Proteochemometric modeling of the origin of thymidylate synthase inhibition

Abstract

Due to its crucial role in DNA synthesis, thymidylate synthase (TS) has been considered as a potential therapeutic target. Inhibition of the enzyme is a promising strategy for the treatment of some hyperproliferative diseases, including infections. As TS species-specific inhibitors would be able to distinguish between the host and the pathogens, developing highly selective inhibitors is of great clinical importance. TS is among the most highly conserved enzymes over evolutionary history, making the design of its species-selective inhibitor significantly challenging. The chemical interaction space, governed by a set of non-selective TS inhibitors, has been explored for human TS and its homologous proteins in both Toxoplasma gondii and Escherichia coli using proteochemometrics modeling (PCM). Validity, robustness, and prediction power of the PCM model have been assessed applying a diverse set of internal/external validation approaches. Our PCM model has provided major structural information, which is indeed of great help to design new TS species-specific inhibitors with the simultaneous inhibition ability toward both T. gondii and E. coli. To show applicability of the PCM model, new compounds have been designed based on structural information provided by the constructed model. Final results have been very promising with regard to selectivity ratios of the designed compounds for different TS isoforms, confirming the applicability of the PCM model.

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

“…However,G RIND descriptors were never used in any of these studies.O ur observations suggestt hat usingstructural information of proteins providedb yG RIND descriptors beside z-scales which are sequence basedd escriptors can improve modeling. To test whether this finding can be generalized for other datasets, we used the calculated GRIND descriptors in addition to z-scale descriptorsand re-modeled the data set we usedi no ur previous publication [51].F or the sake of comparisonw eo nly used isoforms I, II, IX, XII and feature selection for GRIND descriptors was performed using genetic algorithm. The new results were also in favor of using GRIND descriptors beside z-scales (Supplementary Ta ble 1, Supplementary Fig.…”

“…[35] PCM has been previously shown to be successful in many protein-ligand interactions studies including melanocortin receptors, [36][37][38] G-protein coupled receptors, [39,40] multiple mutated variants of HIV-1 protease andreverse transcriptase, [41][42][43][44] cytochromeP 450, [45,46] protein kinases, [47] dengue virus NS3 proteases, [48] histone deacetylases, [49] penicillinbindingp roteins, [50] and carbonic anhydrases. [51,52] Abstract:D ue to its physiological and clinical roles, carbonic anhydrase (CA) is one of the most interesting case studies. There are different classes of CAinhibitors includings ulfonamides, polyamines, coumarins and dithiocarbamates (DTCs).…”

“…The selectivity of some benzene/benzoic acid derivatives which act as low micromolar inhibitors, has been recently investigated for different isoformso fC A, using proteochemometrics approach by Rasti et al [51] It has been proposed that these compoundsi nhibit the enzymeb yl ocating in as ite betweent he phenol-and the coumarin-binding sites. [24] In the previous study we applied sequenceb ased protein descriptors to investigate the interaction space of CA/ligandc omplexes.…”

“…[24] In the previous study we applied sequenceb ased protein descriptors to investigate the interaction space of CA/ligandc omplexes. [51] Sequence basedp rotein descriptors have been the most widely used descriptors in PCM modeling;h owever,t here have beenafew attempts to improve PCM results by applying 3-D based protein descriptors. It has beenp reviously shown that applying 3-D descriptors sucha sl ocal descriptors of protein structure [53] and 3-D cavity descriptor FuzCav, [54,55] can positively affect the PCM modeling, while using 3-D information such as those encoded by the binding pocket kernel [56] and To p-Match similarity score [57,58] does not improve the modeling.…”

Probing the chemical interaction space governed by 4-amino-substituted benzenesulfonamides and carbonic anhydrase isoforms

“…To investigate the selectivity, proteochemometrics (PCM) approach can be applied since it considers interaction space of different ligands across multiple receptors ( 13 ). PCM investigations have so far shed light on valuable information regarding major protein families such as G protein-coupled receptors ( 14 ), proteases ( 15 ), thymidylate synthase ( 16 ), cytochrome P450 ( 17 ), CA ( 18 19 ) and phosphodiesterase ( 20 ). In the present study, we have developed a PCM model in which we applied different combinations of z-scale and molecular interaction field (MIF) based descriptors to investigate the chemical interaction space between six isoforms of CA and a series of sulfonamide-derivatives inhibitors.…”