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.

AutoWeka: toward an automated data mining software for QSAR and QSPR studies

In biology and chemistry, a key goal is to discover novel compounds affording potent biological activity or chemical properties. This could be achieved through a chemical intuition-driven trial-and-error process or via data-driven predictive modeling. The latter is based on the concept of quantitative structure-activity/property relationship (QSAR/QSPR) when applied in modeling the biological activity and chemical properties, respectively, of compounds. Data mining is a powerful technology underlying QSAR/QSPR as it harnesses knowledge from large volumes of high-dimensional data via multivariate analysis. Although extremely useful, the technicalities of data mining may overwhelm potential users, especially those in the life sciences. Herein, we aim to lower the barriers to access and utilization of data mining software for QSAR/QSPR studies. AutoWeka is an automated data mining software tool that is powered by the widely used machine learning package Weka. The software provides a user-friendly graphical interface along with an automated parameter search capability. It employs two robust and popular machine learning methods: artificial neural networks and support vector machines. This chapter describes the practical usage of AutoWeka and relevant tools in the development of predictive QSAR/QSPR models.

AVAILABILITY

The software is freely available at http://www.mt.mahidol.ac.th/autoweka.

4D-LQTA-QSAR and docking study on potent Gram-negative specific LpxC inhibitors: a comparison to CoMFA modeling

A quasi 4D-QSAR has been carried out on a series of potent Gram-negative LpxC inhibitors. This approach makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. This new methodology is based on the generation of a conformational ensemble profile, CEP, for each compound instead of only one conformation, followed by the calculation intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are independent variables employed in a QSAR analysis. The comparison of the proposed methodology to comparative molecular field analysis (CoMFA) formalism was performed. This methodology explores jointly the main features of CoMFA and 4D-QSAR models. Step-wise multiple linear regression was used for the selection of the most informative variables. After variable selection, multiple linear regression (MLR) and partial least squares (PLS) methods used for building the regression models. Leave-N-out cross-validation (LNO), and Y-randomization were performed in order to confirm the robustness of the model in addition to analysis of the independent test set. Best models provided the following statistics: [Formula in text] (PLS) and [Formula in text] (MLR). Docking study was applied to investigate the major interactions in protein-ligand complex with CDOCKER algorithm. Visualization of the descriptors of the best model helps us to interpret the model from the chemical point of view, supporting the applicability of this new approach in rational drug design.

QSAR models for CXCR2 receptor antagonists based on the genetic algorithm for data preprocessing prior to application of the PLS linear regression method and design of the new compounds using in silico virtual screening

The CXCR2 receptors play a pivotal role in inflammatory disorders and CXCR2 receptor antagonists can in principle be used in the treatment of inflammatory and related diseases. In this study, quantitative relationships between the structures of 130 antagonists of the CXCR2 receptors and their activities were investigated by the partial least squares (PLS) method. The genetic algorithm (GA) has been proposed for improvement of the performance of the PLS modeling by choosing the most relevant descriptors. The results of the factor analysis show that eight latent variables are able to describe about 86.77% of the variance in the experimental activity of the molecules in the training set. Power prediction of the QSAR models developed with SMLR, PLS and GA-PLS methods were evaluated using cross-validation, and validation through an external prediction set. The results showed satisfactory goodness-of-fit, robustness and perfect external predictive performance. A comparison between the different developed methods indicates that GA-PLS can be chosen as supreme model due to its better prediction ability than the other two methods. The applicability domain was used to define the area of reliable predictions. Furthermore, the in silico screening technique was applied to the proposed QSAR model and the structure and potency of new compounds were predicted. The developed models were found to be useful for the estimation of pIC₅₀ of CXCR2 receptors for which no experimental data is available.

Antibody recognition and conformational flexibility of a plaque-specific beta-amyloid epitope modulated by non-native peptide flanking regions

Here we report on the synthesis, antibody binding, and QSAR studies of a series of linear and cyclic peptides containing a beta-amyloid plaque-specific epitope (Abeta(4-10); FRHDSGY). In these constructs, two or three alpha- l-Ala, alpha- d-Ala, or beta-Ala residues were introduced at both N- and C-termini of the epitope as non-native flanking sequences. Cyclization of the linear Abeta(4-10) epitope peptide resulted in reduced antibody binding. However, the antibody binding could be fully compensated by insertion of alanine flanks into the corresponding cyclic peptides. These results indicate that the modification of a beta-amyloid plaque-specific epitope by combination of cyclization and flanking sequences could generate highly antigenic peptides compared to the native sequence. A novel 3D QSAR method, which explicitly handles conformational flexibility, was developed for the case of such molecular libraries. This method led to the prediction of the binding conformation for the common FRHDSGY sequence.

Novel semi-automated methodology for developing highly predictive QSAR models: application for development of QSAR models for insect repellent amides

Conventional 3D-QSAR models are built using global minimum conformations or quantum-mechanics based geometry-optimized conformations as bioactive conformers. QSAR models developed using the global minima as bioactive conformers, employing the GFA, PLS and G/PLS methodologies, gave good non-validated r(2) (0.898, 0.868 and 0.922) and performed well on an internal validation test with leave-one-out correlation q(2) (LOO) (0.902, 0.726 and 0.924), leave-10%-out correlation q(2) (L10O) (0.874, 0.728 and 0.883) and leave-20%-out q(2) (L20O) (0.811, 0.716 and 0.907). However, they showed poor predictive ability on an external data set with best predictive r(2) (Pred-r(2)) of 0.349, 0.139 and 0.204 respectively. A novel methodology to mine bioactive conformers, from clusters of conformations with good 3D-spatial representation around pharmacophoric moiety, furnishes highly predictive 3D-QSAR models. The best QSAR model (model A) showed r(2) of 0.989, q(2) (LOO) of 0.989, q(2) (L10O) of 0.980, q(2) (L20O) of 0.963 and Pred-r(2) on eight test compounds of 0.845. The methodology is based on mimicking the multi-way Partial Least Squares (PLS) technique by performing several automated sequential PLS analyses. The poses/shapes of the mined bioactive conformers provide valuable insight into the mechanism of action of the insect repellents. All of the repetitive tasks were automated using Tcl-based Cerius2 scripts.