Computational Approaches to Improve Aggrecanase-1 Inhibitory Activity of (4-keto) Phenoxy) Methyl Biphenyl-4-sulfonamide: Group Based QSAR and Docking Studies
TL;DR: Docking of the compounds with aggrecanase-1 enzyme showed that there is a strong negative correlation between the binding energy and aggre Canase- 1 inhibitory activity.
Abstract: Group based Quantitative Structure Activity Relationship (GQSAR) was developed for thirty (4-keto-phenoxy) methyl biphenyl-4-sulfonamides which exhibit aggrecanase-1 enzyme inhibitory activity. This enzyme is involved in osteoarthritis. The data is divided into training and test sets, where the latter is used for validating the model. Substitution in the R1 position plays a major role when compared to substitution in R2 position. The former position is influenced by two descriptors, namely electrotopological and connectivity indices. R2 position is influenced by radius of gyration. The statistical parameters for the training set (r2 = 0.80, r2adj = 0.77, q2 = 0.69, F-ratio = 26.80 and standard error = 0.24) and the predicted r2 (r2 test =0.95) are satisfactory. Docking of the compounds with aggrecanase-1 enzyme showed that there is a strong negative correlation between the binding energy and aggrecanase -1 inhibitory activity. Compounds with the carbonyl substitution interact with the S'1 pocket which is needed for enhanced activity. The two methodologies described here can help in lead optimization.
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TL;DR: A novel fragment‐based QSAR model has been developed using 22 pyrazole‐derived compounds exhibiting inhibitory activity against Leishmanial CRK3, and it is implied that electron‐rich substitution at R1 position improves the inhibitoryActivity, while decline in inhibitory Activity was observed in presence of nitrogen at R2 position.
Abstract: The CRK3 cyclin-dependent kinase of Leishmania plays an important role in regulating the cell-cycle progression at the G2/M phase checkpoint transition, proliferation, and viability inside the host macrophage. In this study, a novel fragment-based QSAR model has been developed using 22 pyrazole-derived compounds exhibiting inhibitory activity against Leishmanial CRK3. Unlike other QSAR methods, this fragment-based method gives flexibility to study the relationship between molecular fragments of interest and their contribution for the variation in the biological response by evaluating cross-term fragment descriptors. Based on the fragment-based QSAR model, a combinatorial library was generated, and top two compounds were reported after predicting their activity. The QSAR model showed satisfactory statistical parameters for the data set (r(2) = 0.8752, q(2) = 0.6690, F-ratio = 30.37, and pred_r(2) = 0.8632) with four descriptors describing the nature of substituent groups and the environment of the substitution site. Evaluation of the model implied that electron-rich substitution at R1 position improves the inhibitory activity, while decline in inhibitory activity was observed in presence of nitrogen at R2 position. The analysis carried out in this study provides a substantial basis for consideration of the designed pyrazole-based leads as potent antileishmanial drugs.
21 citations
Cites methods from "Computational Approaches to Improve..."
...Group-based QSAR (GQSAR) is a fragment-based method that gives flexibility to study the relationship between molecular fragments of interest and their contribution for the variation in the biological response by evaluating cross-term fragment descriptors (8)....
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...Unlike the conventional QSAR methods, GQSAR can be applied to both congeneric (template-based approach) and noncongeneric series (user-defined scheme) of compounds to obtain site-specific clues where it has to be optimized for designing new molecules and quantitatively predicting their activity (8)....
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01 Jan 2021
TL;DR: The recent advances and applications of docking techniques along with the challenges faced to cope up with the current needs of researches, particularly in structural biology and drug design are reported.
Abstract: Molecular complex formation, via intermolecular interactions, is widely studied using the computational method called molecular docking. The efficiency of molecular docking in (1) exploring molecular recognition via predicting the affinity as well as binding mode between the interacting molecules, for example, protein–ligand interactions, and (2) identifying drug-like molecules from a pool of pharmaceutically interesting compounds, etc., is well proven in the field of drug design via computer-aided drug design. Here, we report the recent advances and applications of docking techniques along with the challenges faced to cope up with the current needs of researches, particularly in structural biology and drug design. The complexity in sampling the conformational landscape of the interacting molecules arises due to the increased degrees of freedom resulted from structural flexibility, proton placements/exchange, etc. The challenging aspects discussed in this chapter, especially in fine-tuning the scoring function, are expected to improve its accuracy.
3 citations