Tryptophan hydroxylase (TPH) catalyses l-tryptophan into 5-hydroxy-l-tryptophan, which is the first and rate-limiting step of serotonin (5-HT) biosynthesis. Earlier, we found that TPH2 up-regulated in the hippocampus of postnatal rats after the oral treatment of Bacopa monniera leaf extract containing the active compound bacosides. However, the knowledge about the interactions between bacosides with TPH is limited. In this study, we take advantage of in silico approach to understand the interaction of bacoside-TPH complex using three different docking algorithms such as HexDock, PatchDock and AutoDock. All these three algorithms showed that bacoside A and A3 well fit into the cavity consists of active sites. Further, our analysis revealed that major active compounds bacoside A3 and A interact with different residues of TPH through hydrogen bond. Interestingly, Tyr235, Thr265 and Glu317 are the key residues among them, but none of them are either at tryptophan or BH4 binding region. However, its note worthy to mention that Tyr 235 is a catalytic sensitive residue, Thr265 is present in the flexible loop region and Glu317 is known to interacts with Fe. Interactions with these residues may critically regulate TPH function and thus serotonin synthesis. Our study suggested that the interaction of bacosides (A3/A) with TPH might up-regulate its activity to elevate the biosynthesis of 5-HT, thereby enhances learning and memory formation.
Amino acid repeats play an important role in the structure and function of proteins. Analysis of long repeats in protein sequences enables one to understand their abundance, structure and function in the protein universe. In the present study, amino acid repeats of length >50 (long repeats) were identified in a non-redundant set of UniProt sequences using the RADAR program. The underlying structures and functions of these long repeats were carried out using the Gene3D for structural domains, Pfam for functional domains and enzyme and non-enzyme functional classification for catalytic and binding of the proteins. From a structural perspective, these long repeats seem to predominantly occur in certain architectures such as sandwich, bundle, barrel, and roll and within these architectures abundant in the superfolds. The lengths of the repeats within each fold are not uniform exhibiting different structures for different functions. We also observed that long repeats are in the domain regions of the family and are involved in the function of the proteins. After grouping based on enzyme and non-enzyme classes, we observed the abundant occurrence of long repeats in specific catalytic and binding of the proteins. In this study, we have analyzed the occurrence of long repeats in the protein sequence universe apart from well-characterized short tandem repeats in sequences and their structures and functions of the proteins at the domain level. The present study suggests that long repeats may play an important role in the structure and function of domains of the proteins.
Background:
Coronary heart disease generally occurs due to cholesterol accumulation in the walls of the heart arteries. Statins are the most widely used drugs which work by inhibiting the active site of 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGCR) enzyme that is responsible for cholesterol synthesis. Series of atorvasatin analogs with HMGCR inhibition activity have been synthesised experimentally which would be expensive and time consuming.
Method:
In the present study, we employed both the QSAR model and chemical similarity search for identifying novel HMGCR inhibitors for heart related diseases. To implement this, a 2D QSAR model was developed by correlating the structural properties to their biological activity of a series of atoravastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of atorvastatin analogs was performed by using PubChem database search.
Results:
The three descriptor model of charge (GATS1p), connectivity (SCH-7) and distance (VE1_D) of the molecules is obtained for HMGCR inhibition with the statistical values of R2= 0.67, RMSEtr= 0.33, R2ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained by chemical similarily search and the inhibition activities of the compounds were calculated using QSAR model, which were close in the range of experimentally observed threshold.
Conclusion:
The present study suggests that QSAR model and chemical similarity search could be used in combination for identification of novel compounds with activity by in silico with less computation and effort.
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