Comparative Molecular Surface Analysis Research Articles

Probability-driven 3D pharmacophore mapping of antimycobacterial potential of hybrid molecules combining phenylcarbamoyloxy and N-arylpiperazine fragments

ABSTRACTThe current study examines in silico characterization of the structure-inhibitory potency for a set of phenylcarbamic acid derivatives containing an N-arylpiperazine scaffold, considering the electronic, steric and lipophilic properties. The main objective of the ligand-based modelling was the systematic study of classical comparative molecular field analysis (CoMFA)/comparative molecular surface analysis (CoMSA) performance for the modelling of in vitro efficiency observed for these phenylcarbamates, revealing their inhibitory activities against a virulent Mycobacterium tuberculosis H37Rv strain. We compared the findings of efficiency modelling produced by a standard 3D methodology (CoMFA) and its neural counterparts (CoMSA) regarding multiple training/test subsets and variables used. Moreover, systematic space inspection, splitting values into the analysed training/test subsets, was performed to monitor statistical estimator performance while mapping the probability-driven pharmacophore pattern. Consequently, a ‘pseudo-consensus’ 3D-quantitative structure-activity relationship (3D-QSAR) approach was applied to retrieve an ‘average’ pharmacophore hypothesis by the investigation of the most densely populated training/test subpopulations to specify the potentially important factors contributing to the inhibitory activity of phenylcarbamic acid analogues. In addition, examination of descriptor-based similarity with a principal component analysis (PCA) procedure was employed to visualize noticeable variations in the performance of these molecules with respect to their structure and activity profiles.

Towards Intelligent Drug Design System: Application of Artificial Dipeptide Receptor Library in QSAR-Oriented Studies.

The pharmacophore properties of a new series of potential purinoreceptor (P2X) inhibitors determined using a coupled neural network and the partial least squares method with iterative variable elimination (IVE-PLS) are presented in a ligand-based comparative study of the molecular surface by comparative molecular surface analysis (CoMSA). Moreover, we focused on the interpretation of noticeable variations in the potential selectiveness of interactions of individual inhibitor-receptors due to their physicochemical properties; therefore, the library of artificial dipeptide receptors (ADP) was designed and examined. The resulting library response to individual inhibitors was arranged in the array, preprocessed and transformed by the principal component analysis (PCA) and PLS procedures. A dominant absolute contribution to PC1 of the Glu attached to heptanoic gating acid and Phe bonded to the linker m-phenylenediamine/triazine scaffold was revealed by the PCA. The IVE-PLS procedure indicated the receptor systems with predominant Pro bonded to the linker and Glu, Gln, Cys and Val directly attached to the gating acid. The proposed comprehensive ligand-based and simplified structure-based methodology allows the in-depth study of the performance of peptide receptors against the tested set of compounds.

Proline-Based Carbamates as Cholinesterase Inhibitors.

Series of twenty-five benzyl (2S)-2-(arylcarbamoyl)pyrrolidine-1-carboxylates was prepared and completely characterized. All the compounds were tested for their in vitro ability to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), and the selectivity of compounds to individual cholinesterases was determined. Screening of the cytotoxicity of all the compounds was performed using a human monocytic leukaemia THP-1 cell line, and the compounds demonstrated insignificant toxicity. All the compounds showed rather moderate inhibitory effect against AChE; benzyl (2S)-2-[(2-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (IC50 = 46.35 μM) was the most potent agent. On the other hand, benzyl (2S)-2-[(4-bromophenyl)-] and benzyl (2S)-2-[(2-bromophenyl)carbamoyl]pyrrolidine-1-carboxylates expressed anti-BChE activity (IC50 = 28.21 and 27.38 μM, respectively) comparable with that of rivastigmine. The ortho-brominated compound as well as benzyl (2S)-2-[(2-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate demonstrated greater selectivity to BChE. The in silico characterization of the structure–inhibitory potency for the set of proline-based carbamates considering electronic, steric and lipophilic properties was provided using comparative molecular surface analysis (CoMSA) and principal component analysis (PCA). Moreover, the systematic space inspection with splitting data into the training/test subset was performed to monitor the statistical estimators performance in the effort to map the probability-guided pharmacophore pattern. The comprehensive screening of the AChE/BChE profile revealed potentially relevant structural and physicochemical features that might be essential for mapping of the carbamates inhibition efficiency indicating qualitative variations exerted on the reaction site by the substituent in the 3′-/4′-position of the phenyl ring. In addition, the investigation was completed by a molecular docking study of recombinant human AChE.

Modeling corrosion inhibition efficacy of small organic molecules as non-toxic chromate alternatives using comparative molecular surface analysis (CoMSA)

Traditionally many structural alloys are protected by primer coatings loaded with corrosion inhibiting additives. Strontium Chromate (or other chromates) have been shown to be extremely effectively inhibitors, and find extensive use in protective primer formulations. Unfortunately, hexavalent chromium which imbues these coatings with their corrosion inhibiting properties is also highly toxic, and their use is being increasingly restricted by legislation. In this work we explore a novel tridimensional Quantitative-Structure Property Relationship (3D-QSPR) approach, comparative molecular surface analysis (CoMSA), which was developed to recognize “high-performing” corrosion inhibitor candidates from the distributions of electronegativity, polarizability and van der Waals volume on the molecular surfaces of 28 small organic molecules. Multivariate statistical analysis identified five prototypes molecules, which are capable of explaining 71% of the variance within the inhibitor data set; whilst a further five molecules were also identified as archetypes, describing 75% of data variance. All active corrosion inhibitors, at a 80% threshold, were successfully recognized by the CoMSA model with adequate specificity and precision higher than 70% and 60%, respectively. The model was also capable of identifying structural patterns, that revealed reasonable starting points for where structural changes may augment corrosion inhibition efficacy. The presented methodology can be applied to other functional molecules and extended to cover structure-activity studies in a diverse range of areas such as drug design and novel material discovery.

Receptor independent and receptor dependent CoMSA modeling with IVE-PLS: application to CBG benchmark steroids and reductase activators

Comparative molecular surface analysis (CoMSA) with robust IVE-PLS variable elimination if tested for the benchmark CBG steroid series provides highly predictive RI 3D QSAR models, but failed however to model the activity of sulforaphane (SP) activators of quinone reductase. The application of the SP poses obtained from multipose molecular docking to model the RD IVE-PLS CoMSA resulted in a predictive form. This model indicated lipophilic potential as the activity determinant. The individual molecular surface areas of the highest contribution to the SP activity was identified and visualized by CoMSA contour plots.

Modeling Robust QSAR. 2. Iterative Variable Elimination Schemes for CoMSA: Application for Modeling Benzoic Acid pKaValues

A variety of issues decide the efficiency of 3D QSAR methods, and their practical importance for drug design is still controversial. This refers both to the predictive ability and the possibility for the indication of these areas within 3D molecular representations that are responsible for biological or chemical effects. Technically, the latter comes down to the selection or elimination of the reliable variables during 3D QSAR modeling using the Partial Least-Squares (PLS) method. In this paper we used a series of benzoic acids to test the dependence between the predictive ability and variable selection performance of PLS with Iterative Variable Elimination (IVE-PLS) in the Comparative Molecular Surface Analysis (CoMSA) modeling of Hammett constant which correlates with the pKa values. Modeling this chemical effect allowed us to select the IVE-PLS variant that plots the contour maps indicating a carboxylic function, i.e., the region including the dissociation reaction center that determines the respective pKa values. In fact, it appeared that a novel robust IVE version is capable of the indication of the proper contour plots independent of the method used for the calculation of partial atomic charges (AM1 or Gasteiger-Marsili).

Drug design using comparative molecular surface analysis

Chemists have eagerly extended a concept of shape into the molecular scale in an attempt to explain molecular effects. Although the molecular surface that shapes a molecule is a fuzzy category, which makes important theoretical problems with its precise interpretation, shape analysis appears to be an efficient method providing an explanation for a variety of effects. Comparative molecular surface analysis (CoMSA) is a three-dimensional quantitative structure–activity relationship (3D QSAR) method used for the comparison of molecular surfaces and shapes. In this review, the authors demonstrate that 3D QSAR basically works as a visualisation tool that investigates molecular similarity and indicates pharmacophoric sites. In this context, CoMSA that is based on the Kohonen self-organising maps that allow the projection of three-dimensional molecular data into two-dimensional maps without topological distortion has an important advantage over the traditional 3D QSAR methods. However, It should be remembered that the majority of the 3D QSAR limitations are still important for this technique and consequently a strategy to use the molecular data to produce drugs in this way still maps a ‘far and bumpy road’.

Comparative Molecular Surface Analysis (CoMSA) for Virtual Combinatorial Library Screening of Styrylquinoline HIV-1 Blocking Agents

We used comparative molecular surface analysis to design molecules for the synthesis as part of the search for new HIV-1 integrase inhibitors. We analyzed the virtual combinatorial library (VCL) constituted from various moieties of styrylquinoline and styrylquinazoline inhibitors. Since imines can be applied in a strategy of dynamic combinatorial chemistry (DCC), we also tested similar compounds in which the -C=N- or -N=C- linker connected the heteroaromatic and aromatic moieties. We then used principal component analysis (PCA) or self-organizing maps (SOM), namely, the Kohonen neural networks to obtain a clustering plot analyzing the diversity of the VCL formed. Previously synthesized compounds of known activity, used as molecular probes, were projected onto this plot, which provided a set of promising virtual drugs. Moreover, we further modified the above mentioned VCL to include the single bond linker -C-N- or -N-C-. This allowed increasing compound stability but expanded also the diversity between the available molecular probes and virtual targets. The application of the CoMSA with SOM indicated important differences between such compounds and active molecular probes. We synthesized such compounds to verify the computational predictions.

3D QSAR study of hypolipidemic asarones by comparative molecular surface analysis

Three-dimensional quantitative structure–activity relationship (3D QSAR) modeled for α-asarone derivatives using the comparative molecular surface analysis (CoMSA) allowed us to reveal a correlation between the activity of these compounds and the electrostatic potential at the molecular surface. The grid formalism (s-CoMSA) allowed us to indicate a pharmacophore that is of key importance for compound activity. The CoMSA formalism coupled with the iterative variable elimination method gives a highly predictive model.

Modeling Robust QSAR. 1. Coding Molecules in 3D-QSAR from a Point to Surface Sectors and Molecular Volumes

Shape analysis is a powerful tool in chemistry and drug design. In the current work, we compare the results of CoMFA and Comparative Molecular Surface Analysis (CoMSA), the 3D-QSAR method, for a series of hypolipidemic and antiplatelet asarones and antifungal N-myristoyltransferase inhibitors. In this publication we show that a sector CoMSA formalism enables an analysis of the biological activity that is more directly related to the molecular shape and individual molecular functionalities than the traditional uniform and directionless CoMFA field. Iterative Variable Elimination allowed us to identify the potential pharmacophoric sites. We modeled QSARs for both series and demonstrate that sector-based molecular descriptors give very predictive models and allow one to generate a spatial interpretation of the QSAR models. In particular, we identified the central aromatic ring and carbonyl functions as the moieties determining the activity of the asarones series, while the pattern of substitution of the aromatic ring determines the activity of N-myristoyltransferase inhibitors.

Probability Issues in Molecular Design: Predictive and Modeling Ability in 3D-QSAR Schemes

In the current work we investigated 3D-QSAR data by the use of the coupled leave-several-out (LSO) and leave-one-out (LOO) cross-validation (CV) procedures. We verified the above mentioned scheme using both simulated data and real 3D QSAR data describing a series of CoMFA steroids, heterocyclic azo dyes and styrylquinoline HIV integrase inhibitors. Unlike in standard analyses, this technique characterizes individual method not by a single performance metrics but screens a whole possible modeling space by sampling different molecules into the training and test sets, respectively. This allowed us for the discussion of the information included in the estimators validating cross-validation procedures, as well as the comparison of the efficiency of several 3D QSAR schemes, in particular, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Surface Analysis (CoMSA). Moreover, it allows one to acquire some general knowledge about predictive and modeling ability in 3D QSAR method.

GRID Formalism for the Comparative Molecular Surface Analysis: Application to the CoMFA Benchmark Steroids, Azo Dyes, and HEPT Derivatives.

Shape analysis is a powerful tool in chemistry and drug design, and molecular surface defines shape in the molecular scale. In the current publication we presented a novel formalism for the comparative molecular surface analysis (s-CoMSA). The method enables both quantitative modeling of 3D-QSAR and finding possible pharmacophoric sites. The method provides very predictive models for the CBG activity of the benchmark steroid series, tinctorial properties of the heterocyclic azo dyes and anti-HIV activity of the HEPT series.

Comparative Molecular Surface Analysis (CoMSA) for Modeling Dye—Fiber Affinities of the Azo and Anthraquinone Dyes.

Despite recent investigations aimed at modeling 3D QSAR for dye molecules a controversy still exists: can a pharamacophore hypothesis be used for such purposes. In the present publication we reported on the application of the CoMSA method for modeling 3D QSAR of azo and anthraquinone dyes. We obtained very predictive models, which significantly outperform those reported in the previous CoMFA studies, especially for the azo dyes. Our results proved the previous conclusion that steric requirements are far less pronounced for the cellulose cavities than for the classical drug receptor. Moreover, our results indicate that all molecular surface segments are important for dye−fiber interactions, which also makes an important difference in relation to the classical drug pharmacophore. On the other hand, high predictivity of the CoMSA models indicates that a pharmacophore concept is suitable for the description of the dye−fiber interactions. However, this pharmacophore must substantially differ from the drug pha...

Mapping dye pharmacophores by the Comparative Molecular Surface Analysis (CoMSA): application to heterocyclic monoazo dyes

Despite recent investigations aimed at developing for dye molecules Quantitative Structure Activity Relationships on the basis of the data that describe 3D molecular structure of these molecules (so-called 3D QSARs), it is still uncertain whether a pharamacophore hypothesis can be used for modeling dye–cellulose affinities. We have used the Comparative Molecular Surface Analysis (CoMSA) method for modeling the cellulose affinity of heterocyclic monoazo dyes. We used the molecule of the highest cellulose affinity as the template pattern. Moreover, we also used so-called alternative mapping strategy with smaller molecular fragments applied as different templates to investigate the contribution of the individual molecular moieties to the compound–cellulose attraction and for an estimation of the relative importance of the steric and electrostatic interactions. We obtained models significantly outperforming those reported in Comparative Molecular Field Analysis studies. Our results proved the previous hypothesis that polar interactions are decisive for the dye–fiber affinity. Moreover, the highly predictive models that were obtained indicate that a pharamacophore (or more precisely tinctophore) concept can be used efficiently for the description of the dye–fiber interactions.

3D-QSAR study of antifungal N-myristoyltransferase inhibitors by comparative molecular surface analysis

The correlation between the molecular electrostatic potential (MEP) on benzofuran inhibitors and the N-myristoyltransferase (Nmt) inhibitory activity is investigated by a three-dimensional quantitative structure–activity relationship (3D-QSAR) approach. To each compound, a Kohonen neural network (KNN) is used to project the MEP values on a Connolly surface into a two-dimensional map. Each node in the KNN map is coded by the associated MEP value of the occupying sampling point and all the coding nodes are collected together to define chemical descriptors. The best partial least squares (PLS) model with the selected descriptors is searched among the possible candidate models by genetic algorithm (GA). The resulting best model is significantly more predictive than the one with all chemical descriptors. The model has given additional information about the binding of a benzofuran inhibitor to Nmt.

Comparative molecular surface analysis (CoMSA) for modeling dye-fiber affinities of the azo and anthraquinone dyes.

Despite recent investigations aimed at modeling 3D QSAR for dye molecules a controversy still exists: can a pharamacophore hypothesis be used for such purposes. In the present publication we reported on the application of the CoMSA method for modeling 3D QSAR of azo and anthraquinone dyes. We obtained very predictive models, which significantly outperform those reported in the previous CoMFA studies, especially for the azo dyes. Our results proved the previous conclusion that steric requirements are far less pronounced for the cellulose cavities than for the classical drug receptor. Moreover, our results indicate that all molecular surface segments are important for dye-fiber interactions, which also makes an important difference in relation to the classical drug pharmacophore. On the other hand, high predictivity of the CoMSA models indicates that a pharmacophore concept is suitable for the description of the dye-fiber interactions. However, this pharmacophore must substantially differ from the drug pharmacophore used for the illustration of the drug-receptor interactions. From a theoretical point of view dye-cellulose interactions can be an interesting case in which shape decides the activity rules not by the steric repulsion but as a cofactor determining the electrostatic potential distribution.

The Comparative Molecular Surface Analysis (CoMSA) with Modified Uniformative Variable Elimination‐PLS (UVE‐PLS) Method: Application to the Steroids Binding the Aromatase Enzyme.

ChemInformVolume 34, Issue 22 Other Subjects The Comparative Molecular Surface Analysis (CoMSA) with Modified Uniformative Variable Elimination-PLS (UVE-PLS) Method: Application to the Steroids Binding the Aromatase Enzyme. Jaroslaw Polanski, Jaroslaw Polanski Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this authorRafal Gieleciak, Rafal Gieleciak Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this author Jaroslaw Polanski, Jaroslaw Polanski Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this authorRafal Gieleciak, Rafal Gieleciak Inst. Chem., Silesian Univ., PL-40-006 Katowice, Pol.Search for more papers by this author First published: 28 May 2003 https://doi.org/10.1002/chin.200322231AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume34, Issue22June 3, 2003 RelatedInformation

ChemInform Abstract: The Comparative Molecular Surface Analysis (COMSA) — A Nongrid 3D QSAR Method by a Coupled Neural Network and PLS System: Predicting pKa Values of Benzoic and Alkanoic Acids.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

The Comparative Molecular Surface Analysis (COMSA) − A Nongrid 3D QSAR Method by a Coupled Neural Network and PLS System: Predicting pKa Values of Benzoic and Alkanoic Acids

A self-organizing neural network was used to design a novel method capable of the quantitative prediction of molecular properties. The method is based on the comparison of molecular surfaces performed by the coupled neural network and PLS system. Unlike CoMFA and related methods it does not compare the properties describing a discrete set of points but the average property values calculated for a certain area of the molecular surface. It has been found that the results of the PLS analysis of the series of the comparative matrices of the molecular electrostatic potential (MEP) are quite stable. Also the results only slightly depend on such parameters as the number of points sampled at the molecular surface (D) or a winning distance (MD) of the self-organizing neurons. The influence of these parameters for modeling the effects limited by steric and electronic effects was determined and the pK(a) values of the ortho-, meta-, and para- (o-, m-, p-) analogues of benzoic acid and selected alkanoic acids were predicted. We generally found that for the series analyzed CoMSA gave better models than CoMFA.

The comparative molecular surface analysis (COMSA): a novel tool for molecular design

A new method allowing for 3-D QSAR analysis and the prediction of biological activity is presented. Unlike comparative molecular field analysis (CoMFA)-like techniques, it is based not on a comparison of the properties characterizing a discrete set of points but on the mean electrostatic potential (MEP) calculated and labeling specific areas defined on the molecular surface. A Kohonen self-organizing neural network and partial least square (PLS) analysis have been used for performing such an operation. The series of steroids complexing the corticosteroid (CBG) and testosterone (TBG) globulins, which forms a benchmark measuring the performance of the methods in molecular design, and a series of benzoic acids described by the Hammett σ constants is used for testing the method. It is demonstrated that a method can be used efficiently to evaluate the responses determined both by the combination of electrostatic and steric effects or by electrostatic effects alone, therefore, two different schemes were developed. The first one, which involves PLS analysis of the full comparative networks, covers both steric and electrostatic effects. This scheme works well for both the CBG and TBG data. The second scheme takes into account only the properties (MEP) of these regions within molecules that can be superimposed with the template molecule. This scheme provides the best predictive power for the benzoic acids series. Comparison of the results from a CoMFA analysis proves that method is at least as effective for the responses limited by electrostatic effects, although it significantly outperforms CoMFA for CBG affinity which is dominated by steric effects.