Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

MetaSpot: A General Approach for Recognizing the Reactive Atoms Undergoing Metabolic Reactions Based on the MetaQSAR Database.

The prediction of drug metabolism is attracting great interest for the possibility of discarding molecules with unfavorable ADME/Tox profile at the early stage of the drug discovery process. In this context, artificial intelligence methods can generate highly performing predictive models if they are trained by accurate metabolic data. MetaQSAR-based datasets were collected to predict the sites of metabolism for most metabolic reactions. The models were based on a set of structural, physicochemical, and stereo-electronic descriptors and were generated by the random forest algorithm. For each considered biotransformation, two types of models were developed: the first type involved all non-reactive atoms and included atom types among the descriptors, while the second type involved only non-reactive centers having the same atom type(s) of the reactive atoms. All the models of the first type revealed very high performances; the models of the second type show on average worst performances while being almost always able to recognize the reactive centers; only conjugations with glucuronic acid are unsatisfactorily predicted by the models of the second type. Feature evaluation confirms the major role of lipophilicity, self-polarizability, and H-bonding for almost all considered reactions. The obtained results emphasize the possibility of recognizing the sites of metabolism by classification models trained on MetaQSAR database. The two types of models can be synergistically combined since the first models identify which atoms can undergo a given metabolic reactions, while the second models detect the truly reactive centers. The generated models are available as scripts for the VEGA program.

The VEGA web service: multipurpose online tools for molecular modelling and docking analyses.

The paper presents the VEGA Online web service, which includes a set of freely available tools deriving from the development of the VEGA suite of programs. In detail, the paper is focused on two tools: the VEGA Web Edition (WE) and the Score tool. The former is a versatile file format converter including relevant features for 2D/3D conversion, for surface mapping and for editing/preparing input files. The Score application allows rescoring docking poses and in particular includes the MLP Interactions Scores (MLPInS) for describing hydrophobic interactions. To the best of our knowledge, this web service is the only available resource by which one can calculate both the virtual log P of a given input molecule according to the MLP approach plus the corresponding MLP surface.

Obesity and Type 2 Diabetes: Adiposopathy as a Triggering Factor and Therapeutic Options.

Obesity and type 2 diabetes (T2DM) are major public health concerns associated with serious morbidity and increased mortality. Both obesity and T2DM are strongly associated with adiposopathy, a term that describes the pathophysiological changes of the adipose tissue. In this review, we have highlighted adipose tissue dysfunction as a major factor in the etiology of these conditions since it promotes chronic inflammation, dysregulated glucose homeostasis, and impaired adipogenesis, leading to the accumulation of ectopic fat and insulin resistance. This dysfunctional state can be effectively ameliorated by the loss of at least 15% of body weight, that is correlated with better glycemic control, decreased likelihood of cardiometabolic disease, and an improvement in overall quality of life. Weight loss can be achieved through lifestyle modifications (healthy diet, regular physical activity) and pharmacotherapy. In this review, we summarized different effective management strategies to address weight loss, such as bariatric surgery and several classes of drugs, namely metformin, GLP-1 receptor agonists, amylin analogs, and SGLT2 inhibitors. These drugs act by targeting various mechanisms involved in the pathophysiology of obesity and T2DM, and they have been shown to induce significant weight loss and improve glycemic control in obese individuals with T2DM.

Cyclo(His-Pro) Exerts Protective Carbonyl Quenching Effects through Its Open Histidine Containing Dipeptides.

Cyclo(His-Pro) (CHP) is a cyclic dipeptide which is endowed with favorable pharmacokinetic properties combined with a variety of biological activities. CHP is found in a number of protein-rich foods and dietary supplements. While being stable at physiological pH, CHP can open yielding two symmetric dipeptides (His-Pro, Pro-His), the formation of which might be particularly relevant from dietary CHP due to the gastric acidic environment. The antioxidant and protective CHP properties were repeatedly reported although the non-enzymatic mechanisms were scantly investigated. The CHP detoxifying activity towards α,ß unsaturated carbonyls was never investigated in detail, although its open dipeptides might be effective as already observed for histidine containing dipeptides. Hence, this study investigated the scavenging properties of TRH, CHP and its open derivatives towards 4-hydroxy-2-nonenal. The obtained results revealed that Pro-His possesses a marked activity and is more reactive than l-carnosine. As investigated by DFT calculations, the enhanced reactivity can be ascribed to the greater electrophilicity of the involved iminium intermediate. These findings emphasize that the primary amine (as seen in l-carnosine) can be replaced by secondary amines with beneficial effects on the quenching mechanisms. Serum stability of the tested peptides was also evaluated, showing that Pro-His is characterized by a greater stability than l-carnosine. Docking simulations suggested that its hydrolysis can be catalyzed by serum carnosinase. Altogether, the reported results evidence that the antioxidant CHP properties can be also due to the detoxifying activity of its open dipeptides, which might be thus responsible for the beneficial effects induced by CHP containing food.

Hempseed (Cannabis sativa) Peptide H3 (IGFLIIWV) Exerts Cholesterol-Lowering Effects in Human Hepatic Cell Line.

Hempseed (Cannabis sativa) protein is an important source of bioactive peptides. H3 (IGFLIIWV), a transepithelial transported intestinal peptide obtained from the hydrolysis of hempseed protein with pepsin, carries out antioxidant and anti-inflammatory activities in HepG2 cells. In this study, the main aim was to assess its hypocholesterolemic effects at a cellular level and the mechanisms behind this health-promoting activity. The results showed that peptide H3 inhibited the 3-hydroxy-3-methylglutaryl co-enzyme A reductase (HMGCoAR) activity in vitro in a dose-dependent manner with an IC50 value of 59 µM. Furthermore, the activation of the sterol regulatory element binding proteins (SREBP)-2 transcription factor, followed by the increase of low-density lipoprotein (LDL) receptor (LDLR) protein levels, was observed in human hepatic HepG2 cells treated with peptide H3 at 25 µM. Meanwhile, peptide H3 regulated the intracellular HMGCoAR activity through the increase of its phosphorylation by the activation of AMP-activated protein kinase (AMPK)-pathways. Consequently, the augmentation of the LDLR localized on the cellular membranes led to the improved ability of HepG2 cells to uptake extracellular LDL with a positive effect on cholesterol levels. Unlike the complete hempseed hydrolysate (HP), peptide H3 can reduce the proprotein convertase subtilisin/kexin 9 (PCSK9) protein levels and its secretion in the extracellular environment via the decrease of hepatic nuclear factor 1-α (HNF1-α). Considering all these evidences, H3 may represent a new bioactive peptide to be used for the development of dietary supplements and/or peptidomimetics for cardiovascular disease (CVD) prevention.

MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database.

(1) Background: Machine learning algorithms are finding fruitful applications in predicting the ADME profile of new molecules, with a particular focus on metabolism predictions. However, the development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44.

MetaTREE, a Novel Database Focused on Metabolic Trees, Predicts an Important Detoxification Mechanism: The Glutathione Conjugation.

(1) Background: Data accuracy plays a key role in determining the model performances and the field of metabolism prediction suffers from the lack of truly reliable data. To enhance the accuracy of metabolic data, we recently proposed a manually curated database collected by a meta-analysis of the specialized literature (MetaQSAR). Here we aim to further increase data accuracy by focusing on publications reporting exhaustive metabolic trees. This selection should indeed reduce the number of false negative data. (2) Methods: A new metabolic database (MetaTREE) was thus collected and utilized to extract a dataset for metabolic data concerning glutathione conjugation (MT-dataset). After proper pre-processing, this dataset, along with the corresponding dataset extracted from MetaQSAR (MQ-dataset), was utilized to develop binary classification models using a random forest algorithm. (3) Results: The comparison of the models generated by the two collected datasets reveals the better performances reached by the MT-dataset (MCC raised from 0.63 to 0.67, sensitivity from 0.56 to 0.58). The analysis of the applicability domain also confirms that the model based on the MT-dataset shows a more robust predictive power with a larger applicability domain. (4) Conclusions: These results confirm that focusing on metabolic trees represents a convenient approach to increase data accuracy by reducing the false negative cases. The encouraging performances shown by the models developed by the MT-dataset invites to use of MetaTREE for predictive studies in the field of xenobiotic metabolism.

GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics.

Predicting the structures of metabolites formed in humans can provide advantageous insights for the development of drugs and other compounds. Here we present GLORYx, which integrates machine learning-based site of metabolism (SoM) prediction with reaction rule sets to predict and rank the structures of metabolites that could potentially be formed by phase 1 and/or phase 2 metabolism. GLORYx extends the approach from our previously developed tool GLORY, which predicted metabolite structures for cytochrome P450-mediated metabolism only. A robust approach to ranking the predicted metabolites is attained by using the SoM probabilities predicted by the FAME 3 machine learning models to score the predicted metabolites. On a manually curated test data set containing both phase 1 and phase 2 metabolites, GLORYx achieves a recall of 77% and an area under the receiver operating characteristic curve (AUC) of 0.79. Separate analysis of performance on a large amount of freely available phase 1 and phase 2 metabolite data indicates that achieving a meaningful ranking of predicted metabolites is more difficult for phase 2 than for phase 1 metabolites. GLORYx is freely available as a web server at https://nerdd.zbh.uni-hamburg.de/ and is also provided as a software package upon request. The data sets as well as all the reaction rules from this work are also made freely available.

The VEGA suite of programs: an versatile platform for cheminformatics and drug design projects.

The purpose of the article is to offer an overview of the latest release of the VEGA suite of programs. This software has been constantly developed and freely released during the last 20 years and has now reached a significant diffusion and technology level as confirmed by the about 22 500 registered users. While being primarily developed for drug design studies, the VEGA package includes cheminformatics and modeling features, which can be fruitfully utilized in various contexts of the computational chemistry. To offer a glimpse of the remarkable potentials of the software, some examples of the implemented features in the cheminformatics field and for structure-based studies are discussed. Finally, the flexible architecture of the VEGA program which can be expanded and customized by plug-in technology or scripting languages will be described focusing attention on the HyperDrive library including highly optimized functions. AVAILABILITY AND IMPLEMENTATION: The VEGA suite of programs and the source code of the VEGA command-line version are available free of charge for non-profit organizations at http://www.vegazz.net.

Repositioning Dequalinium as Potent Muscarinic Allosteric Ligand by Combining Virtual Screening Campaigns and Experimental Binding Assays.

Structure-based virtual screening is a truly productive repurposing approach provided that reliable target structures are available. Recent progresses in the structural resolution of the G-Protein Coupled Receptors (GPCRs) render these targets amenable for structure-based repurposing studies. Hence, the present study describes structure-based virtual screening campaigns with a view to repurposing known drugs as potential allosteric (and/or orthosteric) ligands for the hM2 muscarinic subtype which was indeed resolved in complex with an allosteric modulator thus allowing a precise identification of this binding cavity. First, a docking protocol was developed and optimized based on binding space concept and enrichment factor optimization algorithm (EFO) consensus approach by using a purposely collected database including known allosteric modulators. The so-developed consensus models were then utilized to virtually screen the DrugBank database. Based on the computational results, six promising molecules were selected and experimentally tested and four of them revealed interesting affinity data; in particular, dequalinium showed a very impressive allosteric modulation for hM2. Based on these results, a second campaign was focused on bis-cationic derivatives and allowed the identification of other two relevant hM2 ligands. Overall, the study enhances the understanding of the factors governing the hM2 allosteric modulation emphasizing the key role of ligand flexibility as well as of arrangement and delocalization of the positively charged moieties.

Impact of the Journal of Chemical Information and Modeling Special Issue on Women in Computational Chemistry.

In this Viewpoint, we provide a commentary on the impact of the Journal of Chemical Information and Modeling Special Issue on Women in Computational Chemistry published in May 2019 and the feedback we received.

Carbachol dimers with primary carbamate groups as homobivalent modulators of muscarinic receptors.

Although agonists and antagonists of muscarinic receptors have been known for long time, there is renewed interest in compounds (such as allosteric or bitopic ligands, or biased agonists) able to differently and selectively modulate these receptors. As a continuation of our previous research, we designed a new series of dimers of the well-known cholinergic agonist carbachol. The new compounds were tested on the five cloned human muscarinic receptors (hM1-5) expressed in CHO cells by means of equilibrium binding experiments, showing a dependence of the binding affinity on the length and position of the linker connecting the two monomers. Kinetic binding studies revealed that some of the tested compounds were able to slow the rate of NMS dissociation, suggesting allosteric behavior, also supported by docking simulations. Assessment of ERK1/2 phosphorylation on hM1, hM2 and hM3 activation showed that the new compounds are endowed with muscarinic antagonist properties. At hM2 receptors, some compounds were able to stimulate GTPγS binding but not cAMP accumulation, suggesting a biased behavior. Classification, Molecular and cellular pharmacology.

Unveiling the molecular mechanisms underpinning biorecognition of early-glycated human serum albumin and receptor for advanced glycation end products.

Serum levels of early-glycated albumin are significantly increased in patients with diabetes mellitus and may play a role in worsening inflammatory status and sustaining diabetes-related complications. To investigate possible pathological recognition involving early-glycated albumin and the receptor for advanced glycation end products (RAGE), an early-glycated human serum albumin (HSAgly), with a glycation pattern representative of the glycated HSA form abundant in diabetic patients, and the recombinant human RAGE ectodomain (VC1) were used. Biorecognition between the two interactants was investigated by combining surface plasmon resonance (SPR) analysis and affinity chromatography coupled with mass spectrometry (affinity-MS) for peptide extraction and identification. SPR analysis proved early-glycated albumin could interact with the RAGE ectodomain with a steady-state affinity constant of 6.05 ± 0.96 × 10-7 M. Such interaction was shown to be specific, as confirmed by a displacement assay with chondroitin sulfate, a known RAGE binder. Affinity-MS studies were performed to map the surface area involved in the recognition. These studies highlighted that a region surrounding Lys525 and part of subdomain IA were involved in VC1 recognition. Finally, an in silico analysis highlighted (i) a key role for glycation at Lys525 (the most commonly glycated residue in HSA in diabetic patients) through a triggering mechanism similar to that previously observed for AGEs or advanced lipoxidation end products and (ii) a stabilizing role for subdomain IA. Albeit a moderate affinity for complex formation, the high plasma levels of early-glycated albumin and high percentage of glycation at Lys525 in diabetic patients make this interaction of possible pathological relevance. Graphical abstract.

FAME 3: Predicting the Sites of Metabolism in Synthetic Compounds and Natural Products for Phase 1 and Phase 2 Metabolic Enzymes.

In this work we present the third generation of FAst MEtabolizer (FAME 3), a collection of extra trees classifiers for the prediction of sites of metabolism (SoMs) in small molecules such as drugs, druglike compounds, natural products, agrochemicals, and cosmetics. FAME 3 was derived from the MetaQSAR database ( Pedretti et al. J. Med. Chem. 2018 , 61 , 1019 ), a recently published data resource on xenobiotic metabolism that contains more than 2100 substrates annotated with more than 6300 experimentally confirmed SoMs related to redox reactions, hydrolysis and other nonredox reactions, and conjugation reactions. In tests with holdout data, FAME 3 models reached competitive performance, with Matthews correlation coefficients (MCCs) ranging from 0.50 for a global model covering phase 1 and phase 2 metabolism, to 0.75 for a focused model for phase 2 metabolism. A model focused on cytochrome P450 metabolism yielded an MCC of 0.57. Results from case studies with several synthetic compounds, natural products, and natural product derivatives demonstrate the agreement between model predictions and literature data even for molecules with structural patterns clearly distinct from those present in the training data. The applicability domains of the individual models were estimated by a new, atom-based distance measure (FAMEscore) that is based on a nearest-neighbor search in the space of atom environments. FAME 3 is available via a public web service at https://nerdd.zbh.uni-hamburg.de/ and as a self-contained Java software package, free for academic and noncommercial research.

Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns.

The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average.

Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database.

Even though glucuronidations are the most frequent metabolic reactions of conjugation, both in quantitative and qualitative terms, they have rather seldom been investigated using computational approaches. To fill this gap, we have used the manually collected MetaQSAR metabolic reaction database to generate two models for the prediction of UGT-mediated metabolism, both based on molecular descriptors and implementing the Random Forest algorithm. The first model predicts the occurrence of the reaction and was internally validated with a Matthew correlation coefficient (MCC) of 0.76 and an area under the ROC curve (AUC) of 0.94, and further externally validated using a test set composed of 120 additional xenobiotics (MCC of 0.70 and AUC of 0.90). The second model distinguishes between O- and N-glucuronidations and was optimized by the random undersampling procedure to improve the predictive accuracy during the internal validation, with the recall measure of the minority class increasing from 0.55 to 0.78.

Prediction of the Formation of Reactive Metabolites by A Novel Classifier Approach Based on Enrichment Factor Optimization (EFO) as Implemented in the VEGA Program.

The study is aimed at developing linear classifiers to predict the capacity of a given substrate to yield reactive metabolites. While most of the hitherto reported predictive models are based on the occurrence of known structural alerts (e.g., the presence of toxophoric groups), the present study is focused on the generation of predictive models involving linear combinations of physicochemical and stereo-electronic descriptors. The development of these models is carried out by using a novel classification approach based on enrichment factor optimization (EFO) as implemented in the VEGA suite of programs. The study took advantage of metabolic data as collected by manually curated analysis of the primary literature and published in the years 2004⁻2009. The learning set included 977 substrates among which 138 compounds yielded reactive first-generation metabolites, plus 212 substrates generating reactive metabolites in all generations (i.e., metabolic steps). The results emphasized the possibility of developing satisfactory predictive models especially when focusing on the first-generation reactive metabolites. The extensive comparison of the classifier approach presented here using a set of well-known algorithms implemented in Weka 3.8 revealed that the proposed EFO method compares with the best available approaches and offers two relevant benefits since it involves a limited number of descriptors and provides a score-based probability thus allowing a critical evaluation of the obtained results. The last analyses on non-cheminformatics UCI datasets emphasize the general applicability of the EFO approach, which conveniently performs using both balanced and unbalanced datasets.