Exploring the substructural space of indole-3-carboxamide derivatives binding to renin: a novel active-site spatial partitioning approach

Synthetic strategies, SAR studies, and computer modeling of indole 2 and 3-carboxamides as the strong enzyme inhibitors: a review

Abstract

Indole derivatives have been the focus of many researchers in the study of pharmaceutical compounds for many years. Researchers have investigated the effect of carboxamide moiety at positions 2 and 3, giving unique inhibitory properties to these compounds. The presence of carboxamide moiety in indole derivatives causes hydrogen bonds with a variety of enzymes and proteins, which in many cases, inhibits their activity. In this review, synthetic strategies of indole 2 and 3-carboxamide derivatives, the type, and mode of interaction of these derivatives against HLGP, HIV-1, renin enzyme, and structure–activity studies of these compounds were investigated. It is hoped that indole scaffolds will be tested in the future for maximum activity in pharmacological compounds.

Graphic abstract

The critical aggregation concentration of peptide surfactants is predictable from dynamic hydrophobic property

Peptide surfactants are a kind of newly emerged functional materials, which have a variety of applications such as building nanoarchitecture, stabilizing membrane proteins and controlling drug release. In the present study, we report the modelling and prediction of critical aggregation concentration (CAC), an important parameter that characterizes the self-assembling behaviour of peptide surfactants through the use of statistical modelling and quantitative structure-property relationship (QSPR) approaches. In order to accurately describe the structural and physicochemical properties of the highly flexible peptide molecules, a new method called molecular dynamics-based hydrophobic cross-field (MD-HCF) is proposed to capture both the hydrophobic profile and dynamic feature of 32 surface-activity, structure-known peptides. A number of statistical models are then developed using partial least squares (PLS) regression with or without improvement by genetic algorithm (GA). We demonstrate that MD-HCF performs much better than the widely used CODESSA method in both its predictability and interpretability. We also highlight the importance of dynamic hydrophobic property in accurate prediction and reasonable explanation of peptide self-assembling behaviour in solution, albeit which is exhaustive to compute compared with those derived directly from peptide static structure. To the best of our knowledge, this study is the first to computationally model and predict the self-assembling behaviour of peptide surfactants.