Www.hp-see.eu HP-SEE In the search of the HDAC-1 inhibitors. The preliminary results of ligand based virtual screening Ilija N. Cvijetić, Ivan O. Juranić,

Slides:



Advertisements
Similar presentations
Shape and Color Clustering with SAESAR Norah E. MacCuish, John D. MacCuish, and Mitch Chapman Mesa Analytics & Computing, Inc.
Advertisements

Design of high-content and focused libraries to improve the development of new active compounds in the framework of the rational drug discovery Design.
Cell Communication Cells need to communicate with one another, whether they are located close to each other or far apart. Extracellular signaling molecules.
Case Study: Dopamine D 3 Receptor Anthagonists Chapter 3 – Molecular Modeling 1.
Jürgen Sühnel Institute of Molecular Biotechnology, Jena Centre for Bioinformatics Jena / Germany Supplementary Material:
1 PharmID: A New Algorithm for Pharmacophore Identification Stan Young Jun Feng and Ashish Sanil NISSMPDM 3 June 2005.
Using Metacomputing Tools to Facilitate Large Scale Analyses of Biological Databases Vinay D. Shet CMSC 838 Presentation Authors: Allison Waugh, Glenn.
M. Wagener 3D Database Searching and Scaffold Hopping Markus Wagener NV Organon.
In-silico screening without structural comparisons: Peptides to non-peptides in one step Maybridge Workshop Oct ‘03 Bregenz Austria.
BL5203: Molecular Recognition & Interaction Lecture 5: Drug Design Methods Ligand-Protein Docking (Part I) Prof. Chen Yu Zong Tel:
Pharmacophore-based Molecular Docking Bert E. Thomas, Diane Joseph- McCarthy, Juan C.Avarez.
 MicroRNAs (miRNAs) are a class of small RNA molecules, about ~21 nucleotide (nt) long.  MicroRNA are small non coding RNAs (ncRNAs) that regulate.
RAPID: Randomized Pharmacophore Identification for Drug Design PW Finn, LE Kavraki, JC Latombe, R Motwani, C Shelton, S Venkatasubramanian, A Yao Presented.
Pharmacophore and FTrees
Computational Techniques in Support of Drug Discovery October 2, 2002 Jeffrey Wolbach, Ph. D.
Module 2: Structure Based Ph4 Design
Molecular docking and QSAR analysis: a combined approach applied to FTase inhibitors and  1a -AR antagonists Università degli Studi di Milano Giulio Vistoli,
ClusPro: an automated docking and discrimination method for the prediction of protein complexes Stephen R. Comeau, David W.Gatchell, Sandor Vajda, and.
Topological Summaries: Using Graphs for Chemical Searching and Mining Graphs are a flexible & unifying model Scalable similarity searches through novel.
Advanced Cancer Topics Journal Review 4/16/2009 AD.
FKPPL workshop May 2012 BUI The Quang Prof. Vincent Breton Prof. Doman Kim Prof. NGUYEN Hong Quang Prof. PHAM Quoc Long Grid enabled in silico drug discovery.
Introduction to Chemoinformatics Irene Kouskoumvekaki Associate Professor December 12th, 2012 Biological Sequence Analysis course.
 Dr. Steven Grant  Dr. Roberto Rosato  Jorge Almenara  Stefanie Coe  Dr. Allison Johnson, HHMI Coordinator.
Afsha Rais.  In chromatins, DNA is wrapped around proteins of which most are histones.  Histones assist in DNA packaging and have a regulatory role.
PHC 222 Medicinal Chemistry-1- Part(I) Dr. Huda Al Salem Lecture (1)
Know More Before You Score: An Analysis of Structure-Based Virtual Screening Protocols ä Structure-Based Virtual Screening (SBVS) is a proven technique.
Function first: a powerful approach to post-genomic drug discovery Stephen F. Betz, Susan M. Baxter and Jacquelyn S. Fetrow GeneFormatics Presented by.
SIRT1 (Sirtuin 1) A mamalian NAD+-dependent hitone deacetylase (HDAC) It deacetylates tumor suppressor.
In silico discovery of inhibitors using structure-based approaches Jasmita Gill Structural and Computational Biology Group, ICGEB, New Delhi Nov 2005.
Università degli Studi di Milano Dipartimento di Scienze Farmaceutiche “Pietro Pratesi” Alessandro Pedretti GriDock: An MPI-based software for virtual.
Ligand-based drug discovery No a priori knowledge of the receptor What information can we get from a few active compounds.
SimBioSys Inc.© Slide #1 Enrichment and cross-validation studies of the eHiTS high throughput screening software package.
Virtual Screening C371 Fall INTRODUCTION Virtual screening – Computational or in silico analog of biological screening –Score, rank, and/or filter.
Bioinformatics MEDC601 Lecture by Brad Windle Ph# Office: Massey Cancer Center, Goodwin Labs Room 319 Web site for lecture:
Hierarchical Database Screenings for HIV-1 Reverse Transcriptase Using a Pharmacophore Model, Rigid Docking, Solvation Docking, and MM-PB/SA Junmei Wang,
Epigenetic Processes from a Molecular Perspective INBRE Meeting 2/16/10.
Computer-aided drug discovery (CADD)/design methods have played a major role in the development of therapeutically important small molecules for several.
Role of Enzymes. 1. Cells are possibly the smallest chemical factories in the world. They build chemical compounds (anabolism) from raw materials and.
Introduction to Chemoinformatics and Drug Discovery Irene Kouskoumvekaki Associate Professor February 15 th, 2013.
GRIDP: Web-enabled Drug Discovery Is there any way I can use computational tools to reduce the number of molecules I have to screen to a manageable number,
Surflex: Fully Automatic Flexible Molecular Docking Using a Molecular Similarity-Based Search Engine Ajay N. Jain UCSF Cancer Research Institute and Comprehensive.
Chap. 3 Problem 1 See Fig. 3.1a & 3.2 for basic information about structure classifications. More on the definitions of primary, secondary, tertiary, and.
Identification of structurally diverse Growth Hormone Secretagogue (GHS) agonists by virtual screening and structure-activity relationship analysis of.
Molecular mechanics Classical physics, treats atoms as spheres Calculations are rapid, even for large molecules Useful for studying conformations Cannot.
EBI is an Outstation of the European Molecular Biology Laboratory. A web based integrated search service to understand ligand binding and secondary structure.
Elon Yariv Graduate student in Prof. Nir Ben-Tal’s lab Department of Biochemistry and Molecular Biology, Tel Aviv University.
Improving compound–protein interaction prediction by building up highly credible negative samples Toward more realistic drug-target interaction predictions.
Docking and Virtual Screening Using the BMI cluster
Molecular Modeling in Drug Discovery: an Overview
Nehad A. El Sayed, Amal A. H. Eissa, Reem K. Arafa and Ghada F. El Masry* Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Cairo University.
Structural Bioinformatics Elodie Laine Master BIM-BMC Semester 3, Genomics of Microorganisms, UMR 7238, CNRS-UPMC e-documents:
Jump to first page Relational Data. Jump to first page Inductive Logic Programming (ILP) n Can use ILP to find a set of rules capturing a property that.
Page 1 Computer-aided Drug Design —Profacgen. Page 2 The most fundamental goal in the drug design process is to determine whether a given compound will.
School of Pharmacy, Sungkyunkwan University Fri. seminar.
Dhanvantari GenOME to hit
OpenEye Scientific Software
Simplified picture of the principles used for multiple copy simultaneous search (MCSS) and for computational combinatorial ligand design (CCLD). Simplified.
Approaching the mechanism of anticancer activity of a copper(II) complex through molecular modelling, docking and dynamic studies. I.N. Zoi1 , A.X. Lygeros1.
APPLICATIONS OF BIOINFORMATICS IN DRUG DISCOVERY
DATA MINING FOR SMALL MOLECULE ALLOSTERIC INHIBITORS
Ligand-Based Structural Hypotheses for Virtual Screening
Molecular Docking Profacgen. The interactions between proteins and other molecules play important roles in various biological processes, including gene.
Virtual Screening.
Multidimensional Drug Profiling By Automated Microscopy
Reporter: Yu Lun Kuo (D )
William J. Zuercher, Jonathan M. Elkins, Stefan Knapp 
Epigenetics modification
Deacetylase Enzymes Chemistry & Biology
Know More Before You Score: An Analysis of Structure-Based Virtual Screening Protocols Structure-Based Virtual Screening (SBVS) is a proven technique for.
Peter Man-Un Ung, Rayees Rahman, Avner Schlessinger 
Presentation transcript:

HP-SEE In the search of the HDAC-1 inhibitors. The preliminary results of ligand based virtual screening Ilija N. Cvijetić, Ivan O. Juranić, Alessandro Pedretti, Giulio Vistoli, Branko J. Drakulić The HP-SEE initiative is co-funded by the European Commission under the FP7 Research Infrastructures contract no

Methodology - Shape and electrostatics play crucial role in ligand-receptor interactions - ROCS compares the molecules by the shape and pharmacophoric similarity - EON compares molecules by electrostatic similarity HP-SEE User Meeting – Belgrade * - The OpenEye* applications are fully functional on our home cluster PARADOX - Part of the source codes were made for the ligand-based virtual screening - Installed applications are industry standards - OMEGA generate conformers

Virtual Screening HP-SEE User Meeting – Belgrade Majority of those compounds are available - Potentially, any can act on some of biological targets - One of the methods for the selection of compounds that could act on chosen target is virtual screening - Significantly faster and cheaper, comparing to biological tests - At the end of this year 70 th million small molecule should be added to Chemical abstract database *

Virtual Screening HP-SEE User Meeting – Belgrade Ligand based – significantly faster - Structure based – known structure of the receptor - Why structurally dissimilar compounds act on same receptors?

Virtual Screening HP-SEE User Meeting – Belgrade

Background HP-SEE User Meeting – Belgrade Inhibition of histone deacetylases (HDACs) elicits anticancer effects in several tumor cells by inhibition of cell growth and inducing cell differentiation - Acetylation and deacetylation of proteins are important regulatory mechanisms of cellular events - Histone deacetylases (HDACs) are involved in acetylation of lysine residues of histone and non-histone proteins

Background HP-SEE User Meeting – Belgrade Classification of histone deacetylases: Class I - HDAC1,HDAC2, HDAC3, and HDAC8 - Class I and II HDACs are overexpressed in ovarian (HDAC 1- 3), lung (HDAC 1 and 3) and gastric cancer (HDAC2); Class I and II HDAC inhibitors have been approved as anti- cancer therapeutics Class II - HDAC4, HDAC5, HDAC6, HDAC7, HDAC9, HDAC10 Class IV – HDAC 11 Class III is a series of the NAD + -dependent Sir2 family of enzymes - Class I and II and IV HDACs are Zn 2+ - containing hydrolases; class III are NAD + dependent

Binding site HP-SEE User Meeting – Belgrade HDAC 2 PDB code: 3MAX

Binding site HP-SEE User Meeting – Belgrade Hydrophobic Hydrophilic

Binding site HP-SEE User Meeting – Belgrade Hydrophobic Hydrophilic

Template and database HP-SEE User Meeting – Belgrade Ligand from the PDB entry 3MAX - Database: ChemBank – comprise 2346 molecules - After the filtering 1990 molecules - Significantly larger databases available – up to few million of compounds

Results HP-SEE User Meeting – Belgrade molecules from database were submitted to OMEGA ~ conformations were generated -Pharmacophoric and shape similarity was searched with the ROCS program hits were selected according to the Tanimoto index - For the objects A and B, Tanimoto index represents the ratio of common elements and the all elements in both objects - Multiconformer set was compared against the template

Results – 13 HP-SEE User Meeting – Belgrade Shape Tanimoto score Color Tanimoto score Tanimoto Combo score - Color force field – Implicit MillsDean and Explicit MillsDean - Color force-fields include 6 pharmacophoric features: - Implicit MillsDean – includes protonation state at pH = 7 Hydrogen-bond donors, hydrogen-bond acceptors, hydrophobes, anions, cations, and rings Scoring

Results – 14 HP-SEE User Meeting – Belgrade Color force field of the template ligand

Results – 15 HP-SEE User Meeting – Belgrade The best ranked molecule from the ROCS search was Nifenazone – TanimotoCombo Score It has been used as the analgesic drug. - Withdrawn due to heavy side effects 2D structure of Nifenazone

Results – 16 HP-SEE User Meeting – Belgrade Electrostatic similarity study of the ROCS output (best ranked conformation per molecule) against the template molecule was performed in EON program - The 100 top ranked molecules from ROCS output was used - Electrostatic grids of the molecules were compared - Tanimoto index as a scoring function - The best scored was itdac-7, Tanimoto score MMFF94s charges were used - The itdac7 was proved as the modulator of Sir2 deacetylase

Results – 17 HP-SEE User Meeting – Belgrade Electrostatic comparison of the template (green) and the itdac-7 (gray)

Performance and further objectives – 18 HP-SEE User Meeting – Belgrade Screening of the much larger databases: - ZINC (over 21 milion compounds), Commercial Compound Collection (CoCoCo), 7 milion compounds, Assigning more reliable charges to the ROCS hits - Validation of hits obtained molecules/sec in average overlays/sec - 16 cpu, OpenEye MPI Performance Further objectives - executed with pbs

Conclusions - All Openeye applications installed on our home cluster are fully functional - We obtained the hit molecule that have been approved as the modulator of enzymes involved in deacetylations - Can run on several nodes - OpenMPI via pbs - Good scalability for reported calculations HP-SEE User Meeting – Belgrade

Conclusion Thank you for your attention! – 20 HP-SEE User Meeting – Belgrade