Presentation made at PepTalk 2011 in San Diego on Jan. 13, 2011. The emphasis is on computational methods to explore global and local structure similarities in determining the possible promiscuity of drugs to bind to multiple protein receptors.
A keynote presentation at the NBIC Annual Meeting. Covers the concept of polypharmacology, a bioinformatics approach to off-target binding and a systems approach to dynamical modeling of the process.
Presentation on March 12, 2011 at the Skaggs School of Pharmacy and Pharmaceutical Sciences (UCSD) during the Workshop in Allosteric and Orthosteric Ligands in Drug Action
Presentation made at PepTalk 2011 in San Diego on Jan. 13, 2011. The emphasis is on computational methods to explore global and local structure similarities in determining the possible promiscuity of drugs to bind to multiple protein receptors.
A keynote presentation at the NBIC Annual Meeting. Covers the concept of polypharmacology, a bioinformatics approach to off-target binding and a systems approach to dynamical modeling of the process.
Presentation on March 12, 2011 at the Skaggs School of Pharmacy and Pharmaceutical Sciences (UCSD) during the Workshop in Allosteric and Orthosteric Ligands in Drug Action
At Worldwide Power Products, we specialize in power generation equipment including over 100 new and used engines and generator sets for the industrial, marine, and petroleum industries. Brands include Caterpillar, Cummins, Multiquip, Olympian, Waukesha, Detroit Diesel, Onan, among many others.
At Worldwide Power Products, we specialize in power generation equipment including over 100 new and used engines and generator sets for the industrial, marine, and petroleum industries. Brands include Caterpillar, Cummins, Multiquip, Olympian, Waukesha, Detroit Diesel, Onan, among many others.
Systems Pharmacology as a tool for future therapy development: a feasibility ...Guide to PHARMACOLOGY
Systems pharmacology has the potential to facilitate a novel range of medical interventions. Databases such as the IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb, www.guidetopharmacology.org) provide information on drugs and their pharmacological effects. Combining these resources with understanding of biological systems gives us the opportunity to predict, model and quantify the effects of drug administration on whole systems. We can also ask how multiple drugs can be used together in new types of therapies that outperform conventional single target therapies.
Here, we explore the feasibility of undertaking a systems pharmacology analysis of the mevalonate branch of the cholesterol biosynthesis pathway.
Presented by Joanna Sharman at ISMB/ECCB 2015 in Dublin
Cardiotoxicity is unfortunately a common side effect of many modern chemotherapeutic agents. The mechanisms that underlie these detrimental effects on heart muscle, however, remain unclear. The Drug Toxicity Signature Generation Center at ISMMS aims to address this unresolved issue by providing a bridge between molecular changes in cells and the prediction of pathophysiological effects. I will discuss ongoing work in which we use next-generation sequencing to quantify changes in gene expression that occur in cardiac myocytes after they are treated with potentially toxic chemotherapeutic agents. I will focus in particular on the computational pipeline we are developing that integrates sophisticated sequence alignment, statistical and network analysis, and dynamical mathematical models to develop novel predictions about the mechanisms underlying drug-induced cardiotoxicity.
Jaehee Shim is a Ph.D candidate in the Biophysics and Systems Pharmacology Program at Icahn School of Medicine at Mount Sinai (ISMMS). As a part of her Ph.D. studies, she is building dynamical prediction models based on analysis of gene expression data generated by the Drug Toxicity Signature Generation Center at ISMMS. She received her B.S in Biochemistry from the University of Michigan-Dearborn. Prior to starting her Ph.D, Jaehee worked at the ISMMS Genomics Core with a team of senior scientists and gained experience in improving and troubleshooting RNA sequencing protocols using Next Generation Sequencing Platforms.
Next Generation Data and Opportunities for Clinical PharmacologistsPhilip Bourne
Presentation at the Pre-meeting Workshop Next-Generation Clinical Pharmacology: Integrating Systems Pharmacology, Data-Driven Therapeutics, and Personalized Medicine. American Society for Clinical Pharmacology and Therapeutics Annual Meeting Atlanta GA March 18, 2014.
Exploiting Edinburgh's Guide to PHARMACOLOGY database as a source of protein ...Chris Southan
Presented by Jamie Davies at the SULSA Synthetic Biology Meeting, Edinburgh, 10 June 2014
http://www.eventbrite.co.uk/e/sulsa-synthetic-biology-meeting-registration-11251454403?aff=eorg
Abstract: Synthetic creation of new biological systems typically incorporates pathways and signaling modules from known protein building blocks. Testing the models underpinning the synthetic engineering thus needs the experimental manipulation of individual proteins, for example, ablating a specific enzyme activity via RNAi, SNP mutation, or knockout. However, the option of small-molecule inhibition as the system perturbation has the advantages of 1) rapid onset 2) dose-response 3) analog testing for structure-activity relationships, 4) exploring mixtures for combinatorial effects 5) pulsing and reversal by wash-out. 6) accurate measurements of added substances and 7) a vast precedent of published results in natural systems from medicinal chemistry, pharmacology, and chemical biology. For the synthetic biologists the GToPdb1 can thus be considered as compendium of the latter. It encompasses an interaction matrix between ~4000 small molecules and ~1000 human proteins with a focus on drugs, clinical candidates, research compounds and peptide ligands These not only have ~ 10,000 mapped binding constants but also the spectrum of documented modulation extends across enzymes, receptors, channels and transporters. It thus becomes an increasingly plausible option to choose a “Lego protein” from GToPdb as a synthetic system component that can have experimentally useable activity probes available from chemical vendors. Even if it does not currently have a suitable target-probe pair, as knowledge base (and expertise resource via the curation team who populate it) GToPdb is an ideal starting point from which to walk out to wider chemogenomic spaces. For example, while an approved drug and its target might seem a logical choice, analogs from the lead series or different chemotypes from which the drug was optimized, or even failed in development, can have superior probe-like properties for in vitro experiments (e.g. be more potent, specific and soluble). The GToPdb facilitates access to such compound data via curated papers and patents.
References
1. Pawson AJ, Sharman JL, Benson HE, Faccenda E, Alexander SP, Buneman OP, Davenport AP, McGrath JC, Peters JA, Southan C, Spedding M, Yu W, Harmar AJ; NC-IUPHAR. The IUPHAR/BPS Guide to PHARMACOLOGY: an expert-driven knowledgebase of drug targets and their ligands. Nucleic Acids Res. 2014 Jan 1;42(1)
QIVIVE extrapolation requires a precise correlation between exposure and the effective chemical concentration at the site where the MIE occurs.
This work demonstrates that intracellular distribution is not ruled only by physical-chemical parameters, rather it is mainly regulated by specific biological-mediated mechanisms. Substances with
apparent chemical similarity may show different distribution profile, as shown by the intra-nuclear distribution of polyphenols. While our results derive from a limited number of substances applied to
one cell line, it is plausible that using different substances and/or different cell lines would also have shown that intracellular distribution is not directly related to physical-chemical parameters.
Chemical uptake should be specifically measured and simple extrapolations based on physical-chemical properties may provide misleading decision
Drug-induced liver injury (DILI) is one of the most important reasons for drug development failure at both pre-approval and post-approval stages. There has been increased interest in developing predictive in vivo, in vitro and in silico models to identify compounds that cause idiosyncratic hepatotoxicity. In the current study we applied machine learning, Bayesian modeling method with extended connectivity fingerprints and other interpretable descriptors. The model that was developed and internally validated (using a training set of 295 compounds) was then applied to a large test set relative to the training set (237 compounds) for external validation. The resulting concordance of 60%, sensitivity of 56%, and specificity of 67% were comparable to internal validation. The Bayesian model with ECFC_6 fingerprint and interpretable descriptors suggested several substructures that are chemically reactive and may also be important for DILI-causing compounds, e.g. ketones, diols and -methyl styrene type structures. Using SMARTS filters published by several pharmaceutical companies we evaluated whether such reactive substructures could be readily detected by any of the published filters. It was apparent that the most stringent filters used in this study, like the Abbott alerts which captures thiol traps and other compounds, may be of utility in identifying DILI-causing compounds (sensitivity 67%). A significant outcome of the present study is that we provide predictions for many compounds that cause DILI by using the knowledge we have available from previous studies for computational approaches. These computational models may represent a cost effective selection criteria prior to costly in vitro or in vivo experimental studies.
Presented online as part of the NASM series in Advancing Drug Discovery see https://www.nationalacademies.org/event/40883_09-2023_advancing-drug-discovery-data-science-meets-drug-discovery
For a panel discussion at the Associate Research Libraries Spring meeting April 27, 2022, Montreal https://www.arl.org/schedule-for-spring-2022-association-meeting/
Frontiers of Computing at the Cellular and Molecular ScalesPhilip Bourne
3 basic points when establishing a new biomedical initiative. Presented at Frontiers of Computing in Health and Society, George Mason University, September 21, 2021.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
1. Polypharmacology Studied Using Structural Bioinformatics and Systems Biology Philip E. Bourne University of California San Diego [email_address] http://www.sdsc.edu/pb UCL – December 08, 2010
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10. Need to Start with a 3D Drug-Receptor Complex - The PDB Contains Many Examples Computational Methodology Generic Name Other Name Treatment PDBid Lipitor Atorvastatin High cholesterol 1HWK, 1HW8… Testosterone Testosterone Osteoporosis 1AFS, 1I9J .. Taxol Paclitaxel Cancer 1JFF, 2HXF, 2HXH Viagra Sildenafil citrate ED, pulmonary arterial hypertension 1TBF, 1UDT, 1XOS.. Digoxin Lanoxin Congestive heart failure 1IGJ
13. A Reverse Engineering Approach to Drug Discovery Across Gene Families Characterize ligand binding site of primary target (Geometric Potential) Identify off-targets by ligand binding site similarity (Sequence order independent profile-profile alignment) Extract known drugs or inhibitors of the primary and/or off-targets Search for similar small molecules Dock molecules to both primary and off-targets Statistics analysis of docking score correlations … Computational Methodology Xie and Bourne 2009 Bioinformatics 25(12) 305-312
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20. Adverse Effects of SERMs cardiac abnormalities thromboembolic disorders ocular toxicities loss of calcium homeostatis ????? Side Effects - The Tamoxifen Story PLoS Comp. Biol. , 2007 3(11) e217
29. binding site comparison protein ligand docking MD simulation & MM/GBSA Binding free energy calculation structural proteome off-target? network construction & mapping drug target Clinical Outcomes 1OHR Possible Nelfinavir Repositioning
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32. Distribution of Top Hits on the Human Kinome p-value < 1.0e-3 p-value < 1.0e-4 Manning et al., Science , 2002, V298, 1912 Possible Nelfinavir Repositioning
33. Interactions between Inhibitors and Epidermal Growth Factor Receptor (EGFR) – 74% of binding site resides are comparable 1. Hydrogen bond with main chain amide of Met793 (without it 3700 fold loss of inhibition) 2. Hydrophobic interactions of aniline/phenyl with gatekeeper Thr790 and other residues H-bond: Met793 with quinazoline N1 H-bond: Met793 with benzamide hydroxy O38 EGFR-DJK Co-crys ligand EGFR-Nelfinavir DJK = N-[4-(3-BROMO-PHENYLAMINO)-QUINAZOLIN-6-YL]-ACRYLAMIDE
35. Off-target Interaction Network Identified off-target Intermediate protein Pathway Cellular effect Activation Inhibition Possible Nelfinavir Repositioning
36. Inhibition rate of Nelfinavir on EGFR, ErbB2, ErbB4, Akt1, Akt2 Akt3 HTRF® TranscreenerTM ADP Assay is performed for Nelfinavir on 20 μ M by GenScript Results are inconclusive Non-specific aggregation problem? Possible Nelfinavir Repositioning
37. Other Experimental Evidence to Show Nelfinavir inhibition on EGFR, IGF1R, CDK2 and Abl is Supportive The inhibitions of Nelfinavir on IGF1R, EGFR, Akt activity were detected by immunoblotting. The inhibition of Nelfinavir on Akt activity is less than a known PI3K inhibitor Joell J. Gills et al. Clinic Cancer Research September 2007 13; 5183 Nelfinavir inhibits growth of human melanoma cells by induction of cell cycle arrest Nelfinavir induces G1 arrest through inhibition of CDK2 activity. Such inhibition is not caused by inhibition of Akt signaling. Jiang W el al. Cancer Res. 2007 67(3) BCR-ABL is a constitutively activated tyrosine kinase that causes chronic myeloid leukemia (CML) Druker, B.J., et al New England Journal of Medicine, 2001. 344 (14): p. 1031-1037 Nelfinavir can induce apoptosis in leukemia cells as a single agent Bruning, A., et al. , Molecular Cancer, 2010. 9 :19 Nelfinavir may inhibit BCR-ABL Possible Nelfinavir Repositioning
44. Map 2 onto 1 – The TB-Drugome http://funsite.sdsc.edu/drugome/TB/ Similarities between the binding sites of M.tb proteins (blue), and binding sites containing approved drugs (red).
49. Drug Failure - The Torcetrapib Story PLoS Comp Biol 2009 5(5) e1000387
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51. Computational Evaluation of Drug Off-Target Effects 336 genes 1587 reactions Plos Comp. Biol. 2010 6(9): e1000938 Proteome Drug binding site alignments SMAP Predicted drug targets Drug and endogenous substrate binding site analysis Competitively inhibitable targets Inhibition simulations in context-specific model COBRA Toolbox Predicted causal targets and genetic risk factors Metabolic network Scientific literature Tissue and biofluid localization data Gene expression data Physiological objectives System exchange constraints Flux states optimizing objective Physiological context-specific model Influx Efflux Drug response phenotypes Drug targets Physiological objectives Causal drug targets All targets
Absorption, distribution, metabolism and excretion
Updated for 2009
P distance to environmental boundary; Pi Di and alphai D distance to central atom alpha direction to central atom
This is great data!
3,996 proteins in TB proteome 749 solved structures in the PDB, representing a total of 284 proteins (7.2% coverage) ModBase contains homology models for entire TB proteome 1,446 ‘high quality’ homology models were added to the data set Structural coverage increased to 43.8% Retained only those models with a model score of > 0.7 and a Modpipe quality score of > 1.1 (2818 models). There were multiple models per protein. For each TB protein, chose the model with the best model score, and if they were equal, chose the model with the best Modpipe quality score (1703 models). However, 251 (+6) models were removed since they correspond to TB proteins that already have solved structures. 1446 models remained) Score for the reliability of a Model, derived from statistical potentials (F. Melo, R. Sanchez, A. Sali,2001 PDF ). A model is predicted to be good when the model score is higher than a pre-specified cutoff (0.7). A reliable model has a probability of the correct fold that is larger than 95%. A fold is correct when at least 30% of its Calpha atoms superpose within 3.5A of their correct positions. The ModPipe Protein Quality Score is a composite score comprising sequence identity to the template, coverage , and the three individual scores evalue , z-Dope and GA341 . We consider a MPQS of >1.1 as reliable
(nutraceuticals excluded)
Multi-target therapy may be more effective than single-target therapy to treat infectious diseases Most of the proteins listed are potential novel drug targets for the development of efficient anti-tuberculosis chemotherapeutics. GSMN-TB : Genome Scale Metabolic Reaction Network of M.tb (http://sysbio/sbs.surrey.ac.uk/tb) 849 reactions, 739 metabolites, 726 genes Can optimize the model for in vivo growth Carry out multiple gene inhibition and compute the maximal theoretical growth rate (if close to zero, that combination of genes is essential for growth)