Docking-based Alignment Research Articles

Development of novel monoamine oxidase B (MAO-B) inhibitors by combined application of docking-based alignment, 3D-QSAR, ADMET prediction, molecular dynamics simulation, and MM_GBSA binding free energy

Unsaturated ketone derivatives are known as inhibitors of monoamine oxidase B (MAO-B), a potential drug target of Parkinson’s disease. Here, docking-based alignment, 3 D-QSAR (three-dimensional quantitative structure-activity relationship) studies, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, molecular dynamics (MD) simulation, and MM_GBSA binding free energy were performed on a novel series of MAO-B inhibitors. The objective is to predict new MAO-B inhibitors with high potency activity. The 3 D-QSAR models were created using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). Molecular docking findings indicated that compounds with strong inhibitory efficacy also had a high binding affinity. 3 D-QSAR studies showed the importance of steric, electrostatic, and H-bond acceptor fields on the inhibitory activity of MAO-B. Based on the appropriate 3 D-QSAR model, a new series of MAO-B inhibitors were predicted and their pharmacokinetic characteristics were evaluated using in silico ADMET prediction. All screened compounds show good oral bioavailability without any side effects. Moreover, the dynamic behavior and stability of the most active compounds were evaluated using MD simulations. The results showed that unsaturated ketone derivatives are stable and compact during the 100 ns of MD simulation. Finally, the binding free energy of complexes was determined using the MM_GBSA method; the findings indicated that the T1 compound is more stable (ΔGbinding = −409.506 KJ/mol) than the data set's highest active compound (ΔGbinding = −31.883 KJ/mol). Communicated by Ramaswamy H. Sarma

Improved 3D-QSAR prediction by multiple-conformational alignment: A case study on PTP1B inhibitors

Three-dimension quantitative structure activity relationship (3D-QSAR) was one of the major statistical techniques to investigate the correlation of biological activity with structural properties of candidate molecules, and the accuracy of statistic greatly depended on molecular alignment methodology. Exhaustive conformational search and successful conformational superposition could extremely improve the predictive accuracy of QSAR modeling. In this work, we proposed a solution to optimize QSAR prediction by multiple-conformational alignment methods, with a set of 40 flexible PTP1B inhibitors as case study. Three different molecular alignment methods were used for the development of 3D-QSAR models listed as following: (1) docking-based alignment (DBA); (2) pharmacophore-based alignment (PBA) and (3) co-crystallized conformer-based alignment (CCBA). Among these three alignments, it was indicated that the CCBA was the best and the fastest strategy in 3D-QSAR development, with the square correlation coefficient (r2) and cross-validated squared correlation coefficient (q2) of comparative molecular field analysis (CoMFA) were 0.992 and 0.694; the r2 and q2 of comparative molecular similarity indices analysis (CoMSIA) were 0.972 and 0.603, respectively. The alignment methodologies used here not only generated a robust QSAR model with useful molecular field contour maps for designing novel PTP1B inhibitors, but also provided a solution for constructing accurate 3D-QSAR model for various disease targets. Undoubtedly, such attempt in QSAR analysis would greatly help us to understand essential structural features of inhibitors required by its target, and so as to discover more promising chemical derivatives.

Structure-activity relationships studies on weakly basic N-arylsulfonylindoles with an antagonistic profile in the 5-HT6 receptor

We recently reported a series of 39 weakly basic N-arylsulfonylindoles as novel 5-HT6 antagonists. Eight of the compounds exhibited moderate to high binding affinities, with 2-(4-(2-Methoxyphenyl)piperazin-1-yl)-1-(1-tosyl-1H-indol-3-yl)ethanol 16 showing the highest binding affinity (pKi = 7.87). Given these encouraging results and as a continuation of our research, we performed an extensive step-by-step search for the best 3D-QSAR model that allows us to rationally propose novel molecules with improved 5-HT6 affinity based on our previously reported series. A comparative molecular similarity indices analysis (CoMSIA) model built on a docking-based alignment was developed, wherein steric, electrostatic, hydrophobic and hydrogen bond properties are correlated with biological activity. The model was validated internally and externally (q2 = 0.721; r2pred = 0.938), and identified the sulfonyl and hydroxyl groups and the piperazine ring among the main regions of the molecules that can be modified to create new 5-HT6 antagonists.

Flavonoids as CDK1 Inhibitors: Insights in Their Binding Orientations and Structure-Activity Relationship.

In the last years, the interactions of flavonoids with protein kinases (PKs) have been described by using crystallographic experiments. Interestingly, different orientations have been found for one flavonoid inside different PKs and different chemical substitutions lead to different orientations of the flavonoid scaffold inside one PK. Accordingly, orientation predictions of novel analogues could help to the design of flavonoids with high PK inhibitory activities. With this in mind, we studied the binding modes of 37 flavonoids (flavones and chalcones) inside the cyclin-dependent PK CDK1 using docking experiments. We found that the compounds under study adopted two different orientations into the active site of CDK1 (orientations I and II in the manuscript). In addition, quantitative structure–activity relationship (QSAR) models using CoMFA and CoMSIA methodologies were constructed to explain the trend of the CDK1 inhibitory activities for the studied flavonoids. Template-based and docking-based alignments were used. Models developed starting from docking-based alignment were applied for describing the whole dataset and compounds with orientation I. Adequate R2 and Q2 values were obtained by each method; interestingly, only hydrophobic and hydrogen bond donor fields describe the differential potency of the flavonoids as CDK1 inhibitors for both defined alignments and subsets. Our current application of docking and QSAR together reveals important elements to be drawn for the design of novel flavonoids with increased PK inhibitory activities.

Identification of the Structural Features of Guanine Derivatives as MGMT Inhibitors Using 3D-QSAR Modeling Combined with Molecular Docking.

DNA repair enzyme O6-methylguanine-DNA methyltransferase (MGMT), which plays an important role in inducing drug resistance against alkylating agents that modify the O6 position of guanine in DNA, is an attractive target for anti-tumor chemotherapy. A series of MGMT inhibitors have been synthesized over the past decades to improve the chemotherapeutic effects of O6-alkylating agents. In the present study, we performed a three-dimensional quantitative structure activity relationship (3D-QSAR) study on 97 guanine derivatives as MGMT inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Three different alignment methods (ligand-based, DFT optimization-based and docking-based alignment) were employed to develop reliable 3D-QSAR models. Statistical parameters derived from the models using the above three alignment methods showed that the ligand-based CoMFA (Qcv2 = 0.672 and Rncv2 = 0.997) and CoMSIA (Qcv2 = 0.703 and Rncv2 = 0.946) models were better than the other two alignment methods-based CoMFA and CoMSIA models. The two ligand-based models were further confirmed by an external test-set validation and a Y-randomization examination. The ligand-based CoMFA model (Qext2 = 0.691, Rpred2 = 0.738 and slope k = 0.91) was observed with acceptable external test-set validation values rather than the CoMSIA model (Qext2 = 0.307, Rpred2 = 0.4 and slope k = 0.719). Docking studies were carried out to predict the binding modes of the inhibitors with MGMT. The results indicated that the obtained binding interactions were consistent with the 3D contour maps. Overall, the combined results of the 3D-QSAR and the docking obtained in this study provide an insight into the understanding of the interactions between guanine derivatives and MGMT protein, which will assist in designing novel MGMT inhibitors with desired activity.

3D-QSAR and docking studies on piperidine-substituted diarylpyrimidine analogues as HIV-1 reverse transcriptase inhibitors

In this paper, 65 small molecules of HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) were aligned by two different alignment methods (substructure-based and docking-based alignment) for the construction of three-dimensional quantitative structure–activity relationship models. And the molecular descriptors were calculated by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. Statistical parameters derived from the models using the above two different alignment methods showed that the docking-based CoMFA and CoMSIA models were better than the substructure-based models with both higher internal consistencies (q 2 of 0.727 and 0.698, respectively) and external predictive powers ( $$r_{\text{pred}}^{2}$$ of 0.906 and 0.940, respectively). According to the generated contour maps, several key structural features required for increasing the biological activity of compounds were achieved. Additionally, a docking study was also performed to explore the binding mechanisms of this series of compounds, and the obtained binding interactions are in agreement with the results of the contour maps. These structural-based and ligand-based computational studies can offer useful references for the further rational design of HIV-1 NNRTIs.

Stepwise development of structure–activity relationship of diverse PARP-1 inhibitors through comparative and validated in silico modeling techniques and molecular dynamics simulation

Inhibitors of poly (ADP-ribose) polymerase-1 (PARP-1) enzyme are useful for the treatment of various diseases including cancer. Comparative in silico studies were performed on different ligand-based (2D-QSAR, Kernel-based partial least square (KPLS) analysis, Pharmacophore Search Engine (PHASE) pharmacophore mapping), and structure-based (molecular docking, MM-GBSA analyses, Gaussian-based 3D-QSAR analyses on docked poses) modeling techniques to explore the structure–activity relationship of a diverse set of PARP-1 inhibitors. Two-dimensional (2D)-QSAR highlighted the importance of charge topological index (JGI7), fractional polar surface area (JursFPSA3), and connectivity index (CIC2) along with different molecular fragments. Favorable and unfavorable fingerprints were demonstrated in KPLS analysis, whereas important pharmacophore features (one acceptor, one donor, and two ring aromatic) along with favorable and unfavorable field effects were demonstrated in PHASE-based pharmacophore model. MM-GBSA analyses revealed significance of different polar, non-polar, and solvation energies. Docking-based alignment of ligands was used to perform Gaussian-based 3D-QSAR study that further demonstrated importance of different field effects. Overall, it was found that polar interactions (hydrogen bonding, bridged hydrogen bonding, and pi–cation) play major roles for higher activity. Steric groups increase the total contact surface area but it should have higher fractional polar surface area to adjust solvation energy. Structure-based pharmacophore mapping spotted the positive ionizable feature of ligands as the most important feature for discriminating highly active compounds from inactives. Molecular dynamics simulation, conducted on highly active ligands, described the dynamic behaviors of the protein complexes and supported the interpretations obtained from other modeling analyses. The current study may be useful for designing PARP-1 inhibitors.

Molecular docking- and genetic algorithm-based approaches to produce robust 3D-QSAR models

Conformational alignment is a crucial step in 3D-Quantitative structure–activity relationship (QSAR) modeling, e.g., Comparative Molecular Field Alignment (CoMFA). Different alignment methods including alignment on an appropriate template conformation, field fit alignment, database alignment, pharmacophore-based alignment, atom fit, and docking-based alignment are preferred choices for 3D-QSAR modeling. Our experience suggested that high quality 3D-QSARs are not guaranteed if docking/scoring or combination of both used as a pre-alignment criteria. Therefore, there is a need to investigate whether the widely used docking methods and scoring functions are the best choices for envisaging the alignment hypothesis. In the current study, a comparative evaluation of docking algorithms and scoring functions was performed and evaluated. For the purpose of evaluation a subset of widely used docking algorithms were used for docking based alignment in CoMFA. It was concluded that no docking or scoring method was able to produce the robust QSAR model (reflected by low q2 values) at least for the case of the dataset used in current study. In order to improve the relatively poor q2 values, we performed an evolutionary method and the q2 value was optimized using genetic algorithm based conformational selection approach. A dataset of Glycogen Phosphorylaseb (E.C.2.4.1.1) inhibitors was used to apply the protocol.

Docking-based 3D-QSAR modeling of the inhibitors of IMP metallo-β-lactamase

IMP metallo-β-lactamases (MBLs) confer broad-spectrum resistance to β-lactam antibiotics such as imipenem and escape the action of almost all clinically used β-lactamase inhibitors. We conducted three-dimensional quantitative structure–activity relationships (3D-QSAR) for a series of IMP-1 MBL inhibitors with the aid of docking-based alignment. While the 3D-QSAR models were created based on a training set of 41 compounds, their external predictivity was validated using a test set of eight compounds. The study has resulted in two types of satisfactory 3D-QSAR models for predicting the biological activity of new compounds: CoMFA model (r2 = 0.989; \( r_{\text{pre}}^{ 2} = 0. 8 4 3 \)) and CoMSIA model (r2 = 0.968; \( r_{\text{pre}}^{ 2} = 0. 9 5 7 \)). Our work will facilitate the design and optimization of new potential inhibitors.

Homology model-guided 3D-QSAR studies of HIV-1 integrase inhibitors.

In the present study, we report the exploration of binding modes of potent HIV-1 integrase (IN) inhibitors MK-0518 (raltegravir) and GS-9137 (elvitegravir) as well as chalcone and related amide IN inhibitors we recently synthesized and the development of 3D-QSAR models for integrase inhibition. Homology models of DNA-bound HIV-1 IN were constructed on the basis of the X-ray crystal structure of the foamy virus IN-DNA complex (PDB ID: 3L2T ) and used for docking. The binding modes of raltegravir and elvitegravir in our homology models are in accordance with those in the foamy virus structure revealing interactions important for inhibitor-IN binding. To gain further insights into the structural requirements for IN inhibition, three-dimensional quantitative structure activity relationship (3D-QSAR) studies were conducted using raltegravir, elvitegravir, and their analogs; our synthesized 3-keto salicylic acid IN inhibitor series; as well as other structurally related HIV-1 IN inhibitors. In the first part of the study with 103 compounds, atom-fit alignments, I and II, and docking-based alignment, III, were used to develop 3D-QSAR models 1, 2, and 3, respectively, each comprising comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSARs. This initial analysis indicated that the docking-based (structure-based) model 3 performed better than the atom-fit (ligand-based) models 1 and 2, in terms of statistical significance and robustness. Thus, the docking-based alignment was then subsequently used with an expanded data set of 296 compounds for building a more comprehensive 3D-QSAR, model 4. Model 4 afforded good q² values of 0.70 and 0.75 for CoMFA and CoMSIA 3D-QSARs, respectively, and showed good predictive performance on an external validation test set of 59 compounds with predictive r² values up to 0.71. The HIV IN-DNA homology model of biological relevance and the comprehensive 3D-QSAR models developed in the present study provide insights and new predictive tools for structure-based design and optimization of IN inhibitors.

Multitemplate Alignment Method for the Development of a Reliable 3D-QSAR Model for the Analysis of MMP3 Inhibitors

A ligand-based 3D-QSAR study for the identification of MMP3 inhibitors was developed by applying an innovative alignment method capable of taking into account information obtained from available X-ray MMP3 structures. Comparison of the obtained model with data recently published using a docking-based alignment method indicated that the ligand-based 3D-QSAR model provided better predictive ability. A second external test set of 106 MMP3 inhibitors further confirmed the predictive ability of the 3D-QSAR model. Finally, certain iminodiacetyl-based hydroxamate-benzenesulfonamide conjugates, which were predicted to be active by the 3D-QSAR model, were tested in vitro for MMP3 inhibition; some provided low nanomolar activity. As such, our results suggest that the multitemplate alignment method is capable of improving the quality of 3D-QSAR models and therefore could be applied to the study of other systems. Furthermore, since MMP3 is an important target toward the treatment of arthritis, this model could be applied to the design of new active MMP3 inhibitors.

Homology modeling of MCH1 receptor and validation by docking/scoring and protein-aligned CoMFA

Homology modeling is becoming a valid method for obtaining three-dimensional coordinates for proteins. However, it is hard to judge the qualities of the resulting models warranting robust subsequent validations. In an attempt to evaluate the quality of Melanin-concentrating hormone 1 receptor (MCH1R) homology models, a number of homology structures were scanned for potential binding cavities. Subsequently, a group of 35 benzylpiperidines' MCH1R inhibitors were docked into each of the proposed binding sites via four different scoring functions. The docked structures were utilized to construct corresponding protein-aligned comparative molecular field analysis (CoMFA) models by employing probe-based (H +, OH, CH 3) energy grids and genetic partial least squares (G/PLS) statistical analysis. The docking-based alignment succeeded in accessing self-consistent CoMFA models upon employing JAIN scoring function in one of the proposed binding pockets in a particular homology model. Furthermore, a ligand-based pharmacophore model was developed for the same set of inhibitors and was found to agree with the successful docking configuration. Therefore, we proved that the overall procedure of docking, scoring, and CoMFA evaluation can be a useful tool to validate homology models, which can be of value for structure-based design, in-silico screening, and in understanding the structural basis of ligand binding to MCH1R.

3D-Quantitative Structure Activity Relationships of Biphenyl Carboxylic Acid MMP-3 Inhibitors: Exploring Automated Docking as Alignment Method

A series of CoMFA models have been derived from docking-based and atom-based alignments. The statistics of these approaches has been compared to determine whether a docking approach can be employed as an automated alignment tool for the development of 3D-QSAR models. Using a well-characterized training set of 51 biphenyl carboxylic acid MMP-3 inhibitors, the docking-based alignment provided by a DOCK4/PMF-scoring protocol has yielded statistically significant, cross-validated CoMFA models comparable to those derived with a traditional atom-based alignment technique. Field fit minimization has been applied to refine the atom-based and docking-based alignments. The refinement appears to be beneficial for the docking-based approach. For the atom-based alignment, however, field-fit refinement has not resulted in improved CoMFA models. The statistically best CoMFA model has been created by the atom-based alignment that has been found, however, to be inconsistent with the stromelysin crystal structure. The docking alignment refined by field-fit alignment has resulted in a final alignment that is consistent with the crystal structure and only slightly statistically inferior to the atom-based aligned CoMFA model. The results show␣the ability of an automated docking/field-fit alignment technique to provide self-consistent CoMFA alignments.