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Proteochemometric Modeling of the Interaction Space of Carbonic Anhydrase and its Inhibitors: An Assessment of Structure‐based and Sequence‐based Descriptors
Author(s) -
Rasti Behnam,
Namazi Mohsen,
KarimiJafari M. H.,
Ghasemi Jahan B.
Publication year - 2017
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201600102
Subject(s) - carbonic anhydrase , sequence (biology) , computational biology , computer science , space (punctuation) , carbonic anhydrase ii , data mining , chemistry , bioinformatics , biochemistry , biology , enzyme , operating system
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.

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