Proteochemometric Modeling of the Interaction Space of Carbonic Anhydrase and its Inhibitors: An Assessment of Structure‐based and Sequence‐based Descriptors

Abstract

Due to its physiological and clinical roles, carbonic anhydrase (CA) is one of the most interesting case studies. There are different classes of CAinhibitors including sulfonamides, polyamines, coumarins and dithiocarbamates (DTCs). However, many of them hardly act as a selective inhibitor against a specific isoform. Therefore, finding highly selective inhibitors for different isoforms of CA is still an ongoing project. Proteochemometrics modeling (PCM) is able to model the bioactivity of multiple compounds against different isoforms of a protein. Therefore, it would be extremely applicable when investigating the selectivity of different ligands towards different receptors. Given the facts, we applied PCM to investigate the interaction space and structural properties that lead to the selective inhibition of CA isoforms by some dithiocarbamates. Our models have provided interesting structural information that can be considered to design compounds capable of inhibiting different isoforms of CA in an improved selective manner. Validity and predictivity of the models were confirmed by both internal and external validation methods; while Y-scrambling approach was applied to assess the robustness of the models. To prove the reliability and the applicability of our findings, we showed how ligands-receptors selectivity can be affected by removing any of these critical findings from the modeling process.

Exploring the origin of phosphodiesterase inhibition via proteochemometric modeling

“…2). In accordance with our previous work, 41 albeit slight changes, we modied the ALMOND algorithm and introduced an ALMOND-like algorithm with the ability to calculate the GRIND descriptors for complex structures such as protein cavity. Briey, our algorithm works by following 3 steps: (i) calculating MIFs using the program GRID 45 and nding the points with favorable interaction energies, (ii) reducing the points to those showing the best interaction energies, using the genetic algorithm.…”

“…In addition, thus far PCM have been successfully applied to investigate protein families such as G protein-coupled receptors, 30,31 proteases, 32,33 kinases, [34][35][36] antibodies, 37 cytochrome P450 38,39 and carbonic anhydrase. 40,41 While the majority of these researches have used sequence based descriptors for describing proteins, whereby the latter has shown a positive impact on molecular interaction eld (MIF) based descriptors called GRid-INdependent Descriptors (GRIND) on modeling and on the signicance of using z scales-GRINDs combinatorial descriptors. 41 Hence, in the present study, we developed a unied PCM model with the combination of z scales and MIF-based GRIND to investigate the interaction space and the selectivity between two subfamilies of PDE4 (PDE4B and PDE4D) and a series of their inhibitors.…”

“…40,41 While the majority of these researches have used sequence based descriptors for describing proteins, whereby the latter has shown a positive impact on molecular interaction eld (MIF) based descriptors called GRid-INdependent Descriptors (GRIND) on modeling and on the signicance of using z scales-GRINDs combinatorial descriptors. 41 Hence, in the present study, we developed a unied PCM model with the combination of z scales and MIF-based GRIND to investigate the interaction space and the selectivity between two subfamilies of PDE4 (PDE4B and PDE4D) and a series of their inhibitors. This approach provides the ability to nd differential structural features that can be taken into consideration for designing compounds with better selectivity towards PDE4B and PDE4D.…”

“…Since the positive impact of GRIND descriptors on modeling and the signicance of using z-scale-GRIND combinatorial descriptors have been already conrmed, 41 we applied a combination of z-scale descriptors and GRIND descriptors in our modeling. Prior to modeling, feature selection was carried out with regard to GRIND descriptors using genetic algorithm in order to nd the best tted structural descriptors.…”

Probing the origin of dihydrofolate reductase inhibition via proteochemometric modeling

“…One major strength of PCM is that it does not require structural information of proteins to provide specific information about their functions . Since its first introduction by Lapinsh et al in 2001, the approach has been successfully applied to investigate different protein families such as cytochrome P450, kinases, melanocortin receptors, G protein‐coupled receptors, HIV proteases, aromatases, carbonic anhydrases, and phosphodiesterases . Herein, we have applied a unified PCM model to investigate the interaction space and selectivity of a set of inhibitors toward different families of DHFR.…”