinity would be less likely to induce an inflammatory response. Meanwhile, molecular dynamics simulation and RMSF statistical analysis proved that the protein, TLR4, stably preserve its conformation despite ligand interactions. Overall, the structure of the TLR4 was considered inflexible.
“…In order to determine the conformation of the peptide, the values are plotted against one another, and the angular spectrum ranges between 180 and +180 degrees on both the x- and y-axes. The Ramachandran outlier is a depiction of those amino acids that reside in areas of the plot that are not favorable [ 44 ].…”
Polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) are widespread across the globe, existing in the environment in complex mixtures potentially capable of initiating respiratory illnesses. Here, we use an in silico approach to evaluate the potential pro-inflammatory effects of various carcinogenic PAHs and VOCs through their binding affinity towards the human toll-like receptor 4 (TLR4). For receptors and ligands, RCSB Protein Data Bank and PubChem were used in obtaining their 3D structures, respectively. Autodock Vina was utilized to obtain the best docking poses and binding affinities of each PAH and VOC. Out of the 14 PAHs included in this study, indeno(1,2,3-cd)pyrene, benzo(ghi)perylene, and benzo[a]pyrene had the highest binding affinity values of −10, −9, and −8.9 kcal/mol, respectively. For the VOCs, out of the 10 compounds studied, benzene, 1,4-dichlorobenzene, and styrene had the highest binding affinity values of −3.6, −3.9, and −4.6 kcal/mol, respectively. Compounds with higher affinity than LPS (−4.1 kcal/com) could potentially induce inflammation, while compounds with lower affinity would be less likely to induce an inflammatory response. Meanwhile, molecular dynamics simulation and RMSF statistical analysis proved that the protein, TLR4, stably preserve its conformation despite ligand interactions. Overall, the structure of the TLR4 was considered inflexible.
“…13 , the Ramachandran plot's Φ and Ψ angles are used to evaluate the characteristics of the selected protein chains [ 56 ]. A large portion of the residues of amino acids of the proteins is in the permissible zone [ 57 ]. Fig.…”
“…Briefly, the methods can be separated on the basis of the use of known protein structures as templates to build the target (template-based modeling) or models generated solely from the amino acid sequence using first principles of physics when limited template homology is available (de novo modeling). 242 The Rosetta software suite enables protein structure prediction and has been used to predict 3D models of proteins with and without experimental data. 243 Rosetta works by first sampling the conformational space allowed by the amino acids in a protein.…”
Section: Engineered Affinity Interactions By Computational Designmentioning
confidence: 99%
“…Primary computational methods for determining protein structure include homology, threading, fragment-based, and de novo approaches. Briefly, the methods can be separated on the basis of the use of known protein structures as templates to build the target (template-based modeling) or models generated solely from the amino acid sequence using first principles of physics when limited template homology is available ( de novo modeling) . The Rosetta software suite enables protein structure prediction and has been used to predict 3D models of proteins with and without experimental data .…”
Section: Engineered Affinity Interactions
By Computational
Designmentioning
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