Construction of 4D-QSAR models for use in the design of novel p38-MAPK inhibitors

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

4D- quantitative structure-activity relationship modeling: making a comeback

Introduction: Predictive Quantitative Structure-Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.

Molecular Modelling of Potential Candidates for the Treatment of Depression

A lot of research initiatives in the last decades have been focused on the search of new strategies to treat depression. However, despite the availability of various antidepressants, current treatment is still far from ideal. Unwanted side effects, modest response rates and the slow onset of action are the main shortcomings. As a strategy to improve symptomatic relief and response rates, the dual modulation of the serotonin transporter and the histamine H₃ receptor by a single chemical entity has been proposed in the literature. Accordingly, this work aims to elucidate key structural features responsible for the dual inhibitory activity of the hexahydro-pyrrolo-isoquinoline derivatives. For this purpose, two approaches were employed, four-dimensional quantitative structure-activity relationship (4D-QSAR) and molecular docking. The 4D-QSAR models for both receptors allowed the identification of the pharmacophore groups critical for the modelled biological activity, whereas the binding mode of this class of compounds to the human serotonin transporter was assessed by molecular docking. The findings can be applicable to design new antidepressants.

xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles

A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.

A receptor dependent-4D QSAR approach to predict the activity of mutated enzymes

Screening and selection tools to obtain focused libraries play a key role in successfully engineering enzymes of desired qualities. The quality of screening depends on efficient assays; however, a focused library generated with a priori information plays a major role in effectively identifying the right enzyme. As a proof of concept, for the first time, receptor dependent - 4D Quantitative Structure Activity Relationship (RD-4D-QSAR) has been implemented to predict kinetic properties of an enzyme. The novelty of this study is that the mutated enzymes also form a part of the training data set. The mutations were modeled in a serine protease and molecular dynamics simulations were conducted to derive enzyme-substrate (E-S) conformations. The E-S conformations were enclosed in a high resolution grid consisting of 156,250 grid points that stores interaction energies to generate QSAR models to predict the enzyme activity. The QSAR predictions showed similar results as reported in the kinetic studies with >80% specificity and >50% sensitivity revealing that the top ranked models unambiguously differentiated enzymes with high and low activity. The interaction energy descriptors of the best QSAR model were used to identify residues responsible for enzymatic activity and substrate specificity.

Constructing and Validating 3D-pharmacophore Models to a Set of MMP-9 Inhibitors for Designing Novel Anti-melanoma Agents

A receptor-independent (RI) four-dimensional structure-activity relationship (4D-QSAR) formalism was applied to a set of sixty-four β-N-biaryl ether sulfonamide hydroxamate derivatives, previously reported as potent inhibitors against matrix metalloproteinase subtype 9 (MMP-9). MMP-9 belongs to a group of enzymes related to the cleavage of several extracellular matrix components and has been associated to cancer invasiveness/metastasis. The best RI 4D-QSAR model was statistically significant (N=47; r(2) =0.91; q(2) =0.83; LSE=0.09; LOF=0.35; outliers=0). Leave-N-out (LNO) and y-randomization approaches indicated the QSAR model was robust and presented no chance correlation, respectively. Furthermore, it also had good external predictability (82 %) regarding the test set (N=17). In addition, the grid cell occupancy descriptors (GCOD) of the predicted bioactive conformation for the most potent inhibitor were successfully interpreted when docked into the MMP-9 active site. The 3D-pharmacophore findings were used to predict novel ligands and exploit the MMP-9 calculated binding affinity through molecular docking procedure.

Antiprotozoal Nitazoxanide Derivatives: Synthesis, Bioassays and QSAR Study Combined with Docking for Mechanistic Insight

In view of the serious health problems concerning infectious diseases in heavily populated areas, we followed the strategy of lead compound diversification to evaluate the near-by chemical space for new organic compounds. To this end, twenty derivatives of nitazoxanide (NTZ) were synthesized and tested for activity against Entamoeba histolytica parasites. To ensure drug-likeliness and activity relatedness of the new compounds, the synthetic work was assisted by a quantitative structure-activity relationships study (QSAR). Many of the inherent downsides – well-known to QSAR practitioners – we circumvented thanks to workarounds which we proposed in prior QSAR publication. To gain further mechanistic insight on a molecular level, ligand-enzyme docking simulations were carried out since NTZ is known to inhibit the protozoal pyruvate ferredoxin oxidoreductase (PFOR) enzyme as its biomolecular target.

Quantitative Structure-Activity Relationship Studies for Potential Rho-Associated Protein Kinase Inhibitors

A series of pyridylthiazole derivatives developed by Lawrence et al. as Rho-associated protein kinase inhibitors were subjected to four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis. The models were generated applying genetic algorithm (GA) optimization combined with partial least squares (PLS) regression. The best model presented validation values ofr2=0.773,qCV2=0.672,rpred2=0.503,Δrm2=0.197,rm test2⁡⁡=0.520,rY-rand2=0.19, andRp2=0.590. Furthermore, analyzing the descriptors it was possible to propose new compounds that predicted higher inhibitory concentration values than the most active compound of the series.

Multidimensional (3D/4D-QSAR) probability-guided pharmacophore mapping: investigation of activity profile for a series of drug absorption promoters

A hybrid approach that combines 3D and 4D-QSAR methods based on grid and neural paradigms with automated IVE-PLS procedure was examined to identify the pharmacophore pattern for cholic acid derivatives as potential drug absorption promoters.

Insight into the Structural Determinants of Imidazole Scaffold-Based Derivatives as TNF-α Release Inhibitors by in Silico Explorations

Presently, 151 widely-diverse pyridinylimidazole-based compounds that show inhibitory activities at the TNF-α release were investigated. By using the distance comparison technique (DISCOtech), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) methods, the pharmacophore models and the three-dimensional quantitative structure-activity relationships (3D-QSAR) of the compounds were explored. The proposed pharmacophore model, including two hydrophobic sites, two aromatic centers, two H-bond donor atoms, two H-bond acceptor atoms, and two H-bond donor sites characterizes the necessary structural features of TNF-α release inhibitors. Both the resultant CoMFA and CoMSIA models exhibited satisfactory predictability (with Q² (cross-validated correlation coefficient) = 0.557, R²_ncv (non-cross-validated correlation coefficient) = 0.740, R²_pre (predicted correlation coefficient) = 0.749 and Q² = 0.598, R²_ncv = 0.767, R²_pre = 0.860, respectively). Good consistency was observed between the 3D-QSAR models and the pharmacophore model that the hydrophobic interaction and hydrogen bonds play crucial roles in the mechanism of actions. The corresponding contour maps generated by these models provide more diverse information about the key intermolecular interactions of inhibitors with the surrounding environment. All these models have extended the understanding of imidazole-based compounds in the structure-activity relationship, and are useful for rational design and screening of novel 2-thioimidazole-based TNF-α release inhibitors.

Application of 4D-QSAR studies to a series of benzothiophene analogs

Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis was applied to a series of 52 benzothiophene analogs synthesized by Hiroshi Yamashita et al. (2011, United Sates Patent no. US8,349,840) and evaluated as dopamine D2 receptor inhibitors. The QSAR equations, generated by a combined scheme of genetic algorithms (GA) and partial least squares (PLS) regression, were evaluated by leave-one-out cross-validation, using a training and test set of 42 and ten compounds, respectively. Four different alignments were tested, and model 2, generated from Eq. 10, showed the best statistical results; it was therefore chosen to represent the data set. This study allowed a quantitative prediction of compounds potency and supported the design of the new benzothiophene.

Application of 4D-QSAR studies to a series of raloxifene analogs and design of potential selective estrogen receptor modulators

Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis was applied on a series of 54 2-arylbenzothiophene derivatives, synthesized by Grese and coworkers, based on raloxifene (an estrogen receptor-alpha antagonist), and evaluated as ERa ligands and as inhibitors of estrogen-stimulated proliferation of MCF-7 breast cancer cells. The conformations of each analogue, sampled from a molecular dynamics simulation, were placed in a grid cell lattice according to three trial alignments, considering two grid cell sizes (1.0 and 2.0 Å). The QSAR equations, generated by a combined scheme of genetic algorithms (GA) and partial least squares (PLS) regression, were evaluated by "leave-one-out" cross-validation, using a training set of 41 compounds. External validation was performed using a test set of 13 compounds. The obtained 4D-QSAR models are in agreement with the proposed mechanism of action for raloxifene. This study allowed a quantitative prediction of compounds' potency and supported the design of new raloxifene analogs.

Receptor-dependent 4D-QSAR analysis of peptidemimetic inhibitors of Trypanosoma cruzi trypanothione reductase with receptor-based alignment

Receptor-dependent four-dimensional quantitative structure-activity relationship (RD-4D-QSAR) studies were applied on a series of 21 peptides reversible inhibitors of Trypanosoma cruzi trypanothione reductase (TR) (Amino Acids, 20, 2001, 145). The RD-4D-QSAR (J Chem Inform Comp Sci, 43, 2003, 1591) approach can evaluate multiple conformations from molecular dynamics simulation and several superposition structure alignments inside a box composed by unitary cubic cells. The descriptors are the occupancy frequency of the atoms types inside the grid cells. We could develop 3D-QSAR models that were highly predictive (q(2) above 0.71). The 3D-QSAR models can be visualized as a spatial map of atom types that are important on the comprehension of the ligand-enzyme interaction mechanism, pointing main pharmacophoric groups and TR subsites described in the literature. We were able also to identify some TR subsites for further development in the drug discovery process against tropical diseases not yet studied.

Molecular dynamics simulation and free energy calculation studies of the binding mechanism of allosteric inhibitors with p38α MAP kinase

p38 MAP kinase is a promising target for anti-inflammatory treatment. The classical kinase inhibitors imatinib and sorafenib as well as BI-1 and BIRB-796 were reported to bind in the DFG-out form of human p38α, known as type II or allosteric kinase inhibitors. Although DFG-out conformation has attracted great interest in the design of type II kinase inhibitors, the structural requirements for binding and mechanism of stabilization of DFG-out conformation remain unclear. As allosteric inhibition is important to the selectivity of kinase inhibitor, herein the binding modes of imatinib, sorafenib, BI-1 and BIRB-796 to p38α were investigated by molecular dynamics simulation. Binding free energies were calculated by molecular mechanics/Poisson-Boltzmann surface area method. The predicted binding affinities can give a good explanation of the activity difference of the studied inhibitors. Furthermore, binding free energies decomposition analysis and further structural analysis indicate that the dominating effect of van der Waals interaction drives the binding process, and key residues, such as Lys53, Gly71, Leu75, Ile84, Thr106, Met109, Leu167, Asp168, and Phe169, play important roles by forming hydrogen bond, salt bridge, and hydrophobic interactions with the DFG-out conformation of p38α. Finally, we also conducted a detailed analysis of BI-1, imatinib, and sorafenib binding to p38α in comparison with BIRB-796 exploited for gaining potency as well as selectivity of p38 inhibitors. These results are expected to be useful for future rational design of novel type II p38 inhibitors.

Recent advances in fragment-based QSAR and multi-dimensional QSAR methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

4D-QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. The quantitative structure–activity relationship (QSAR) formalisms are among the most important strategies that can be applied for the successful design new molecules. This review provides a comprehensive review on the evolution and current status of 4D-QSAR, highlighting present challenges and new opportunities in drug design.

3D-Pharmacophore mapping of thymidine-based inhibitors of TMPK as potential antituberculosis agents

Tuberculosis (TB) is the primary cause of mortality among infectious diseases. Mycobacterium tuberculosis monophosphate kinase (TMPKmt) is essential to DNA replication. Thus, this enzyme represents a promising target for developing new drugs against TB. In the present study, the receptor-independent, RI, 4D-QSAR method has been used to develop QSAR models and corresponding 3D-pharmacophores for a set of 81 thymidine analogues, and two corresponding subsets, reported as inhibitors of TMPKmt. The resulting optimized models are not only statistically significant with r(2) ranging from 0.83 to 0.92 and q(2) from 0.78 to 0.88, but also are robustly predictive based on test set predictions. The most and the least potent inhibitors in their respective postulated active conformations, derived from each of the models, were docked in the active site of the TMPKmt crystal structure. There is a solid consistency between the 3D-pharmacophore sites defined by the QSAR models and interactions with binding site residues. Moreover, the QSAR models provide insights regarding a probable mechanism of action of the analogues.

Free-energy force-field three-dimensional quantitative structure–activity relationship analysis of a set of p38-mitogen activated protein kinase inhibitors

The p38-mitogen-activated protein kinases (p38-MAPKs) belong to a family of serine-threonine kinases activated by pro-inflammatory or stressful stimuli that are known to be involved in several diseases. Their biological importance, related to the release of inflammatory pro-cytokines such as tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 (IL-1), has generated many studies aiming at the development of selective inhibitors for the treatment of inflammatory diseases. In this work, we developed receptor-based three dimensional (3D) quantitative structure-activity relationship (QSAR) models for a series of 33 pyridinyl imidazole compounds [Liverton et al. (1999) 42:2180], using a methodology named free-energy force-field (FEFF) [Tokarski and Hopfinger (1997) 37:792], in which scaled intra- and intermolecular energy terms of the Assisted Model Building Energy Refinement (AMBER) force field combined with a hydration-shell solvation model are the independent variables used in the QSAR studies. Multiple temperature molecular-dynamics simulations (MDS) of ligand-protein complexes and genetic-function approximation (GFA) were employed using partial least squares (PLS) as the fitting functions to develop FEFF-3D-QSAR models for the binding process. The best model obtained in the FEFF-3D-QSAR receptor-dependent (RD) method shows the importance of the van der Waals energy change upon binding and the electrostatic energy in the interaction of ligands with the receptor. The QSAR equations described here show good predictability and may be regarded as representatives of the binding process of ligands to p38-MAPK. Additionally, we have compared the top FEFF-3D-QSAR model with receptor independent (RI) 4D-QSAR models developed in a recent study [Romeiro et al. (2005) 19:385].