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

Design of 5’,7’-Dihydroxyflavones and β-D-Glucopyranosyl Heterocyclic Derivatives as Glycogen Phosphorylase Inhibitors

There has been a significant increase in the number of people diagnosed with diabetes from 1980 to 2016, rising from 108 million to approximately 420 million people worldwide, and is predicted to rise to above 640 million by the year 2040 and type II diabetes is seen in 90-95% of those diagnosed. Many treatments currently exist to treat type II diabetes, although there are considerable adverse health effects associated with these drugs including a risk of hypoglycaemia. Accordingly, there is a swift need for a new, effective treatment that has little to no side effect for those suffering from T2D. β-D-Glucopyranosyl derivatives are known to inhibit glycogen phosphorylase, which is a valid target for controlling hyperglycaemia in type 2 diabetes. Computational methods, such as molecular docking with Glide and GOLD as well as post-docking free energy calculations using MM-GBSA calculations were used to screen a library of β-D-glucopyranosyl analogues and we, for the first time, have derived computational models of MMGBSA for the GPb catalytic site have revealed excellent predictive potential based on a thorough statistical analysis. Using these models, correlations between predicted and experimental inhibitory potential as high as 0.95 – 0.97 were obtained for a training set of ligands. These methods have substantial potential for discovery of new effective compounds in the treatment of T2D as thousands of potential ligands could in the future be screened. Previous computational screening of 5,7-dihydroxyflavone analogues which have been predicted to bind at the caffeine binding site have been performed and a number of these analogues have been synthesized and will undergo kinetic experiments which will give insight into the effectiveness of 5’,7’-dihydroxyflavone derivatives so that a wider library of similar compounds could be tested that displays biological activity towards glycogen phosphorylase

The effect of Retinol Binding Protein on the Proteome of muscle cells.

Vitamin A is essential for normal embryonic development and vision. Retinol binding protein (RBP) and its receptor, STRA6, are vital for the maintenance of intracellular stores of vitamin A. Recently, elevated serum RBP concentration has been implicated as a contributing factor to the development of insulin resistance and type II diabetes. However, conflicting opinions exist as to how increased RBP levels can cause insulin resistance. Some suggest it is as a result of the activation of macrophages in adipose tissue and the secretion of cytokines. Others suggest it is as a result of RBP induced STRA6 phosphorylation, and the activation of the JAK/STAT signalling pathway. Regardless of the mechanism, reducing circulating levels of RBP may be a novel strategy for the treatment of type II diabetes. Several small molecules have been designed to promote renal clearance of RBP, thus lowering serum levels. In order to consolidate the theories surrounding RBP induced insulin resistance, a proteomic study was devised to determine the direct effect of RBP on muscle cells, since the muscle is the main target of insulin induced glucose uptake. Results suggest that RBP may be affecting the enzymes involved in glucose storage and glycogen catabolism. Artificial methods aimed at reducing serum RBP levels may act by preventing RBP induced glycogen disruption. In a related study, it was noted that small molecules aimed at reducing circulating RBP levels had a direct effect on muscle cells to stimulate glucose uptake. This phenomenon occurred independently of the predicted mechanism of action. A second proteomic study was conducted to determine the direct mechanism of action of the compounds in muscle cells. The molecules appear to stimulate the influx of glucose by reducing the ATP yield from oxidative phosphorylation and enhancing the utilisation of alternate energy stores. The C-terminal region of STRA6 appears to be a large SH2 motif-containing intracellular segment which may be capable of forming an independently folding domain. As such it may represent the site of interaction with other proteins in the system. Therefore, it was cloned, expressed and characterised. The secondary structure of the domain was shown to be largely α-helical and a model was constructed. Possible functional roles for this region were investigated

Fragment-based 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. Quantitative structure–activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design

A robust machine learning approach for the prediction of allosteric binding sites

Previously held under moratorium from 28 March 2017 until 28 March 2022Allosteric regulatory sites are highly prized targets in drug discovery. They remain difficult to detect by conventional methods, with the vast majority of known examples being found serendipitously. Herein, a rigorous, wholly-computational protocol is presented for the prediction of allosteric sites. Previous attempts to predict the location of allosteric sites by computational means drew on only a small amount of data. Moreover, no attempt was made to modify the initial crystal structure beyond the in silico deletion of the allosteric ligand. This behaviour can leave behind a conformation with a significant structural deformation, often betraying the location of the allosteric binding site. Despite this artificial advantage, modest success rates are observed at best. This work addresses both of these issues. A set of 60 protein crystal structures with known allosteric modulators was collected. To remove the imprint on protein structure caused by the presence of bound modulators, molecular dynamics was performed on each protein prior to analysis. A wide variety of analytical techniques were then employed to extract meaningful data from the trajectories. Upon fusing them into a single, coherent dataset, random forest - a machine learning algorithm - was applied to train a high performance classification model. After successive rounds of optimisation, the final model presented in this work correctly identified the allosteric site for 72% of the proteins tested. This is not only an improvement over alternative strategies in the literature; crucially, this method is unique among site prediction tools in that is does not abuse crystal structures containing imprints of bound ligands - of key importance when making live predictions, where no allosteric regulatory sites are known.Allosteric regulatory sites are highly prized targets in drug discovery. They remain difficult to detect by conventional methods, with the vast majority of known examples being found serendipitously. Herein, a rigorous, wholly-computational protocol is presented for the prediction of allosteric sites. Previous attempts to predict the location of allosteric sites by computational means drew on only a small amount of data. Moreover, no attempt was made to modify the initial crystal structure beyond the in silico deletion of the allosteric ligand. This behaviour can leave behind a conformation with a significant structural deformation, often betraying the location of the allosteric binding site. Despite this artificial advantage, modest success rates are observed at best. This work addresses both of these issues. A set of 60 protein crystal structures with known allosteric modulators was collected. To remove the imprint on protein structure caused by the presence of bound modulators, molecular dynamics was performed on each protein prior to analysis. A wide variety of analytical techniques were then employed to extract meaningful data from the trajectories. Upon fusing them into a single, coherent dataset, random forest - a machine learning algorithm - was applied to train a high performance classification model. After successive rounds of optimisation, the final model presented in this work correctly identified the allosteric site for 72% of the proteins tested. This is not only an improvement over alternative strategies in the literature; crucially, this method is unique among site prediction tools in that is does not abuse crystal structures containing imprints of bound ligands - of key importance when making live predictions, where no allosteric regulatory sites are known

Lipophilicity in drug design: an overview of lipophilicity descriptors in 3D-QSAR studies

The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modeling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies

A review of calcineurin biophysics with implications for cardiac physiology

Calmodulin is a prevalent calcium sensing protein found in all cells. Three genes exist for calmodulin and all three of these genes encode for the exact same protein sequence. Recently mutations in the amino acid sequence of calmodulin have been identified in living human patients. Thus far, patients harboring these mutations in the calmodulin sequence have only displayed an altered cardiac related phenotype. Calcineurin is involved in many key physiological processes and its activity is regulated by calcium and calmodulin. In order to assess whether or not calcineurin contributes to calmodulinopathy (a pathological state arising from dysfunctional calmodulin), a comprehensive search of relevant literature has been performed. Herein, the physiological roles of calcineurin and consequences of dysfunction have been reviewed for literature focused on the heart