Computational approaches to shed light on molecular mechanisms in biological processes

Computational approaches based on Molecular Dynamics simulations, Quantum Mechanical methods and 3D Quantitative Structure-Activity Relationships were employed by computational chemistry groups at the University of Milano-Bicocca to study biological processes at the molecular level. The paper reports the methodologies adopted and the results obtained on Aryl hydrocarbon Receptor and homologous PAS proteins mechanisms, the properties of prion protein peptides, the reaction pathway of hydrogenase and peroxidase enzymes and the defibrillogenic activity of tetracyclines. © Springer-Verlag 2007

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458

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 computational study of the substrate conversion and selective inhibition of aldosterone synthase

When a functional or structural impairment of cardiac output has occurred, the cardiovascular system will attempt to compensate for the reduced blood flow. Unfortunately, many of the resulting processes, such as the renin angiotensin aldosterone system, will progressively weaken the heart, resulting in the condition called heart failure. The renin angiotensin aldosterone regulatory system is currently targeted with medicine for heart failure. Many successes for the prolongation of patient age have been achieved by inhibition of angiotensin II synthesis and action. It has become apparent that this approach is suboptimal. Antagonists of aldosterone have provided better treatment options, however, side-effects are still observed. In the search for an alternative therapeutic application, we have studied a novel treatment involving the selective inhibition of aldosterone biosynthesis. The scope of this study has involved the in silico design and prediction of novel inhibitors, the synthesis of these inhibitors and analogues, and finally the in vitro measurement of their potency. The biosynthesis of aldosterone is performed by two cytochrome p450 enzymes, 11B1 and 11B2, denoted as CYP11B1 and CYP11B2, respectively. From these two family members, only CYP11B2 can perform the final synthesis step that converts 18-hydroxycorticosterone into aldosterone. CYP11B1 performs the synthesis of glucocorticoids that are responsible for metabolic, immunologic and homeostatic functions. Because these glucocorticoid actions should not be inhibited, the newly designed medicine must be CYP11B2 selective. Since CYP11B1 is highly homologous to CYP11B2, we have performed an in silico study that allows us to model the interactions of substrates and inhibitors in both the active sites of CYP11B1 and CYP11B2. Using comparative modelling, we have constructed models for the three dimensional architecture of both proteins. These models have been validated by investigating the torsional properties of the protein backbone and residue side chains, the overall protein packing and the dynamic behaviour of the protein models. Subsequently, the models have been used to evaluate the binding mechanisms and conversion mechanisms for the natural steroidal ligands of CYP11B1 and CYP11B2. A hypothetical binding mode has been proposed for 18-hydroxycorticosterone in CYP11B2, featuring the presence of stabilising hydrogen bonding interactions required for its conversion. Quantum mechanical analyses on the conversion of the steroids involved have shown a favourable conversion for this conformation, thereby supporting our hypothesis. In addition, the quantum mechanical analyses have provided insights on steroid conformations in the active sites during conversion. The suitability of the protein models for inhibitor design has been tested by subjecting the models to a case study with four known inhibitors of CYP11B1 and CYP11B2. Using molecular dynamics and molecular docking, the inhibitor potencies for CYP11B1 and CYP11B2 have been predicted, and their interactions with the proteins have been evaluated. The trends in inhibitor potency found by these computational methods have been confirmed by in vitro inhibition measurements. As a next step, the molecular docking study has been expanded to improve the confidence in the predictive power of the models. Using the protein states evaluated by the molecular dynamics study, the molecular docking results of inhibitor analogues have been investigated and the predictive power of the models has been qualitatively improved. In a final approach, we have performed a ligand-based investigation of the inhibitor analogues to determine which ligand characteristics are important for the potency for CYP11B1 and CYP11B2. To this end, we have conducted decision tree analyses on the physico-chemical properties of inhibitor substituents, resulting in a collection of descriptors that can be used for the prediction and design of novel inhibitors. We have shown that a combination of synthesis, molecular modelling and experimental measurements form a promising approach towards the design of potentially new inhibitors

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Exploring QSARs of some Translocator protein (TSPO) ligands using MLR and PC-ANN techniques

Quantitative structure-activity relationship study was performed to understand the activity of a set of 136 ligands of Translocator protein (TSPO) compounds. QSAR models were developed using multiple linear regression (MLR) as linear method. While principal component - artificial neural networks (PC-ANN) modeling method was used as nonlinear method. The results obtained offer good regression models having good prediction ability. The MLR resulted with models (12-24) which have coefficient of determination (R 2 ) >0.6, the best model (number 24) resulted with correlation coefficient (R) = 0.909, coefficient of determination (R 2 ) = 0.826, and adjusted coefficient of determination (R 2 adj) = 0.788. Cross Validation leave one out (LOO) and leave many out (LMO) were performed on the resulted MLR models, models 19-24 showed a good predictive power. After that principle component analysis (PCA) performed to divide the data into three data sets, then the ANN performed on the chosen models (19-24) from leave one out (LOO) and leave many out (LMO) validation. ANN resulted models were validated through randomization test, then the conditions proposed by Golbraikh and Tropsha were applied to conclude that the QSAR models has acceptable prediction power or not. However the best ANN model with a good predictivepower was model #24, with R test values 0.83