Comparative Molecular Field Analysis Analysis Research Articles

Integration of virtual screening of phytoecdysteroids as androgen receptor inhibitors by 3D-QSAR Model, CoMFA, molecular docking and ADMET analysis: An extensive and interactive machine learning

Ecdysteroids, a class of naturally isolated polyhydroxylated sterols, stands at a very good place in the pharmaceutical industry from their medicinal point of views like anti-inflammatory, neuroprotective, anti-microbial, anti-diabetic, antioxidant, and anti-tumor effects. Due to their excellent antioxidant and anti-microbial potential, ecdysteroids have extensive use in skin products, especially derma creams. To monitor the best anti-acne phytoecdysteroids, here made use of different computational approaches, by using the rapid, easy, cost-effective and high throughput method to screen and identify ecdysteroids as androgen receptor inhibitors. 3D-QSAR study was carried out on a dataset of ecdysteroids by using comparative molecular field analysis (CoMFA) to determine the factors responsible for the activity of compounds. Statistically a cross-validated (q2) 0.1457 and regression coefficient (r2) 0.9713 indicated the best model. Contour map results showed the influence of steric effect to enhance activity. A molecular docking analysis was done to further find out the binding sites and their anti-acne potential against three crystal structured macromolecules (PDB ID: 2REQ, 2BAC, 4EM0). Docking results were further evaluated by prime MM-GBSA analysis and findings confirmed the accuracy. Toxicity by ADMET assessment was carried out and M2 was found as lead druglike with best anti-acne activity against Propionium acnes GehA lipase bacteria after passing all filters. This research study is novel because it is representing first effort to explore ecdysteroids class for their high therapeutic output as androgen receptor inhibitor by using computational tools and expectedly led to novel scaffold for androgen receptor inhibitor. This is a novel and new approach to investigate the ecdysteroids for first time for their practical applications.

QSAR Modelling of Biological Activity in Cannabinoids with Quantum Similarity Combinations of Charge Fitting Schemes and 3D-QSAR.

Quantitative structure-activity relationship(QSAR) modeled the biological activities of 30 cannabinoids with quantum similarity descriptors(QSD) and Comparative Molecular Field Analysis (CoMFA). The PubChem[https://pubchem.ncbi.nlm.nih.gov/] database provided the geometries, binding affinities(Ki ) to the cannabinoid receptors type 1(CB1) and 2(CB2), and the median lethal dose(LD50 ) to breast cancer cells. An innovative quantum similarity approach combining (self)-similarity indexes calculated with different charge-fitting schemes under the Topo-Geometrical Superposition Algorithm(TGSA) were used to obtain QSARs. The determination coefficient(R2 ) and leave-one-out cross-validation[Q2 (LOO)] quantified the quality of multiple linear regression and support vector machine models. This approach was efficient in predicting the activities, giving predictive and robust models for each endpoint [pLD50 : R2 =0.9666 and Q2 (LOO)=0.9312; pKi (CB1): R2 =1.0000 and Q2 (LOO)=0.9727, and pKi (CB2): R2 =0.9996 and Q2 (LOO)=0.9460], where p is the negative logarithm. The descriptors based on the electrostatic potential encrypted better electronic information involved in the interaction. Moreover, the similarity-based descriptors generated unbiased models independent of an alignment procedure. The obtained models showed better performance than those reported in the literature. An additional 3D-QSAR CoMFA analysis was applied to 15 cannabinoids, taking THC as a template in a ligand-based approach. From this analysis, the region surrounding the amino group of the SR141716 ligand is the more favorable for the antitumor activity.

3D-QSAR modeling and molecular docking studies on a series of 2, 4, 5-trisubstituted imidazole derivatives as CK2 inhibitors

Protein case in kinase II alpha subunit (CK2) plays an imperative function in treating cancer disease. Herein, we have performed a three-dimensional quantitative structure activity relationship (3D-QSAR), and molecular docking analysis on a novel series of 2, 4, 5-trisubstituted imidazole derivatives in order to design potent kinase II alpha subunit (CK2) inhibitors. The 3D-QSAR methods such as comparative molecular similarity indexes analysis (COMSIA), and the comparative molecular field analysis (COMFA) were investigate using twenty-four molecules of 2, 4, 5-trisubstituted imidazole derivatives as anticancer agent. The best COMFA and COMSIA models exhibit excellent values of 0.66 and 0.75 and values of 0.98 and 0.99 respectively. To check the validity of the selected COMFA and COMSIA models, a variety of validation tests were utilized: Internal validation analyses, and externally validation beside Y-randomization according to the principles of the Organization for Economic Co-operation and Development (OECD), and the Golbraikh and Tropsha’s criteria for the validation of 3D-QSAR models. The proposed models for COMFA and COMSIA analysis have been successful. The developed models, indicating that they were reliable for activity prediction. Based on the preceding results, we designed several new potent molecules. Such outcome can proffer helpful theoretical references for future experimental studies. Communicated by Ramaswamy H. Sarma

Insight into the structural requirement of aryl sulphonamide based gelatinases (MMP-2 and MMP-9) inhibitors – Part I: 2D-QSAR, 3D-QSAR topomer CoMFA and Naïve Bayes studies – First report of 3D-QSAR Topomer CoMFA analysis for MMP-9 inhibitors and jointly inhibitors of gelatinases together

ABSTRACT Gelatinases [gelatinase A – matrix metalloproteinase-2 (MMP-2), gelatinase B – matrix metalloproteinase-9 (MMP-9)] play key roles in many disease conditions including cancer. Despite some research work on gelatinases inhibitors both jointly and individually had been reported, challenges still exist in achieving potency as well as selectivity. Here in part I of a series of work, we have reported the structural requirement of some arylsulfonamides. In particular, regression-based 2D-QSARs, topomer CoMFA (comparative molecular field analysis) and Bayesian classification models were constructed to refine structural features for attaining better gelatinase inhibitory activity. The 2D-QSAR models exhibited good statistical significance. The descriptors nsssN, SHBint6, SHBint7, PubchemFP629 were directly correlated with the MMP-2 binding affinities whereas nsssN, SHBint10 and AATS2i were directly proportional to MMP-9 binding affinities. The topomer CoMFA results indicated that the steric and electrostatic fields play key roles in gelatinase inhibition. The established Naïve Bayes prediction models were evaluated by fivefold cross validation and an external test set. Furthermore, important molecular descriptors related to MMP-2 and MMP-9 binding affinities and some active/inactive fragments were identified. Thus, these observations may be helpful for further work of aryl sulphonamide based gelatinase inhibitors in future.

Three-dimensional Quantitative Structure-activity Relationship (3DQSAR) and Molecular Docking Study of 2-((pyridin-3-yloxy)methyl) Piperazines as α7 Nicotinic Acetylcholine Receptor Modulators for the Treatment of Inflammatory Disorders.

Comparative molecular field analysis (CoMFA) of 27 analogues of 2-((pyridin-3-yloxy)methyl)piperazine derivatives was carried out using software Tripos SYBYL X. Optimal r2 (0.854) and q2 (0.541) values were obtained for the developed 3D-QSAR model. The contour plots obtained from CoMFA analysis have shown 13.84% steric contribution and 66.14% electrostatic contribution towards an anti-inflammatory activity. The homology model of the receptor protein, α7 nicotinic acetylcholine, was generated in SWISS MODELLER using auto template mode and was analysed for the quality using Procheck, QMEAN Z-score, Anolea and GROMOS plots. The QMEAN score for the model was observed to be - 3.862. The generated model of alpha 7 nicotinic acetylcholine receptor was used for docking study of 27 piperazine analogues using Auto-Dock 4.2.5.1. The dock score obtained from docking analysis was then correlated with experimental pIC50 values for in-silico validation of the developed CoMFA model and a good correlation was obtained with correlation coefficient (r2) value of -0.7378. The present investigation suggests an optimal 3D-QSAR with CoMFA model for further evaluating new chemical entities based on piperazine skeleton.

QSAR and Docking Studies of DATA Analogues as HIV-1 Reverse Transcriptase Inhibitors

Background: Acquired Immunodeficiency Syndrome (AIDS) caused by Human Immunodeficiency Virus (HIV) has seriously threatened human health, so development of new, selective and safe non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) remains a high priority for medical research. Diaryltriazine (DATA) have been identified as a new class of potent nonnucleoside HIV-1 Reverse Transcriptase (RT) inhibitors. The study deals with Topomer CoMFA (Comparative Molecular Field Analysis) and molecular docking to explore the important features of DATA analogues for exerting potent HIV-1 RT inhibitors activity. Methods: In this work, 40 DATA analogues were studied using a combination of molecular modeling techniques including Three-Dimensional Quantitative Structure–Activity Relationship (3D-QSAR), molecular docking, and Topomer CoMFA were used to build 3D-QSAR models. Results: The results show that the Topomer CoMFA analysis has the cross-validation q2 = 0.800, SDCV = 0.45, the non-cross-validated r2 = 0.958, SD = 0.21, and the correlation coefficient of external validation Q2 ext = 0.965 showed that the model is reasonable and credible, and has a good predictive ability. Then binding mode pattern of the compounds to the binding site of enzyme was confirmed and the mechanism of drug and acceptor was studied by docking studies, the results showed that the drug and GLU138, LYS101, THR139 sites have an obvious function, these researches have provided an useful information for designing more effective HIV-1IN inhibitors. Conclusion: A series of 40 DATAs analogues was subjected to a 3D-QSAR study. Using Topomer CoMFA 3D-QSAR method built model, and the model has shown a good predictive and statistical validation. Substituent with low electronic density in the R5 and R3 positions and substituent with high electronic density in the R2 and C2 positions will increase the biological activity, small substituent on R4 positions and naphthyloxy as the spacer group C6 substituent hydrophobic will increase biological activity. This effect is supported by Topomer CoMFA contour map and docking results of HIV-1RT inhibition active site, the results of the 3D-QSAR and docking analyses have provided a guide for the synthesis of new putative inhibitors for HIV-1RT to improved inhibitory activity.

Insights into the Structural Requirements of Potent Brassinosteroids as Vegetable Growth Promoters Using Second-Internode Elongation as Biological Activity: CoMFA and CoMSIA Studies.

In the present study, we have employed the ligand-based drug design technique, 3D-QSAR, through a comparative molecular field analysis (CoMFA) and a comparative molecular similarity indices analysis (CoMSIA) to determine the key factors for the plant growth promoting activity of brassinosteroids reported in literature, using the bean second-internode bioassay measured on two groups of compounds with different molar concentrations. This is the first 3D-QSAR study using the second internode elongation as biological activity. These results provide useful ideas for the design of new molecules, which could be explored in the future to identify novel vegetable growth promoters with similar or greater biological activity than natural brassinosteroids. The reliability of this study was supported by the robust statistical parameters obtained from CoMFA (Model A, r2pred = 0.751; Model B, r2pred = 0.770) and CoMSIA (Model A, r2pred = 0.946; Model B, r2pred = 0.923) analysis.

3D-QSAR (CoMFA, CoMSIA) and molecular docking studies of natural flavonoid derivatives to explore structural requirements for MCF-7 cell line inhibition

In the present study, molecular modelling studies have been reported on a series of natural flavonoid derivatives to analyse structure activity relationship studies of MCF-7 inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)-based QSAR methods. Eighty-four compounds were used as training set to establish the model and 20 compounds were used as external test set to validate these models. The generated models exhibited good statistical results such as q2, r2. CoMFA analysis yielded the q2 of 0.765 and r2 of 0.968 with five components. CoMSIA model generated using steric, electrostatic, hydrophobic, donor and acceptor fields with q2 value of 0.592 and r2 value of 0.932 with six components was found to be the optimal model among the various models generated. According to contour map results, we have recommended the critical sites for modification in structure which will be useful in designing potent compounds with improved activity.

Prediction of octanol-air partition coefficients for polychlorinated biphenyls (PCBs) using 3D-QSAR models

Based on the experimental data of octanol-air partition coefficients (KOA) for 19 polychlorinated biphenyl (PCB) congeners, two types of QSAR methods, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), are used to establish 3D-QSAR models using the structural parameters as independent variables and using logKOA values as the dependent variable with the Sybyl software to predict the KOA values of the remaining 190 PCB congeners. The whole data set (19 compounds) was divided into a training set (15 compounds) for model generation and a test set (4 compounds) for model validation. As a result, the cross-validation correlation coefficient (q2) obtained by the CoMFA and CoMSIA models (shuffled 12 times) was in the range of 0.825–0.969 (>0.5), the correlation coefficient (r2) obtained was in the range of 0.957–1.000 (>0.9), and the SEP (standard error of prediction) of test set was within the range of 0.070–0.617, indicating that the models were robust and predictive. Randomly selected from a set of models, CoMFA analysis revealed that the corresponding percentages of the variance explained by steric and electrostatic fields were 23.9% and 76.1%, respectively, while CoMSIA analysis by steric, electrostatic and hydrophobic fields were 0.6%, 92.6%, and 6.8%, respectively. The electrostatic field was determined as a primary factor governing the logKOA. The correlation analysis of the relationship between the number of Cl atoms and the average logKOA values of PCBs indicated that logKOA values gradually increased as the number of Cl atoms increased. Simultaneously, related studies on PCB detection in the Arctic and Antarctic areas revealed that higher logKOA values indicate a stronger PCB migration ability. From CoMFA and CoMSIA contour maps, logKOA decreased when substituents possessed electropositive groups at the 2-, 3-, 3′-, 5- and 6- positions, which could reduce the PCB migration ability. These results are expected to be beneficial in predicting logKOA values of PCB homologues and derivatives and in providing a theoretical foundation for further elucidation of the global migration behaviour of PCBs.

Molecular modeling studies of [6,6,5] Tricyclic Fused Oxazolidinones as FXa inhibitors using 3D-QSAR, Topomer CoMFA, molecular docking and molecular dynamics simulations.

Coagulation factor Xa (Factor Xa, FXa) is a particularly promising target for novel anticoagulant therapy. The first oral factor Xa inhibitor has been approved in the EU and Canada in 2008. In this work, 38 [6,6,5] Tricyclic Fused Oxazolidinones were studied using a combination of molecular modeling techniques including three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, molecular dynamics and Topomer CoMFA (comparative molecular field analysis) were used to build 3D-QSAR models. The results show that the best CoMFA model has q(2)=0.511 and r(2)=0.984, the best CoMSIA (comparative molecular similarity indices analysis) model has q(2)=0.700 and r(2)=0.993 and the Topomer CoMFA analysis has q(2)=0.377 and r(2)=0.886. The results indicated the steric, hydrophobic, H-acceptor and electrostatic fields play key roles in models. Molecular docking and molecular dynamics explored the binding relationship of the ligand and the receptor protein.

Pharmacophore Based Comparative Molecular Field Analysis of CRTh2 Antagonists

Chemoattractant receptor homologous molecule expressed on Th2 cells (CRTh2) is a G-protein coupled receptor targeted for inflammatory diseases such as asthma, allergic rhinitis and atopic dermatitis. In this study, pharmacophore modeling and comparative molecular field analysis (CoMFA) were performed on the series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids derivatives. Five highly active compounds were used for generation of pharmacophore models using GASP module. The best pharmacophore model was selected and used as template for the alignment of compounds which was used for CoMFA analysis. The best predictions obtained for CoMFA was $q^2

CoMFA and CoMSIA analysis of ACE-inhibitory, antimicrobial and bitter-tasting peptides

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to the ACE-inhibitory, antimicrobial, and bitter-tasting peptides. Predictive 3D-QSAR models were established using SYBYL multifit molecular alignment rule over a training set and a test set. The optimum models were all statistically significant with cross-validated coefficients (Q2) >0.5 and conventional coefficients (R2) >0.9, indicating that they were reliable enough for activity prediction. The obtained results may aid in the design of novel bioactive peptides.

3D-QSAR and 3D-QSSR studies of thieno[2,3-d]pyrimidin-4-yl hydrazone analogues as CDK4 inhibitors by CoMFA analysis

To investigate the structural basis underlying potency and selectivity of a series of novel analogues of thieno[2,3-d]pyrimidin-4-yl hydrazones as cyclin-dependent kinase 4 (CDK4) inhibitors and to use this information for drug design strategies. Three-dimensional quantitative structure-activity relationship (3D-QSAR) and three-dimensional quantitative structure-selectivity relationship (3D-QSSR) models using comparative molecular field analysis (CoMFA) were conducted on a training set of 48 compounds. Partial least squares (PLS) analysis was employed. External validation was performed with a test set of 9 compounds. The obtained 3D-QSAR model (q(2)=0.724, r(2)=0.965, r(2)pred=0.945) and 3D-QSSR model (q(2)=0.742, r(2)=0.923, r(2)pred=0.863) were robust and predictive. Contour maps with good compatibility to active binding sites provided insight into the potentially important structural features required to enhance activity and selectivity. The contour maps indicated that bulky groups at R1 position could potentially enhance CDK4 inhibitory activity, whereas bulky groups at R3 position have the opposite effect. Appropriate incorporation of bulky electropositive groups at R4 position is favorable and could improve both potency and selectivity to CDK4. These two models provide useful information to guide drug design strategies aimed at obtaining potent and selective CDK4 inhibitors.

Brine shrimp lethality assay ‘an effective prescreen’: Microwave-assisted synthesis, BSL toxicity and 3DQSAR studies-based designing, docking and antitumor evaluation of potent chalcones

Context: In the course of searching potential antitumor agents from a library of chalcones synthesized under microwave irradiations, the brine shrimp lethality (BSL) assay and a 3D structure–activity relationship (3DQSAR) studies were followed by the antitumor evaluation of most potent analogues.Objective: The objective of the current study was to effectively use the BSL assay for the identification of potential cytotoxic analogues from a set of compounds.Methods: We applied the comparative molecular field analysis (CoMFA) and devised 3DQSAR on 33 synthesized chalcones leading to prediction of five related compounds with improved activity. The scope of BSL assay for the prediction of antitumor potency was tested through the in vitro antitumor studies against six human tumor cell-lines, docking studies and the tubulin-polymerization assay.Results: The newly designed compounds 34–38 displayed very promising cytotoxic potency. From our results, the BSL toxicity, antitumor efficacy and docking outcomes could be easily co-related.Conclusion: The study draws a very good relationship between a simple, inexpensive, and bench-top BSL assay and the antitumor potential of the cytotoxic compounds. Devising the CoMFA analysis helped in designing chalcones with improved cytotoxic potential as displayed through their BSL and cytotoxic activity against human tumor cell lines. The studies are noteworthy as such comprehensive studies were never performed before on the BSL assay. The present studies widen the scope of the BSL model that may prove quite helpful as a preliminary screen in the antitumor drug designing and synthesis expeditions.

Exploring structural requirements of aurone derivatives as antimalarials by validated DFT-based QSAR, HQSAR, and COMFA–COMSIA approach

Chloroquine resistance is nowadays a great problem in malaria. Aurone derivatives were effective against chloroquine resistant parasite. Validated density functional theory (DFT)-based chemometric modeling, hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity analysis (CoMSIA) studies were conducted on 35 aurone derivatives having antimalarial activity. 2D-QSAR models were developed on the training sets by Y-based ranking method. This model was validated on 50 pairs of the test and the training sets by k-Means cluster analysis method. HQSAR, CoMFA, and CoMSIA models were validated by standard techniques and each method validates the DFT-based 2D-QSAR study and in turn validates the earlier observed structural activity relationship data as well as each other. DFT-based 2D-QSAR model suggests that the increase of Mulliken charge at C14 and HOMO density located on C11 may be conducive to antimalarial activity. Ethyl group attached to C14 and the increase of the value of chemical potential may be beneficial for antimalarial activity. Methoxy fragment is important for better antimalarial activity by HQSAR study. CoMFA analysis shows a favorable steric green region is located near C14 whereas the unfavorable yellow region is far away from C14. A large blue region located near C14 indicates the positively charged groups are favorable at this position. CoMSIA steric features correlates well with the CoMFA steric features. CoMSIA study suggests the bulky hydrophobic substitution at C14 is necessary for antimalarial activity. These results may be utilized to obtain potential antimalarial molecules.

CoMFA and CoMSIA analysis of tetrahydroquinolines as potential antimalarial agents

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used on a dataset of compounds, some of them having been reported to inhibit Plasmodium falciparum protein, farnesyltransferase. The co-crystal structure of the lead molecule, BMS–214662 bound to Rat-PFT was used as a template. CoMFA yielded a good model, with = 0.909, = 0.617 and was validated using an external set ( = 0.748). It compared favourably with CoMSIA. In the CoMFA model the steric and electrostatic fields exerted an almost equal influence on activity. The contour maps indicated the necessity for sterically large electropositive groups with electronegative tail to be present in these molecules for activity, and sterically large electronegative moieties on the sulfonamide linker. By incorporating these features some new compounds have been identified for further investigation.

Various Partial Charge Schemes on 3D-QSAR Models for P-gp Inhibiting Adamantyl Derivatives

We developed three-dimensional quantitative structure activity relationship (3D-QASR) models for 17 adamantyl derivatives as P-glycoprotein (P-gp) inhibitors. Eighteen different partial charge calculation methods were tested to check the feasibility of the 3D-QSAR models. Best predictive comparative molecular field analysis (CoMFA) model was obtained with the Austin Model 1-Bond Charge Correction (AM1-BCC) atomic charge. The 3D-QSAR models were derived with CoMFA and comparative molecular similarity indices analysis (CoMSIA). The final CoMFA model (<TEX>$q^2$</TEX> = 0.764, <TEX>$r^2$</TEX> = 0.988) was calculated with an AM1-BCC charge and electrostatic parameter, whereas the CoMSIA model (<TEX>$q^2$</TEX> = 0.655, <TEX>$r^2$</TEX> = 0.964) was derived with an AM1-BCC charge and combined steric, electrostatic, hydrophobic and HB-acceptor parameters. Leave-five-out (LFO) cross-validation was also performed, which yielded good correlation coefficient for both CoMFA (0.801) and CoMSIA (0.656) models. Robustness of the developed models was checked further with 1000 run bootstrapping analyses, which gave an acceptable correlation coefficient for CoMFA (BS-<TEX>$r^2$</TEX> = 0.997, BS-SD = 0.003) and CoMSIA (BS-<TEX>$r^2$</TEX> = 0.996, BS-SD = 0.018).

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis – a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.

Rigorous Treatment of Multispecies Multimode Ligand−Receptor Interactions in 3D-QSAR: CoMFA Analysis of Thyroxine Analogs Binding to Transthyretin

For a rigorous analysis of the receptor-ligand binding, speciation of the ligands caused by ionization, tautomerism, covalent hydration, and dynamic stereoisomerism needs to be considered. Each species may bind in several orientations or conformations (modes), especially for flexible ligands and receptors. A thermodynamic description of the multispecies (MS), multimode (MM) binding events shows that the overall association constant is equal to the weighted sum of the sums of microscopic association constants of individual modes for each species, with the weights given by the unbound fractions of individual species. This expression is a prerequisite for a precise quantitative characterization of the ligand-receptor interactions in both structure-based and ligand-based structure-activity analyses. We have implemented the MS-MM correlation expression into the comparative molecular field analysis (CoMFA), which deduces a map of the binding site from structures and binding affinities of a ligand set, in the absence of experimental structural information on the receptor. The MS-MM CoMFA approach was applied to published data for binding to transthyretin of 28 thyroxine analogs, each forming up to four ionization species under physiological conditions. The published X-ray structures of several analogs, exhibiting multiple binding modes, served as templates for the MS-MM superposition of thyroxine analogs. Additional modes were generated for compounds with flexible alkyl substituents, to identify bound conformations. The results demonstrate that the MS-MM modification improved predictive abilities of the CoMFA models, even for the standard procedure with MS-MM selected species and modes. The predicted prevalences of individual modes and the generated receptor site model are in reasonable agreement with the available X-ray data. The calibrated model can help in the design of inhibitors of transthyretin amyloid fibril formation.

Comparative docking and CoMFA analysis of curcumine derivatives as HIV-1 integrase inhibitors

The docking studies and comparative molecular field analysis (CoMFA) were performed on highly active molecules of curcumine derivatives against 3' processing activity of HIV-1 integrase (IN) enzyme. The optimum CoMFA model was selected with statistically significant cross-validated r(2) value of 0.815 and non-cross validated r (2) value of 0.99. The common pharmacophore of highly active molecules was used for screening of HIV-1 IN inhibitors. The high contribution of polar interactions in pharmacophore mapping is well supported by docking and CoMFA results. The results of docking, CoMFA, and pharmacophore mapping give structural insights as well as important binding features of curcumine derivatives as HIV-1 IN inhibitors which can provide guidance for the rational design of novel HIV-1 IN inhibitors.