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  • 1.
    Béquignon, Olivier J. M.
    et al.
    Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands.
    Gómez-Tamayo, Jose C.
    Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Hospital del Mar Medical Research Institute, Universitat Pompeu Fabra, Carrer del Dr. Aiguader 88, 08002 Barcelona, Spain.
    Lenselink, Eelke B.
    Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands.
    Wink, Steven
    Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands.
    Hiemstra, Steven
    Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands.
    Lam, Chi Chung
    Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands.
    Gadaleta, Domenico
    Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS─Istituto di Ricerche Farmacologiche Mario Negri, Via la Masa 19, 20156 Milano, Italy.
    Roncaglioni, Alessandra
    Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS─Istituto di Ricerche Farmacologiche Mario Negri, Via la Masa 19, 20156 Milano, Italy.
    Norinder, Ulf
    Örebro University, School of Science and Technology.
    Water, Bob van de
    Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands.
    Pastor, Manuel
    Research Programme on Biomedical Informatics (GRIB), Department of Medicine and Life Sciences, Hospital del Mar Medical Research Institute, Universitat Pompeu Fabra, Carrer del Dr. Aiguader 88, 08002 Barcelona, Spain.
    van Westen, Gerard J. P.
    Leiden Academic Centre for Drug Research, Leiden University, Wassenaarseweg 76, 2333 AL Leiden, The Netherlands.
    Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells2023In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 63, no 17, p. 5433-5445Article in journal (Refereed)
    Abstract [en]

    Oxidative stress is the consequence of an abnormal increase of reactive oxygen species (ROS). ROS are generated mainly during the metabolism in both normal and pathological conditions as well as from exposure to xenobiotics. Xenobiotics can, on the one hand, disrupt molecular machinery involved in redox processes and, on the other hand, reduce the effectiveness of the antioxidant activity. Such dysregulation may lead to oxidative damage when combined with oxidative stress overpassing the cell capacity to detoxify ROS. In this work, a green fluorescent protein (GFP)-tagged nuclear factor erythroid 2-related factor 2 (NRF2)-regulated sulfiredoxin reporter (Srxn1-GFP) was used to measure the antioxidant response of HepG2 cells to a large series of drug and drug-like compounds (2230 compounds). These compounds were then classified as positive or negative depending on cellular response and distributed among different modeling groups to establish structure-activity relationship (SAR) models. A selection of models was used to prospectively predict oxidative stress induced by a new set of compounds subsequently experimentally tested to validate the model predictions. Altogether, this exercise exemplifies the different challenges of developing SAR models of a phenotypic cellular readout, model combination, chemical space selection, and results interpretation.

  • 2.
    Eklund, Martin
    et al.
    Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; AstraZeneca Research and Development, Mölndal, Sweden.
    Norinder, Ulf
    H Lundbeck & Co AS, Valby, Denmark.
    Boyer, Scott
    AstraZeneca Research and Development, Mölndal, Sweden.
    Carlsson, Lars
    AstraZeneca Research and Development, Mölndal, Sweden.
    Choosing Feature Selection and Learning Algorithms in QSAR2014In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 54, no 3, p. 837-843Article in journal (Refereed)
    Abstract [en]

    Feature selection is an important part of contemporary QSAR analysis. In a recently published paper, we investigated the performance of different feature selection methods in a large number of in silico experiments conducted using real QSAR datasets. However, an interesting question that we did not address is whether certain feature selection methods are better than others in combination with certain learning methods, in terms of producing models with high prediction accuracy. In this report we extend our work from the previous investigation by using four different feature selection methods (wrapper, ReliefF, MARS, and elastic nets), together with eight learners (MARS, elastic net, random forest, SVM, neural networks, multiple linear regression, PLS, kNN) in an empirical investigation to address this question. The results indicate that state-of-the-art learners (random forest, SVM, and neural networks) do not gain prediction accuracy from feature selection, and we found no evidence that a certain feature selection is particularly well-suited for use in combination with a certain learner.

  • 3.
    Gao, Li
    et al.
    Örebro University, School of Science and Technology.
    Tu, Yaoquan
    Örebro University, School of Science and Technology.
    Wegman [Palmebäck-Wegman], Pia
    Örebro University, School of Health and Medical Sciences.
    Wingren, Sten
    Örebro University, School of Health and Medical Sciences.
    Eriksson, Leif A.
    A mechanistic hypothesis for the cytochrome P450-catalyzed cis-trans isomerization of 4-hydroxytamoxifen: an unusual redox reaction2011In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 51, no 9, p. 2293-2301Article in journal (Refereed)
    Abstract [en]

    We provide a detailed description of the cis-trans isomerization of 4-hydroxytamoxifen/endoxifen catalyzed by several isoforms from the cytochrome P450 (CYP) superfamily, including CYP1B1, CYP2B6, and CYP2C19. We show that the reactions mainly involve redox processes catalyzed by CYP, DFT calculation results strongly suggest that the isomerization occurs via a cationic intermediate. The cationic cis-isomer is more than 3 kcal/mol more stable than the trans form, resulting in an easier conversion from trans-to-cis than cis-to-trans. The cis-trans isomerization is a rarely reported CYP reaction and is ascribed to the lack of a second abstractable proton on the ethenyl group of the triarylvinyl class of substrates. The cationic intermediates thus formed instead of the stable dehydrogenation products allow for isomerization to occur. As a comparison, the reactions for the tamoxifen derivatives are compared to those of other substrates, 4-hydroxyacetanilide and raloxifene, for which the stable dehydrogenation products are formed.

  • 4.
    Gao, Li
    et al.
    Örebro University, School of Science and Technology.
    Tu, Yaoquan
    Örebro University, School of Science and Technology.
    Wegman [Palmebäck-Wegman], Pia
    Örebro University, School of Science and Technology.
    Wingren, Sten
    Örebro University, School of Science and Technology.
    Eriksson, Leif A.
    Örebro University, School of Science and Technology.
    Conformational enantiomerization and estrogen receptor alpha binding of anti-cancer drug tamoxifen and its derivatives2011In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 51, no 2, p. 306-314Article in journal (Refereed)
    Abstract [en]

    The anticancer drug tamoxifen (TAM) displays two chiral vinyl propeller structures, which interconvert so rapidly that the process is undetectable on the NMR time scale. In the present work, the enantiomerization processes were investigated with molecular modeling techniques. The threshold mechanisms probed at the different rings were shown to be identical, i.e., involving a synchronous three-ring flip, with a correlated rotation of the rings. In order to reveal the pharmacological profiles of the two chiral forms, we performed structural studies on the ligand binding domain of estrogen receptor alpha. (ER alpha LBD) and associated ligands. The enantiomers, with opposite torsional twist, were found to be discriminated by ER alpha. For TAM and its main metabolites, the effects of the stereoselectivity of ER alpha are overcome by the low energy cost for helical inversion between the two torsional enantiomers, estimated to be similar to 3 kcal/mol.

  • 5.
    Garcia de Lomana, Marina
    et al.
    BASF SE, Ludwigshafen am Rhein, Germany; Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
    Morger, Andrea
    In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Berlin, Germany.
    Norinder, Ulf
    Örebro University, School of Science and Technology.
    Buesen, Roland
    BASF SE, Ludwigshafen am Rhein, Germany.
    Landsiedel, Robert
    BASF SE, Ludwigshafen am Rhein, Germany.
    Volkamer, Andrea
    In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité Universitätsmedizin Berlin, Berlin, Germany.
    Kirchmair, Johannes
    Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Vienna, Austria.
    Mathea, Miriam
    BASF SE, Ludwigshafen am Rhein, Germany.
    ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities2021In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 61, no 7, p. 3255-3272Article in journal (Refereed)
    Abstract [en]

    Computational methods such as machine learning approaches have a strong track record of success in predicting the outcomes of in vitro assays. In contrast, their ability to predict in vivo endpoints is more limited due to the high number of parameters and processes that may influence the outcome. Recent studies have shown that the combination of chemical and biological data can yield better models for in vivo endpoints. The ChemBioSim approach presented in this work aims to enhance the performance of conformal prediction models for in vivo endpoints by combining chemical information with (predicted) bioactivity assay outcomes. Three in vivo toxicological endpoints, capturing genotoxic (MNT), hepatic (DILI), and cardiological (DICC) issues, were selected for this study due to their high relevance for the registration and authorization of new compounds. Since the sparsity of available biological assay data is challenging for predictive modeling, predicted bioactivity descriptors were introduced instead. Thus, a machine learning model for each of the 373 collected biological assays was trained and applied on the compounds of the in vivo toxicity data sets. Besides the chemical descriptors (molecular fingerprints and physicochemical properties), these predicted bioactivities served as descriptors for the models of the three in vivo endpoints. For this study, a workflow based on a conformal prediction framework (a method for confidence estimation) built on random forest models was developed. Furthermore, the most relevant chemical and bioactivity descriptors for each in vivo endpoint were preselected with lasso models. The incorporation of bioactivity descriptors increased the mean F1 scores of the MNT model from 0.61 to 0.70 and for the DICC model from 0.72 to 0.82 while the mean efficiencies increased by roughly 0.10 for both endpoints. In contrast, for the DILI endpoint, no significant improvement in model performance was observed. Besides pure performance improvements, an analysis of the most important bioactivity features allowed detection of novel and less intuitive relationships between the predicted biological assay outcomes used as descriptors and the in vivo endpoints. This study presents how the prediction of in vivo toxicity endpoints can be improved by the incorporation of biological information-which is not necessarily captured by chemical descriptors-in an automated workflow without the need for adding experimental workload for the generation of bioactivity descriptors as predicted outcomes of bioactivity assays were utilized. All bioactivity CP models for deriving the predicted bioactivities, as well as the in vivo toxicity CP models, can be freely downloaded from https://doi.org/10.5281/zenodo.4761225.

  • 6.
    Norinder, Ulf
    et al.
    Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
    Spjuth, Ola
    Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden; Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
    Svensson, Fredrik
    The Alzheimer's Research UK University College London Drug Discovery Institute, London, U.K..
    Using Predicted Bioactivity Profiles to Improve Predictive Modeling2020In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 60, no 6, p. 2830-2837Article in journal (Refereed)
    Abstract [en]

    Predictive modeling is a cornerstone in early drug development. Using information for multiple domains or across prediction tasks has the potential to improve the performance of predictive modeling. However, aggregating data often leads to incomplete data matrices that might be limiting for modeling. In line with previous studies, we show that by generating predicted bioactivity profiles, and using these as additional features, prediction accuracy of biological endpoints can be improved. Using conformal prediction, a type of confidence predictor, we present a robust framework for the calculation of these profiles and the evaluation of their impact. We report on the outcomes from several approaches to generate the predicted profiles on 16 datasets in cytotoxicity and bioactivity and show that efficiency is improved the most when including the p-values from conformal prediction as bioactivity profiles.

  • 7.
    Norinder, Ulf
    et al.
    Swetox, Karolinska Institute, Unit of Toxicology Sciences, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Svensson, Fredrik
    Alzheimer's Research UK UCL Drug Discovery Institute, University College, London, England; Francis Crick Institute, London, England.
    Multitask Modeling with Confidence Using Matrix Factorization and Conformal Prediction2019In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, no 4, p. 1598-1604Article in journal (Refereed)
    Abstract [en]

    Multitask prediction of bioactivities is often faced with challenges relating to the sparsity of data and imbalance between different labels. We propose class conditional (Mondrian) conformal predictors using underlying Macau models as a novel approach for large scale bioactivity prediction. This approach handles both high degrees of missing data and label imbalances while still producing high quality predictive models. When applied to ten assay end points from PubChem, the models generated valid models with an efficiency of 74.0-80.1% at the 80% confidence level with similar performance both for the minority and majority class. Also when deleting progressively larger portions of the available data (0-80%) the performance of the models remained robust with only minor deterioration (reduction in efficiency between 5 and 10%). Compared to using Macau without conformal prediction the method presented here significantly improves the performance on imbalanced data sets.

  • 8.
    Saenz-Méndez, Patricia
    et al.
    Computational Chemistry and Biology Group, Facultad de Química, Universidad de la República (UdelaR), Montevideo, Uruguay.
    Elmabsout, Ali Ateia
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Department of Clinical Medicine, School of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Sävenstrand, Helena
    Örebro University, School of Science and Technology.
    Awadalla, Mohamed Khalid Alhaj
    Department of Clinical Medicine, School of Health Sciences, Örebro University, Örebro, Sweden.
    Strid, Åke
    Örebro University, School of Science and Technology.
    Sirsjö, Allan
    Örebro University, School of Health and Medical Sciences, Örebro University, Sweden. Department of Clinical Medicine, School of Health and Medical Sciences, Örebro University, Örebro, Sweden.
    Eriksson, Leif A.
    Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
    Homology models of human all-trans-retinoic acid metabolizing enzymes CYP26B1 and CYP26B1 spliced-variant2012In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 52, no 10, p. 2631-2637Article in journal (Refereed)
    Abstract [en]

    Homology models of CYP26B1 (cytochrome P450RAI2) and CYP26B1 spliced-variant were derived using the crystal structure of cyanobacterial CYP120A1 as template for the model building. The quality of the homology models generated were carefully evaluated, and the natural substrate all-trans-retinoic acid (atRA), several tetralone-derived retinoic acid metabolizing blocking agents (RAMBAs) and a well known potent inhibitor of CYP26B1 (R115866) were docked into the homology model of full-length cytochrome P450 26B1. The results show that in the model of the full length CYP26B1, the protein is capable of distinguishing between the natural substrate (atRA), R115866 and the tetralone derivatives. The spliced-variant of CYP26B1 model displays a reduced affinity for atRA compared to the full length enzyme, in accordance with recently described experimental information.

  • 9.
    Svensson, Fredrik
    et al.
    Department of Chemistry, University of Cambridge, Cambridge, England; IOTA Pharmaceut, Cambridge, England.
    Aniceto, Natalia
    Department of Chemistry, University of Cambridge, Cambridge, England.
    Norinder, Ulf
    Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Cortes-Ciriano, Isidro
    Department of Chemistry, University of Cambridge, Cambridge, England.
    Spjuth, Ola
    Uppsala University, Uppsala, Sweden.
    Carlsson, Lars
    AstraZeneca, Mölndal, Sweden; Department of Computer Science, University of London, Surrey, England.
    Bender, Andreas
    Department of Chemistry, University of Cambridge, Cambridge, England.
    Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty2018In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 58, no 5, p. 1132-1140Article in journal (Refereed)
    Abstract [en]

    Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the outputted prediction intervals to create as efficient (i.e. narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges and the different approaches were evaluated on 29 publicly available datasets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals. This approach afforded an average prediction range of 1.65 pIC50 units at the 80 % confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.

  • 10.
    Svensson, Fredrik
    et al.
    Department of Chemistry, University of Cambridge, Cambridge, England.
    Norinder, Ulf
    Karolinska Institute, Unit of Toxicology Sciences, Södertälje, Sweden; Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Bender, Andreas
    Department of Chemistry, University of Cambridge, Cambridge, England.
    Improving Screening Efficiency through Iterative Screening Using Docking and Conformal Prediction2017In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 57, no 3, p. 439-444Article in journal (Refereed)
    Abstract [en]

    High-throughput screening, where thousands of molecules rapidly can be assessed for activity against a protein, has been the dominating approach in drug discovery for many years. However, these methods are costly and require much time and effort. In order to suggest an improvement to this situation, in this study, we apply an iterative screening process, where an initial set of compounds are selected for screening based on molecular docking. The outcome of the initial screen is then used to classify the remaining compounds through a conformal predictor. The approach was retrospectively validated using 41 targets from the Directory of Useful Decoys, Enhanced (DUD-E), ensuring scaffold diversity among the active compounds. The results show that 57% of the remaining active compounds could be identified while only screening 9.4% of the database. The overall hit rate (7.6%) was also higher than, when using docking alone (5.2%). When limiting the search to the top scored compounds from docking, 39.6% of the active compounds could be identified, compared to 13.5% when screening the same number of compounds solely based on docking. The use of conformal predictors also gives a clear indication of the number of compounds to screen in the next iteration. These results indicate that iterative screening based on molecular docking and conformal prediction can be an efficient way to find active compounds while screening only a small part of the compound collection.

  • 11.
    Tuerkova, Alzbeta
    et al.
    Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.
    Bongers, Brandon J.
    Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
    Norinder, Ulf
    Örebro University, School of Science and Technology. Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
    Ungvári, Orsolya
    Drug Resistance Research Group, Institute of Enzymology, RCNS, Eötvös Loránd Research Network, Budapest, Hungary; Doctoral School of Biology and Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary.
    Székely, Virág
    Drug Resistance Research Group, Institute of Enzymology, RCNS, Eötvös Loránd Research Network, Budapest, Hungary.
    Tarnovskiy, Andrey
    Enamine Ltd., Kyiv, Ukraine.
    Szakács, Gergely
    Drug Resistance Research Group, Institute of Enzymology, RCNS, Eötvös Loránd Research Network, Budapest, Hungary; Department of Medicine I, Institute of Cancer Research, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.
    Özvegy-Laczka, Csilla
    Drug Resistance Research Group, Institute of Enzymology, RCNS, Eötvös Loránd Research Network, Budapest, Hungary.
    van Westen, Gerard J. P.
    Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
    Zdrazil, Barbara
    Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria.
    Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening2022In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 62, no 24, p. 6323-6335Article in journal (Refereed)
    Abstract [en]

    Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets, and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity and drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were determined using dedicated in vitro assays and guided the prioritization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 μM), three OATP1B1 inhibitors (2.69 to 10 μM), and five OATP1B3 inhibitors (1.53 to 10 μM) were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7 and H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses.

  • 12.
    Wu, Min
    et al.
    National University of Ireland, Galway, Ireland.
    Grahn, Elin M.
    Örebro University, School of Science and Technology.
    Eriksson, Leif A.
    National University of Ireland, Galway, Ireland.
    Strid, Åke
    Örebro University, School of Science and Technology.
    Computational evidence for the role of Arabidopsis thaliana UVR8 as UV-B photoreceptor and identification of its chromophore amino acids2011In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 51, no 6, p. 1287-1295Article in journal (Refereed)
    Abstract [en]

    A homology model of the Arabidopsis thaliana UV resistance locus 8 (UVR8) protein is presented herein, showing a seven-bladed β-propeller conformation similar to the globular structure of RCC1. The UVR8 amino acid sequence contains a very high amount of conserved tryptophans, and the homology model shows that seven of these tryptophans cluster at the 'top surface’ of the UVR8 protein where they are intermixed with positive residues (mainly arginines) and a couple of tyrosines. Quantum chemical calculations of excitation spectra of both a large cluster model involving all twelve above-mentioned residues and smaller fragments thereof reveal that absorption maxima appearing in the 280-300 nm range for the full cluster result from interactions between the central tryptophans and surrounding arginines. This observation coincides with the published experimentally measured action spectrum for the UVR8-dependent UVB stimulation of HY5 transcription in mature A. thaliana leaf tissue. In total these findings suggest that UVR8 has in fact in itself the ability to be an ultraviolet-B photoreceptor in plants.

  • 13.
    Wu, Min
    et al.
    University of Gothenburg, Gothenburg, Sweden.
    Strid, Åke
    Örebro University, School of Science and Technology.
    Eriksson, Leif A
    University of Gothenburg, Gothenburg, Sweden.
    Interactions and Stabilities of the UV RESISTANCE LOCUS8 (UVR8) protein dimer and its key mutants2013In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 53, no 7, p. 1736-1746Article in journal (Refereed)
    Abstract [en]

    The dimeric UVR8 protein is a ultraviolet-B radiation (280-315 nm) photoreceptor responsible for the first step in UV-B regulation of gene expression in plants. Its action comprises the actual absorption of the UV quanta by a tryptophan array at the protein-protein interface, followed by monomerisation, and subsequent aggregation with downstream signaling components. A crystal structure of the Arabidopsis thaliana tryptophan-rich wild type UVR8 protein dimer was recently published, showing the presence of several salt bridges involving arginines R146, R286, R338 and R354. In this work, molecular dynamics simulations in conjunction with umbrella sampling was used to calculate the binding free energy for the wild type UVR8 dimer and three of its mutants (R286A, R338A and R286A/R338A), in order to verify whether the key mutants are able to disrupt the dimeric structure as indicated experimentally.

  • 14.
    Zhang, Jin
    et al.
    Department of Chemistry, Umeå University, Umeå, Sweden.
    Mucs, Daniel
    Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden.
    Norinder, Ulf
    Unit of Toxicology Sciences, Karolinska Institute, Södertälje, Sweden; Department of Computer and System Sciences, Stockholm University, Kista, Sweden.
    Svensson, Fredrik
    Drug Discovery Institute, London, England.
    LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox21 and Mutagenicity Data Sets2019In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, no 10, p. 4150-4158Article in journal (Refereed)
    Abstract [en]

    Machine learning algorithms have attained widespread use in assessing the potential toxicities of pharmaceuticals and industrial chemicals because of their faster speed and lower cost compared to experimental bioassays. Gradient boosting is an effective algorithm that often achieves high predictivity, but historically the relative long computational time limited its applications in predicting large compound libraries or developing in silico predictive models that require frequent retraining. LightGBM, a recent improvement of the gradient boosting algorithm, inherited its high predictivity but resolved its scalability and long computational time by adopting a leaf-wise tree growth strategy and introducing novel techniques. In this study, we compared the predictive performance and the computational time of LightGBM to deep neural networks, random forests, support vector machines, and XGBoost. All algorithms were rigorously evaluated on publicly available Tox21 and mutagenicity data sets using a Bayesian optimization integrated nested 10-fold cross-validation scheme that performs hyperparameter optimization while examining model generalizability and transferability to new data. The evaluation results demonstrated that LightGBM is an effective and highly scalable algorithm offering the best predictive performance while consuming significantly shorter computational time than the other investigated algorithms across all Tox21 and mutagenicity data sets. We recommend LightGBM for applications of in silico safety assessment and also other areas of cheminformatics to fulfill the ever-growing demand for accurate and rapid prediction of various toxicity or activity related end points of large compound libraries present in the pharmaceutical and chemical industry.

  • 15.
    Zhang, Jin
    et al.
    Department of Drug Metabolism and Pharmacokinetics, Janssen Pharmaceutica NV, Beerse, Belgium.
    Norinder, Ulf
    Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden; Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
    Svensson, Fredrik
    The Alzheimer's Research UK University College London Drug Discovery Institute, The Cruciform Building, London, U.K.
    Deep Learning-Based Conformal Prediction of Toxicity2021In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 61, no 6, p. 2648-2657Article in journal (Refereed)
    Abstract [en]

    Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.

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