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  • 1.
    Alogheli, Hiba
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Olanders, Gustav
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Brandt, Peter
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Anders, Karlén
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Docking of Macrocycles: Comparing Rigid and Flexible Docking in Glide2017In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 57, no 2, p. 190-202Article in journal (Refereed)
    Abstract [en]

    In recent years, there has been an increased interest in using macrocyclic compounds for drug discovery and development. For docking of these commonly large and flexible compounds to be addressed, a screening and a validation set were assembled from the PDB consisting of 16 and 31 macrocycle-containing protein complexes, respectively. The macrocycles were docked in Glide by rigid docking of pregenerated conformational ensembles produced by the macrocycle conformational sampling method (MCS) in Schrödinger Release 2015-3 or by direct Glide flexible docking after performing ring-templating. The two protocols were compared to rigid docking of pregenerated conformational ensembles produced by an exhaustive Monte Carlo multiple minimum (MCMM) conformational search and a shorter MCMM conformational search (MCMM-short). The docking accuracy was evaluated and expressed as the RMSD between the heavy atoms of the ligand as found in the X-ray structure after refinement and the poses obtained by the docking protocols. The median RMSD values for top-scored poses of the screening set were 0.83, 0.80, 0.88, and 0.58 Å for MCMM, MCMM-short, MCS, and Glide flexible docking, respectively. There was no statistically significant difference in the performance between rigid docking of pregenerated conformations produced by the MCS and direct docking using Glide flexible docking. However, the flexible docking protocol was 2-times faster in docking the screening set compared to that of the MCS protocol. In a final study, the new Prime-MCS method was evaluated in Schrödinger Release 2016-3. This method is faster compared that of to MCS; however, the conformations generated were found to be suboptimal for rigid docking. Therefore, on the basis of timing, accuracy, and ease of set up, standard Glide flexible docking with prior ring-templating is recommended over current gold standard protocols using rigid docking of pregenerated conformational ensembles.

  • 2.
    Ballante, Flavio
    et al.
    Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Università di Roma, P. le A. Moro 5, 00185 Roma, Italy.
    Caroli, Antonia
    Department of Physics, Sapienza Universita ̀ di Roma, P.le Aldo Moro 5, 00185, Roma, Italy.
    Wickersham, Richard B
    Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Roma, Italy; Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis School of Medicine, 700 South Euclid Avenue, St. Louis, Missouri 63110, United States.
    Ragno, Rino
    Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Roma, Italy.
    Hsp90 inhibitors, part 1: definition of 3-D QSAutogrid/R models as a tool for virtual screening.2014In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 54, no 3, p. 956-69Article in journal (Refereed)
    Abstract [en]

    The multichaperone heat shock protein (Hsp) 90 complex mediates the maturation and stability of a variety of oncogenic signaling proteins. For this reason, Hsp90 has emerged as a promising target for anticancer drug development. Herein, we describe a complete computational procedure for building several 3-D QSAR models used as a ligand-based (LB) component of a comprehensive ligand-based (LB) and structure-based (SB) virtual screening (VS) protocol to identify novel molecular scaffolds of Hsp90 inhibitors. By the application of the 3-D QSAutogrid/R method, eight SB PLS 3-D QSAR models were generated, leading to a final multiprobe (MP) 3-D QSAR pharmacophoric model capable of recognizing the most significant chemical features for Hsp90 inhibition. Both the monoprobe and multiprobe models were optimized, cross-validated, and tested against an external test set. The obtained statistical results confirmed the models as robust and predictive to be used in a subsequent VS.

  • 3.
    Ballante, Flavio
    et al.
    Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States.
    Marshall, Garland R
    Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri 63110, United States.
    An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design.2016In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 56, no 1, p. 54-72Article in journal (Refereed)
    Abstract [en]

    Molecular docking is a widely used technique in drug design to predict the binding pose of a candidate compound in a defined therapeutic target. Numerous docking protocols are available, each characterized by different search methods and scoring functions, thus providing variable predictive capability on a same ligand-protein system. To validate a docking protocol, it is necessary to determine a priori the ability to reproduce the experimental binding pose (i.e., by determining the docking accuracy (DA)) in order to select the most appropriate docking procedure and thus estimate the rate of success in docking novel compounds. As common docking programs use generally different root-mean-square deviation (RMSD) formulas, scoring functions, and format results, it is both difficult and time-consuming to consistently determine and compare their predictive capabilities in order to identify the best protocol to use for the target of interest and to extrapolate the binding poses (i.e., best-docked (BD), best-cluster (BC), and best-fit (BF) poses) when applying a given docking program over thousands/millions of molecules during virtual screening. To reduce this difficulty, two new procedures called Clusterizer and DockAccessor have been developed and implemented for use with some common and "free-for-academics" programs such as AutoDock4, AutoDock4(Zn), AutoDock Vina, DOCK, MpSDockZn, PLANTS, and Surflex-Dock to automatically extrapolate BD, BC, and BF poses as well as to perform consistent cluster and DA analyses. Clusterizer and DockAccessor (code available over the Internet) represent two novel tools to collect computationally determined poses and detect the most predictive docking approach. Herein an application to human lysine deacetylase (hKDAC) inhibitors is illustrated.

  • 4.
    Ballante, Flavio
    et al.
    Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Università di Roma, P. le A. Moro 5, 00185, Rome, Italy.
    Ragno, Rino
    Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Università di Roma, P. le A. Moro 5, 00185, Rome, Italy.
    3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications.2012In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 52, no 6, p. 1674-85Article in journal (Refereed)
    Abstract [en]

    Since it first appeared in 1988 3-D QSAR has proved its potential in the field of drug design and activity prediction. Although thousands of citations now exist in 3-D QSAR, its development was rather slow with the majority of new 3-D QSAR applications just extensions of CoMFA. An alternative way to build 3-D QSAR models, based on an evolution of software, has been named 3-D QSAutogrid/R and has been developed to use only software freely available to academics. 3-D QSAutogrid/R covers all the main features of CoMFA and GRID/GOLPE with implementation by multiprobe/multiregion variable selection (MPGRS) that improves the simplification of interpretation of the 3-D QSAR map. The methodology is based on the integration of the molecular interaction fields as calculated by AutoGrid and the R statistical environment that can be easily coupled with many free graphical molecular interfaces such as UCSF-Chimera, AutoDock Tools, JMol, and others. The description of each R package is reported in detail, and, to assess its validity, 3-D QSAutogrid/R has been applied to three molecular data sets of which either CoMFA or GRID/GOLPE models were reported in order to compare the results. 3-D QSAutogrid/R has been used as the core engine to prepare more that 240 3-D QSAR models forming the very first 3-D QSAR server ( www.3d-qsar.com ) with its code freely available through R-Cran distribution.

  • 5.
    Buonfiglio, Rosa
    et al.
    AstraZeneca R&D, Chem Innovat Ctr, Discovery Sci, SE-43183 Molndal, Sweden..
    Engkvist, Ola
    AstraZeneca R&D, Chem Innovat Ctr, Discovery Sci, SE-43183 Molndal, Sweden..
    Varkonyi, Peter
    AstraZeneca R&D, Chem Innovat Ctr, Discovery Sci, SE-43183 Molndal, Sweden..
    Henz, Astrid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Division of Pharmacognosy.
    Vikeved, Elisabet
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Division of Pharmacognosy.
    Backlund, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Division of Pharmacognosy.
    Kogej, Thierry
    AstraZeneca R&D, Chem Innovat Ctr, Discovery Sci, SE-43183 Molndal, Sweden..
    Investigating Pharmacological Similarity by Charting Chemical Space2015In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 55, no 11, p. 2375-2390Article in journal (Refereed)
    Abstract [en]

    In this study, biologically relevant areas of the chemical space were analyzed using ChemGPS-NP. This application enables comparing groups of ligands within a multidimensional space based on principle components derived from physicochemical descriptors. Also, 3D visualization of the ChemGPS-NP global map can be used to conveniently evaluate bioactive compound similarity and visually distinguish between different types or groups of compounds. To further establish ChemGPS-NP as a method to accurately represent the chemical space, a comparison with structure-based fingerprint has been performed. Interesting complementarities between the two descriptions of molecules were observed. It has been shown that the accuracy of describing molecules with physicochemical descriptors like in ChemGPS-NP is similar to the accuracy of structural fingerprints in retrieving bioactive molecules. Lastly, pharmacological similarity of structurally diverse compounds has been investigated in ChemGPS-NP space. These results further strengthen the case of using ChemGPS-NP as a tool to explore and visualize chemical space.

  • 6.
    Caroli, Antonia
    et al.
    Department of Physics, Sapienza Universita ̀ di Roma, P.le Aldo Moro 5, 00185, Roma, Italy.
    Ballante, Flavio
    Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Roma, Italy.
    Wickersham, Richard B
    Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis School of Medicine, 700 South Euclid Avenue, St. Louis, Missouri 63110, United States; Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Roma, Italy .
    Corelli, Federico
    Dipartimento Farmaco Chimico Tecnologico, Universita ̀ degli Studi di Siena, via A. Moro, I-53100 Siena, Italy.
    Ragno, Rino
    Rome Center for Molecular Design, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Roma, Italy.
    Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application.2014In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 54, no 3, p. 970-7Article in journal (Refereed)
    Abstract [en]

    Hsp90 continues to be an important target for pharmaceutical discovery. In this project, virtual screening (VS) for novel Hsp90 inhibitors was performed using a combination of Autodock and Surflex-Sim (LB) scoring functions with the predictive ability of 3-D QSAR models, previously generated with the 3-D QSAutogrid/R procedure. Extensive validation of both structure-based (SB) and ligand-based (LB), through realignments and cross-alignments, allowed the definition of LB and SB alignment rules. The mixed LB/SB protocol was applied to virtually screen potential Hsp90 inhibitors from the NCI Diversity Set composed of 1785 compounds. A selected ensemble of 80 compounds were biologically tested. Among these molecules, preliminary data yielded four derivatives exhibiting IC50 values ranging between 18 and 63 μM as hits for a subsequent medicinal chemistry optimization procedure.

  • 7.
    Friggeri, Laura
    et al.
    Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Ballante, Flavio
    Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Ragno, Rino
    Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Musmuca, Ira
    Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    De Vita, Daniela
    Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Manetti, Fabrizio
    Dipartimento di Biotecnologie, Chimica e Farmacia, Universita ̀ degli Studi di Siena, Via Aldo Moro 2, I-53100 Siena, Italy.
    Biava, Mariangela
    Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Scipione, Luigi
    Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Di Santo, Roberto
    Istituto Pasteur-Fondazione Cenci Bolognetti, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Costi, Roberta
    Istituto Pasteur-Fondazione Cenci Bolognetti, Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Feroci, Marta
    Dipartimento di Scienze di Base e Applicate per l ’ Ingegneria, Sapienza Universita ̀ di Roma, Via Castro Laurenziano 7, I-00161 Rome, Italy.
    Tortorella, Silvano
    Dipartimento di Chimica e Tecnologie del Farmaco, Sapienza Universita ̀ di Roma, P. le A. Moro 5, 00185 Rome, Italy.
    Pharmacophore assessment through 3-D QSAR: evaluation of the predictive ability on new derivatives by the application on a series of antitubercular agents.2013In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 53, no 6, p. 1463-74Article in journal (Refereed)
    Abstract [en]

    Pharmacophoric mapping is a useful procedure to frame, especially when crystallographic receptor structures are unavailable as in ligand-based studies, the hypothetical site of interaction. In this study, 71 pyrrole derivatives active against M. tuberculosis were used to derive through a recent new 3-D QSAR protocol, 3-D QSAutogrid/R, several predictive 3-D QSAR models on compounds aligned by a previously reported pharmacophoric application. A final multiprobe (MP) 3-D QSAR model was then obtained configuring itself as a tool to derive pharmacophoric quantitative models. To stress the applicability of the described models, an external test set of unrelated and newly synthesized series of R-4-amino-3-isoxazolidinone derivatives found to be active at micromolar level against M. tuberculosis was used, and the predicted bioactivities were in good agreement with the experimental values. The 3-D QSAutogrid/R procedure proved to be able to correlate by a single multi-informative scenario the different activity molecular profiles thus confirming its usefulness in the rational drug design approach.

  • 8.
    Jeszenoi, Norbert
    et al.
    Eotvos Lorand Univ, Dept Genet, Pazmany Peter Setany 1-C, H-1117 Budapest, Hungary.;Univ Pecs, Ctr Neurosci, Szentagothai Res Ctr, MTA NAP B Mol Neuroendocrinol Grp,Inst Physiol, Szigeti Ut 12, H-7624 Pecs, Hungary..
    Balint, Monika
    Eotvos Lorand Univ, Dept Biochem, Pazmany Peter Setany 1-C, H-1117 Budapest, Hungary..
    Horvath, Istvan
    Univ Szeged, Chem Doctoral Sch, Dugon Ter 13, H-6720 Szeged, Hungary..
    van der Spoel, David
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
    Hetenyi, Csaba
    Hungarian Acad Sci, MTA ELTE Mol Biophys Res Grp, Pazmany Setany 1-C, H-1117 Budapest, Hungary..
    Exploration of Interfacial Hydration Networks of Target Ligand Complexes2016In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 56, no 1, p. 148-158Article in journal (Refereed)
    Abstract [en]

    Interfacial hydration strongly influences interactions between biomolecules. For example, drug target complexes are often stabilized by hydration networks formed between hydrophilic residues and water molecules at the interface. Exhaustive exploration of hydration networks is, challenging for experimental as well as theoretical methods due to high mobility of participating water molecules. In the present study, we introduced a tool for determination of the complete, void-free hydration structures of molecular interfaces. The tool was applied to 31 complexes including histone proteins, a HIV-1 protease, a G-protein-signaling modulator, and peptide ligands of various lengths. The complexes contained 344 experimentally determined water positions used for validation, and excellent agreement with these was obtained. High-level cooperation between interfacial water molecules was detected by a new approach based on the decomposition of hydration networks into static and dynamic network regions (subnets). Besides providing hydration structures at the atomic level, our results uncovered hitherto hidden networking fundaments of integrity and stability of complex biomolecular interfaces filling an important gap in the toolkit of drug design and structural biochemistry. The presence of continuous, static regions of the interfacial hydration network was found necessary also for stable complexes of histone proteins participating in chromatin assembly and epigenetic regulation.

  • 9.
    Petrović, Dušan
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Biology. Forschungszentrum Jülich, Inst Complex Syst Struct Biochem, Jülich, Germany.
    Bokel, Ansgar
    Heinrich Heine Univ Düsseldorf, Inst Biochem, Düsseldorf, Germany.
    Allan, Matthew
    Forschungszentrum Jülich, Inst Complex Syst Struct Biochem, Jülich, Germany; Penn State Univ, Schreyer Honors Coll, PA USA.
    Urlacher, Vlada B.
    Heinrich Heine Univ Düsseldorf, Inst Biochem, Düsseldorf, Germany.
    Strodel, Birgit
    Forschungszentrum Jülich, Inst Complex Syst Struct Biochem, Jülich, Germany; Heinrich Heine Univ Düsseldorf, Inst Theoret & Computat Chem, Düsseldorf, Germany.
    Simulation-Guided Design of Cytochrome P450 for Chemo- and Regioselective Macrocyclic Oxidation2018In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 58, no 4, p. 848-858Article in journal (Refereed)
    Abstract [en]

    Engineering high chemo-, regio-, and stereoselectivity is a prerequisite for enzyme usage in organic synthesis. Cytochromes P450 can oxidize a broad range of substrates, including macrocycles, which are becoming popular scaffolds for therapeutic agents. However, a large conformational space explored by macrocycles not only reduces the selectivity of oxidation but also impairs computational enzyme design strategies based on docking and molecular dynamics (MD) simulations. We present a novel design workflow that uses enhanced-sampling Hamiltonian replica exchange (HREX) MD and focuses on quantifying the substrate binding for suggesting the mutations to be made. This computational approach is applied to P450 BM3 with the aim to shift regioselectively toward one of the numerous possible positions during beta-cembrenediol oxidation. The predictions are experimentally tested and the resulting product distributions validate our design strategy, as single mutations led up to 5-fold regioselectivity increases. We thus conclude that the HREX-MD-based workflow is a promising tool for the identification of positions for mutagenesis aiming at P450 enzymes with improved regioselectivity.

  • 10.
    Rudling, Axel
    et al.
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, SE-10691 Stockholm, Sweden..
    Orro, Adolfo
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, SE-10691 Stockholm, Sweden..
    Carlsson, Jens
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
    Prediction of Ordered Water Molecules in Protein Binding Sites from Molecular Dynamics Simulations: The Impact of Ligand Binding on Hydration Networks2018In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 58, no 2, p. 350-361Article in journal (Refereed)
    Abstract [en]

    Water plays a major role in ligand binding and is attracting increasing attention in structure-based drug design. Water molecules can make large contributions to binding affinity by bridging protein-ligand interactions or by being displaced upon complex formation, but these phenomena are challenging to model at the molecular level. Herein, networks of ordered water molecules in protein binding sites were analyzed by clustering of molecular dynamics (MD) simulation trajectories. Locations of ordered waters (hydration sites) were first identified from simulations of high resolution crystal structures of 13 protein-ligand complexes. The MD-derived hydration sites reproduced 73% of the binding site water molecules observed in the crystal structures. If the simulations were repeated without the cocrystallized ligands, a majority (58%) of the crystal waters in the binding sites were still predicted. In addition, comparison of the hydration sites obtained from simulations carried out in the absence of ligands to those identified for the complexes revealed that the networks of ordered water molecules were preserved to a large extent, suggesting that the locations of waters in a protein-ligand interface are mainly dictated by the protein. Analysis of >1000 crystal structures showed that hydration sites bridged protein-ligand interactions in complexes with different ligands, and those with high MD-derived occupancies were more likely to correspond to experimentally observed ordered water molecules. The results demonstrate that ordered water molecules relevant for modeling of protein-ligand complexes can be identified from MD simulations. Our findings could contribute to development of improved methods for structure-based virtual screening and lead optimization.

  • 11.
    Silvestri, Laura
    et al.
    Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina.
    Ballante, Flavio
    Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina.
    Mai, Antonello
    Istituto Pasteur - Fondazione Cenci Bolognetti Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina.
    Marshall, Garland R
    Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina; Visiting Professor from the Department of Biochemistry and Molecular Biophysics, Wash ington University School of Medicine, St. Louis, Missouri 63110, United States.
    Ragno, Rino
    Rome Center for Molecular Design Dipartimento di Chimica e Tecnologie del Farmaco, Facolta ̀ di Farmacia e Medicina.
    Histone deacetylase inhibitors: structure-based modeling and isoform-selectivity prediction.2012In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 52, no 8, p. 2215-35Article in journal (Refereed)
    Abstract [en]

    An enhanced version of comparative binding energy (COMBINE) analysis, named COMBINEr, based on both ligand-based and structure-based alignments has been used to build several 3-D QSAR models for the eleven human zinc-based histone deacetylases (HDACs). When faced with an abundance of data from diverse structure-activity sources, choosing the best paradigm for an integrative analysis is difficult. A common example from studies on enzyme-inhibitors is the abundance of crystal structures characterized by diverse ligands complexed with different enzyme isoforms. A novel comprehensive tool for data mining on such inhomogeneous set of structure-activity data was developed based on the original approach of Ortiz, Gago, and Wade, and applied to predict HDAC inhibitors' isoform selectivity. The COMBINEr approach (apart from the AMBER programs) has been developed to use only software freely available to academics.

  • 12.
    Svensson, Fredrik
    et al.
    Univ Cambridge, Ctr Mol Informat, Dept Chem, Lensfield Rd, Cambridge CB2 1EW, England; IOTA Pharmaceut, St Johns Innovat Ctr, Cowley Rd, Cambridge CB4 0WS, England.
    Aniceto, Natalia
    Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K..
    Norinder, Ulf
    Swetox, Unit of Toxicology Sciences, Karolinska Institutet, Forskargatan 20, SE-151 36 Södertälje, Sweden; Department of Computer and Systems Sciences , Stockholm University, Box 7003, SE-164 07 Kista, Sweden.
    Cortes-Ciriano, Isidro
    Centre for Molecular Informatics, Department of Chemistry , University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K..
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carlsson, Lars
    Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, SE-43183, Mölndal, Sweden; Department of Computer Science, Royal Holloway, University of London, Egham Hill, Surrey, U.K..
    Bender, Andreas
    Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K..
    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.

  • 13.
    Zhang, Haiyang
    et al.
    Univ Sci & Technol Beijing, Sch Chem & Biol Engn, Dept Biol Sci & Engn, Beijing 100083, Peoples R China.
    Yin, Chunhua
    Univ Sci & Technol Beijing, Sch Chem & Biol Engn, Dept Biol Sci & Engn, Beijing 100083, Peoples R China.
    Jiang, Yang
    Beijing Univ Chem Technol, Coll Life Sci & Technol, Beijing Key Lab Bioproc, Box 53, Beijing 100029, Peoples R China.
    van der Spoel, David
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Force Field Benchmark of Amino Acids: I. Hydration and Diffusion in Different Water Models2018In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 58, no 5, p. 1037-1052Article in journal (Refereed)
    Abstract [en]

    Thermodynamic and kinetic properties are of critical importance for the applicability of computational models to biomolecules such as proteins. Here we present an extensive evaluation of the Amber ff99SB-ILDN force field for modeling of hydration and diffusion of amino acids with three-site (SPC, SPC/E, SPC/E-b , and TIP3P), four-site (TIP4P, TIP4P-Ew, and TIP4P/2005), and five-site (TIPSP and TIP5P-Ew) water models. Hydration free energies (HFEs) of neutral amino acid side chain analogues have little dependence on the water model, with a root-mean-square error (RMSE) of similar to 1 kcal/mol from experimental observations. On the basis of the number of interacting sites in the water model, HFEs of charged side chains can be putatively classified into three groups, of which the group of three-site models lies between those of four- and five-site water models; for each group, the water model dependence is greatly eliminated when the solvent Galvani potential is considered. Some discrepancies in the location of the first hydration peak (R-RDF) in the ion-water radial distribution function between experimental and calculated observations were detected, such as a systematic underestimation of the acetate (Asp side chain) ion. The RMSE of calculated diffusion coefficients of amino acids from experiment increases linearly with the increasing diffusion coefficients of the solvent water models at infinite dilution. TIP3P has the fastest diffusivity, in line with literature findings, while the "FB" and "OPC" water model families as well as TIP4P/2005 perform well, within a relative error of 5%, and TIP4P/2005 yields the most accurate estimate for the water diffusion coefficient. All of the tested water models overestimate amino acid diffusion coefficients by approximately 40% (TIP4P/2005) to 200% (TIP3P). Scaling of protein-water interactions with TIP4P/2005 in the Amber ff99SBws and ff03ws force fields leads to more negative HFEs but has little influence on the diffusion of amino acids. The most recent FF/water combinations of ff14SB/OPC3, ffl5ipq/SPC/E-b, and fb15/TIP3P-FB do not show obvious improvements in accuracy for the tested quantities. These findings here establish a benchmark that may aid in the development and improvement of classical force fields to accurately model protein dynamics and thermodynamics.

  • 14.
    Zhang, Haiyang
    et al.
    Univ Sci & Technol Beijing, Sch Chem & Biol Engn, Dept Biol Sci & Engn, Beijing 100083, Peoples R China..
    Yin, Chunhua
    Univ Sci & Technol Beijing, Sch Chem & Biol Engn, Dept Biol Sci & Engn, Beijing 100083, Peoples R China..
    Yan, Hai
    Univ Sci & Technol Beijing, Sch Chem & Biol Engn, Dept Biol Sci & Engn, Beijing 100083, Peoples R China..
    van der Spoel, David
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Evaluation of Generalized Born Models for Large Scale Affinity Prediction of Cyclodextrin Host-Guest Complexes2016In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 56, no 10, p. 2080-2092Article in journal (Refereed)
    Abstract [en]

    Binding affinity prediction with implicit solvent models remains a challenge in virtual screening for drug discovery. In order to assess the predictive power of implicit solvent models in docking techniques with Amber scoring, three generalized Born models (GB(HCT), GB(OBC)I, and GB(OBC)II) available in Dock 6.7 were utilized, for determining the binding affinity of a large set of beta-cydodextrin complexes with 75 neutral guest molecules. The results were compared to potential of mean force (PMF) free energy calculations with four GB models (GB(Still), GB(HCT), GB(OBC)I, and GB(OBC)II and to experimental data. Docking results yield similar accuracy to the computationally demanding PMF method with umbrella sampling. Neither docking nor PMF calculations reproduce the experimental binding affinities, however, as indicated by a small Spearman rank order coefficient (similar to 0.5). The binding energies obtained from GB models were decomposed further into individual contributions of the binding partners and solvent environments and compared to explicit solvent simulations for five complexes allowing for rationalizing the difference between explicit and implicit solvent models. An important observation is that the explicit solvent screens the interaction between host and guest much stronger than GB models. In contrast, the screening in GB models is too strong in solutes, leading to overestimation of short-range interactions and too strong binding. It is difficult to envision a way of overcoming these two opposite effects.

  • 15.
    Zhou, Yang
    et al.
    AlbaNova Univ Ctr, KTH Royal Inst Technol, Dept Theoret Chem & Biol, S-10691 Stockholm, Sweden.
    Zou, Rongfeng
    AlbaNova Univ Ctr, KTH Royal Inst Technol, Dept Theoret Chem & Biol, S-10691 Stockholm, Sweden.
    Kuang, Guanglin
    AlbaNova Univ Ctr, KTH Royal Inst Technol, Dept Theoret Chem & Biol, S-10691 Stockholm, Sweden.
    Långström, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Organic Chemistry.
    Halldin, Christer
    Karolinska Inst, Ctr Psychiat Res, Dept Clin Neurosci, S-17176 Stockholm, Sweden;Stockholm Cty Council, S-17176 Stockholm, Sweden.
    Ågren, Hans
    AlbaNova Univ Ctr, KTH Royal Inst Technol, Dept Theoret Chem & Biol, S-10691 Stockholm, Sweden;Henan Univ, Coll Chem & Chem Engn, Kaifeng 475004, Henan, Peoples R China.
    Tu, Yaoquan
    AlbaNova Univ Ctr, KTH Royal Inst Technol, Dept Theoret Chem & Biol, S-10691 Stockholm, Sweden.
    Enhanced Sampling Simulations of Ligand Unbinding Kinetics Controlled by Protein Conformational Changes2019In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, no 9, p. 3910-3918Article in journal (Refereed)
    Abstract [en]

    Understanding unbinding kinetics of protein-ligand systems is of great importance for the design of ligands with desired specificity and safety. In recent years, enhanced sampling techniques have emerged as effective tools for studying unbinding kinetics of protein-ligand systems at the atomistic level. However, in many protein-ligand systems, the ligand unbinding processes are strongly coupled to protein conformational changes and the disclosure of the hidden degrees of freedom closely related to the protein conformational changes so that sampling is enhanced over these degrees of freedom remains a great challenge. Here, we show how potential-scaled molecular dynamics (sMD) and infrequent metadynamics (InMetaD) simulation techniques can be combined to successfully reveal the unbinding mechanism of 3-(1,4-diazabicyclo[3.2.2]nonan-4-yl)-6-[F-18]fluorodibenzo[b,d]thiophene 5,5-dioxide ([F-18]ASEM) from a chimera structure of the alpha 7-nicotinic acetylcholine receptor. By using sMD simulations, we disclosed that the "close to "open" conformational change of loop C plays a key role in the ASEM unbinding process. By carrying out InMetaD simulations with this conformational change taken into account as an additional collective variable, we further captured the key states in the unbinding process and clarified the unbinding mechanism of ASEM from the protein. Our work indicates that combining sMD and InMetaD simulation techniques can be an effective approach for revealing the unbinding mechanism of a protein-ligand system where protein conformational changes control the unbinding process.

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