The secondary structure reveals the recurring arrangements in space of adjacent amino acid residues in the cytokine. The proportion of the coil region demonstrates the stability of the protein . The corresponding amino acid sequence was submitted to I-TASSER server to generate an ensemble of structural conformations when matched with template sequences identified by LOMETS, a meta-server containing multiple threading programs, from the PDB library (Table 1). The templates are experimentally determined protein structures with maximum similarity with the submitted sequence aligned through TM-align structural alignment program algorithms . The templates of the highest significance in the threading alignments measured by the Z-score were used. The generated ensemble of structural conformations, called decoys, were clustered through the SPICKER program based on the pair-wise structure similarity to predict the top five models ranked based on their corresponding C-scores (Table 2, Fig. 5). The respective quality of the tertiary structures of the predicted models from I-TASSER was assessed through the Ramachandran plots obtained from PROCHECK, a server that determines the stereochemical quality of protein structures and QMEAN, an accuracy prediction score [20,21,22]. The obtained values were lower than the acceptable values of the respective analysis whereas an acceptable protein model would be predictable to have over 90% in the most the favoured regions in the Ramachandran plot based on an analysis of 118 structures of resolution of at least 2.0 Å and the QMEAN4 value must be around zero and not below − 4.0 as an indication of the degree of nativeness of the model to an experimental structure of similar size . Thus, further refinements of the predicted models were performed to minimize the probable errors in the predicted structures via the GalaxyRefine2 server and ranked based on their respective Galaxy energies. The server adopts an iterative optimization approach for refining proteins and generates several models with more structural deviations from the submitted model. The iteratively refined models were further validated through Ramachandran plots obtained from PROCHECK and QMEAN [19,20,21]. The best model was selected objectively based on the quality of the protein structure. The Ramachandran plot obtained for the quality assessment of the selected model showed that 90.0% of the structure was under favoured region (red area), 8.6% was under the allowed region (yellow area), and 1.4% was observed under the disallowed region, signalling a high quality of the predicted structure (Fig. 7). The amino acid residues in the disallowed region are VAL-27, ALA-28, and ASN-109. Additionally, the qualitative model energy analysis showed a QMEAN Z-scores value of − 3.06, which is an acceptable score as the value is greater than − 4.0 (Fig. 8a) . This indicates the homology model has a good agreement with the experimental structures of similar size. The predicted Z-score compares the interaction potential between Cβ-interaction energy, all atoms pair-wise energy, the solvation potential, and the torsion angle potential . The estimate of the local quality (Fig. 8b) shows a larger proportion of the residues (x-axis) of the model have high quality in comparison with the native structure (y-axis) with scores above 0.6 (Fig. 8c). The comparison with the non-redundant set of PDB structures relates the quality scores of the model with obtainable scores for experimental structures of similar size. This showed the model has a normalized QMEAN4 score within 1.0 and 2 of the standard deviation of the mean of z score (|Z-score|). This is also indicated by the sequence coloured by local quality (Fig. 8d, e).
Activation of TNFR in response to TNFL8 stimulation induces the recruitment of signalling proteins that mediate signal transduction events which are capable of eliciting stimulatory signals such as the production of the pro-inflammatory chemokine  Targeting TNFRSF8/TNFL8 interactions has been reported to be a useful mechanism in the regulation of pathophysiologic roles of TNFRSF8/TNFL8 activities mostly in oncology and chronic inflammatory diseases . Therapeutic antibodies targeting the TNF family members have been found to exert antagonist signalling effects on the physiologic functions of TNFRSF8 by blocking TNFRSF8/TNFL8 interactions. Moreover, therapeutic agents derived from natural sources have been reported to possess the ability to suppress the expression and signalling of TNF family members . Furthermore, consumption of strawberry significantly decreased soluble TNFR in a randomized cross-over double-blind placebo-controlled trial . The predicted binding sites (Fig. 9) identified through COFACTOR could be targets of potential substrates or inhibitor. The conformational analysis of the cytokine with ligands was simulated through molecular docking to analyse the molecular interaction of the bioactive compounds from Xylopia aethiopica (Dunal) A. Rich fruit with the selected model. The ligands selected were based on a previous report on the interaction of TNFL8 with the extracts of X. aethiopica . X. aethiopica is a spice that has been used in orthodox medicine for the management of various diseases associated with a dysfunctional inflammatory response in various regions of West Africa . The pharmacologically active compounds present in the fruit include L-pinocarveol, 13-epimanoyl oxide, 4-terpineol, apigenin, caffeic acid, chlorogenic acid, cis-α-copaene-8-ol, cumic alcohol, ellagic acid, eudesma-1,3-dien-11-ol, kaempferol, linoleic acid, linolenic acid, myrtenol, o-cymene, oleic acid, palmitic acid, palmitoleic acid, quercetin, rutin, stearic acid, xylopic acid, α-pinene, α-terpineol, 1,8-cineole, and β-pinene [33, 35, 36].
Different therapeutic compounds could induce distinctive pharmacodynamic responses based on variability in their binding modes and binding affinities. The binding-free energies reflect the respective binding affinity of the eight phytochemicals to the model. The results showed that the cytokine interacted favourably with the selected phytochemicals with ellagic acid having the highest binding affinity since it possesses the least binding-free energy followed by rutin and apigenin as presented in Table 2, while caffeic acid was the least among the eight phytochemicals with the binding energy of −n5.22 ± 0.26 kcal/mol. This indicated the bioactive compounds from the fruit Xylopia aethiopica could impair the functionality of the cytokine as presented through the in vivo study. These phytochemicals have earlier been reported by different studies to downregulate many degenerative processes particularly those that are related to inflammatory responses [34, 37].
The prediction of binding sites and visualization of relevant non-covalent interactions in the 3D structures were carried out via the PLIP server to understand the biochemical functionality vis-à-vis the responses of the protein to drugs (Table 2, Fig. 10). Xylopic acid and quercetin formed common hydrophobic interaction patterns with PRO-69 and a hydrogen bond to ASP-66 via their carboxyl groups. All the selected ligands interacted with the TNFL8 through hydrophobic contacts and hydrogen bindings. Caffeic acid, chlorogenic acid, and ellagic acid formed salt bridges with some amino acid residues of the cytokine via their carboxyl groups. Individual interaction sub-patterns were identified among the ligands with one of the most unique interaction patterns revealed in the complex with ellagic acid and rutin, where analogous π-stacking with HIS-122 and π-cation interactions with ARG-64 occurred. π-Stacking which indicates the non-covalent interactions between aromatic rings and π-cation interactions have long been known as major constituents of ligand-protein interfaces [38, 39]. Furthermore, the interactions of the phytochemicals with the cytokine were found to occur in two binding pockets for the different ligands. The ligands found in pocket A are chlorogenic acid, apigenin, rutin, and ellagic acid, while those in pocket B are xylopic acid, caffeic acid, quercetin, and kaempferol (Fig. 11). This implies that the binding of phytochemicals could induce allosteric modulation which could thus regulate the physiological functions of the cytokine . Common residues found in pocket A where chlorogenic acid, apigenin, rutin, and ellagic acid bound include ARG-64 and ASN-70 whereas PRO-69 was common in pocket B where xylopic acid, caffeic acid, quercetin, and kaempferol are present. This study indicates that the selected bioactive compounds most especially ellagic acid, apigenin, and rutin which demonstrated the highest docking interaction energies toward the cytokine could have sufficient specificities and potencies to bind and modulate the function of TNFL8. Moreover, ASN-70 and ARG-64 in pocket A to which the phytochemicals with lower binding energies anchored could be the key residues at the active site of the cytokine. Notably, the interaction model proposed corroborated a previously available study on the inhibition of the expressions of splenic TNFRSF8 by ethanolic extract of X. aethiopica .
The flexibility of the cytokine and its complexes with the selected phytochemicals were simulated using an online tool (CABS-flex 2.0 server) . The cytokine-phytochemical interactions generated RMSF graphs were evaluated based on the RMSF. The protein structural simulation generated RMSF graphs showed the flexibility of the amino acids were relatively low for the complexes while compared to the wild-type TNFL8 without bound phytochemicals. Caffeic acid had relatively higher fluctuations compared to the other phytochemicals. The changes in the fluctuations of the residues at the pockets where the phytochemical binding occurred indicate the interactions of the selected phytochemicals at the active sites of TNFL8 could enhance the rigidity of the amino acids in the active sites. Thus, the binding of the phytochemicals with TNFL8 could influence the TNFL8/TNFRSF8 interactions and subsequence biologic functions and the innate capability to initiate cellular responses. This could corroborate the therapeutic actions of these compounds to disorders that are associated with inflammatory responses [34, 37].