MOE Applications PDF

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MOE Applications 1. Medicinal Chemistry Applications MOEsaic: Web-Application for Ligand Analytics. MOEsaic is a browser-based application for analyzing series of small molecule chemical structures and related property data from drug discovery projects. Align molecules to facilitate pairwise comparison. Conduct substructure and similarity searches. Perform Matched Molecular Pair (MMP) analyses. Profile R-groups with defined scaffolds using a built-in chemical sketcher. Detect activity cliffs and bioisosteres. Visualize the data through property Plots and applied Filters. Design virtual structures and Document findings with text and images. Visualize and Analyze Non-Bonded Interactions: Visualize and analyze ligand-receptor interactions such as hydrogen bonds including CH..O interactions, halogen bonds, sulfuroxygen interactions, proton- and cation-π interactions using Extended Hückel Theory (EHT). EHT more accurately calculates interaction strengths and takes into account electron withdrawal and resonance effects. Protein-Ligand Interaction Diagrams: Automatically generate 2D diagrams of the active site residues interacting with a ligand or series of ligands. Visualize key interactions such as hydrogen bonds, salt bridges, hydrophobic interactions, cation-π, sulfur-LP and halogen bonds in 2D. Identify potential locations for ligand substitution using a depicted steric contour. Visualize solvent exposed ligand atoms and residues with strong hydrophobic interactions. Browse through a chemical series or receptor family series to identify conserved or non-conserved interactions for selectivity analysis. Surfaces and Maps: Build Molecular Surfaces colored by properties to define and characterize active site topology and identify ligand substitution opportunities. Predict knowledge-based non-bonded Contact Preferences or calculate Electrostatic Maps using the non-linear Poisson-Boltzmann equation to identify high value hydrophobic regions and polar hot spots. Calculate water density and binding desolvation penalty maps using 3DRISM, a first principles theory of solvation based on the Density Functional Theory of liquids. Detect non-obvious hydrophobic regions of binding sites created by correlation and cavitation effects to prioritize ligand modifications. Conformational Search and Analysis: Explore ligand conformation space to gain insights regarding bioactive conformations and intra-molecular interactions. Use LowModeMD generate conformations of macrocycles and multi-component systems (e.g., explicit water or counter-ions) by performing a fast implicit vibrational analysis and short molecular dynamics simulation. Flexible Alignment of Multiple Molecules: Perform 3D alignment (or superposition) of known and putative ligands to determine structural requirements for biological activity particularly useful in ligand-based drug design protocols since aligned groups are likely to be important for determining the bioactive conformation. Use the all-atom flexible

alignment procedure that combines a forcefield and a 3D similarity function based on Gaussian descriptions of shape and pharmacophore features to produce an ensemble of possible alignments of a collection of small molecules. Scaffold Replacement, Growing and Fragment Linking: Grow ligands, link fragments and replace scaffolds for fast follow-on compounds incorporating innovative linear, cyclic or fused scaffold arrangements. Refine novel structures in a (flexible) active site while maintaining important pharmacophore interactions and calculate predicted binding affinities. Use Medicinal Chemistry Transforms to explore local SAR by making small isosteric changes to ligands. Pharmacophore Discovery: MOE contains the industry-leading suite of pharmacophore discovery applications used for fragment-, ligand- and structure-based design projects. Pharmacophore modeling is a powerful means to generate and use 3D information to search for novel active compounds, particularly when no receptor geometry is available. Pharmacophore methods use a generalized molecular recognition representation and geometric constraints to bypass the structural or chemical class bias of 2D methods. Use an interactive editor to construct a 3D query from a molecular alignment or receptor structure. Perform a virtual screen of a conformational database to determine candidate active compounds. Customize pharmacophore features with SMARTS chemical patterns (for particular groups) and/or expressions. Restrict shape (receptor or ligand) by using union-of-spheres for included, excluded and exterior volumes. Refine the query with directional vector constraints on atoms or partial matches on features. Molecular Descriptors: Calculate over four hundred 2D and 3D molecular descriptors including topological indices, structural keys, E-state indices, physical properties, topological polar surface area (TPSA) and CCG's VSA descriptors with wide applicability to both biological activity and ADME property prediction. Apply Extended Hückel-based descriptors, such as LogP, LogD, and molar refractivity, for computing molecular properties. Calculate pKa and pKb of small molecules and determine the populations of ligand protonation states at a given pH. Use descriptors for classification, clustering, filtering and predictive model construction. Add custom descriptors using MOE's built-in Scientific Vector Language. Virtual Library Builder: Enumerate compound libraries through the reaction-based Combinatorial Library Builder. Use commercial or customized in-house reagents as input to a reaction engine. Conduct simple esterification reactions or multi-component Ugi type or Groebke-Blackburn-Bienyame reactions. Use standard 2D sketchers to specify reactions or multiple simultaneous reaction steps. Automatically screen reaction products for chemical similarity to a target or with a pharmacophore model. Filter the results with chemical descriptors or Lipinski's rule-of-five for drug-likeness. Calculate focused libraries by applying QSAR or pharmacophore models.

2. Biologics Applications Whole Protein and Interface Visualization and Analysis: Visualize protein:protein interface regions for non-bonded interactions (cation-π, hydrogen bonds, steric clash, etc…). Create molecular surfaces and analyze surface properties such a hydrophobic and electrostatic potentials. Analyze surface patches to understand local hydrophobic and polar properties. Compare multiple structures to understand differences in affinity and structural variability. Highlight potential reactive sites for oxidation and deamidation. Visualize and rank hot spots using knowledge-based potentials and evaluate the non-linear PoissonBoltzmann equation to evaluate electrostatic preferences in order to rationalize interactions and potential sites for mutagenesis. Protein-Protein Docking: Predict protein-protein binding poses. Generate high quality docked protein structures using a coarse-grained bead model in conjunction with FastFourier Transform (FFT) followed by all atom minimization. Focus the sampling space by using knowledge-based rigid body docking. Automatically detect antibody CDR sites to restrict the search space. Generate and analyze protein-protein interaction fingerprints to determine key residues implicated in binding. Antibody and Fusion Protein Modeler: Build 3D antibody structures or fusion proteins (including multi-domain models) from amino acid sequence by assembling domain fragments of experimentally determined backbone structures from one or more templates. Use specialized protocols for antibody modeling. Specify a customizable loop dictionary for knowledge based loop modeling. The homology models are scored with various scoring functions including MM/GBVI. Include environment units such as scFv, Fc or antigen fragments in the structural template for induced fit. Protein Patches: Visualize high valued hydrophobic and charged protein patches to rationalize surface properties and assess aggregation prone regions. Apply protein patch descriptors in QSAR and QSPR models for predicting and modulating protein properties such as solubility and viscosity. Use protein patches for detecting potential binding sites or mapping epitopes. Protein Properties: Calculate a comprehensive set of sequence and structure based physical properties such as pI, zeta potential, mobility, dipole moment, etc. for QSPR modeling. Use the predicted properties in conjunction with preliminary experimental data to rationalize stability and aggregation at a given pH. Calculate properties for an ensemble of mutants to identify and predict physical property trends on a relative scale. Protein Engineering: Explore and compare mutant series against a wild type with a unified protein engineering application. Conduct Alanine Scanning to systematically explore affinity. Assess protein stability and optimize unstable regions by identifying disulfide

bridging opportunities through Cysteine Scanning. Rationalize and perform single point or multiple mutations via Residue Scanning to assess and advance lead candidates. Use Sequence Design to search all possible multiple mutations to determine an optimal sequence. Easily identify residues prone to natural mutation, based on single nucleotide polymorphism, using Resistance Scanning. Automatically generate ensembles using molecular dynamics or LowModeMD to estimate ensemble averaged properties. Mutation and Rotamer Exploration: Perform single point mutations and discover amino acid accessibility with MOE's Rotamer Explorer. Predict the structure of amino acid mutations in a 3D protein structure and candidate rotamers using an energy-based scoring function. Visualize and analyze new interactions and properties using MOE's graphical interface. Sequence Analyzer and Editor: Visualize and modify structures at the residue level with an integrated sequence editor. Edit sequence information by cutting and pasting residues for loop grafting or build sequence, proteins, DNA or PTM structures. Mutate residues and evaluate rotamers with the Rotamer Explorer. Find optimal alignments of protein sequences and structural superposition using CCG's unique technology. Automatically annotate antibodies and apply alignment constraints for optimal superposition. Use the Sequence Editor to adjust alignments interactively. Dynamically color residues by function, sequence similarity or structural proximity. Advanced Molecular Simulations: Apply a streamlined process for structure preparation and optimization. Run molecular dynamics (MOE or NAMD) to evaluate stability and gross motions in loops or solvent. Run LowModeMD for generating an ensemble of conformations for protein loops, domains or peptides. Include explicit solvent with little overhead.

Doc Docking king using open softw software are (a comparison)

Program 1-Click Docking

Year Organisation Published 2011 Mcule

AADS

2011

Indian Institute of Technology

AutoDock

1990

The Scripps Research Institute

AutoDock Vina

2010

BetaDock

2011

Blaster

2009

The Scripps Research Institute Hanyang University University of California San Francisco

BSP-SLIM

2012

University of Michigan

DARWIN

2000

The Wistar Institute

DIVALI

1995

University of California-San

Description

License

Docking predicts the binding orientation and affinity of a ligand to a target Automated active site detection, docking, and scoring(AADS) protocol for proteins with known structures based on Monte Carlo Method Automated docking of ligand to macromolecule by Lamarckian Genetic Algorithm and Empirical Free Energy Scoring Function New generation of AutoDock

Basic free version

Based on Voroni Diagram Combines ZINC databases with DOCK to find ligand for target protein A new method for ligand-protein blind docking using lowresolution protein structures Prediction of the interaction between a protein and another biological molecule by genetic algorithm Based on AMBER-type potential function and

Freeware

Free to use Webservice

Freeware

Open source

Freeware

Freeware

Freeware

Freeware

Francisco Swiss Institute of Bioinformatics Mayo Clinic Cancer Center

EADock

2007

EUDOC

2001

FDS

2003

University of Southampton

FlexAID

2015

University of Sherbrooke

FlexPepDock

2010

The Hebrew University

FLIPDock

2007

Scripps Research Institute

FLOG

1994

Merck Research Laboratories

FRED

2003

OpenEye Scientific

FTDOCK

1997

Biomolecular Modelling Laboratory

GEMDOCK

2004

National Chiao

genetic algorithm Based on evolutionary algorithms

Freeware

Program for Academic identification of drug interaction sites in macromolecules and drug leads from chemical databases Flexible ligand and Academic receptor docking with a continuum solvent model and soft-core energy function Open source Target side-chain flexibility and soft scoring function, based on surface complementarity Modeling of peptideFreeware protein complexes, implemented within the Rosetta framework Free for academic use Genetic algorithm based docking program using FlexTree data structures to represent a protein-ligand complex Academic Rigid body docking program using databases of pregenerated conformations Systematic, exhaustive, Free for academic use nonstochastic examination of all possible poses within the protein active site combined with scoring Function Freeware Based on KatchalskiKatzir algorithm. It discretises the two molecules onto orthogonal grids and performs a global scan of translational and rotational space Generic Evolutionary Freeware

Tung University INRA

GPCRautomodel

2012

HADDOCK

2003

Centre Bijvoet Center for Biomolecular Research

Hammerhead

1996

Arris Pharmaceutical Corporation

idTarget

2012

National Taiwan University

iScreen

2011

China Medical University

LigDockCSA

2011

Seoul National University

LPCCSU

1999

Weizmann Institute of Science

MCDOCK

1999

Georgetown

Method for molecular docking Automates the homology modeling of mammalian olfactory receptors (ORs) based on the six threedimensional (3D) structures of G proteincoupled receptors (GPCRs) available so far and performs the docking of odorants on these models Makes use of biochemical and/or biophysical interaction data such as chemical shift perturbation data resulting from NMR titration experiments, mutagenesis data or bioinformatic predictions. Developed for protein-protein docking, but can also be applied to proteinligand docking. Fast, fully automated docking of flexible ligands to protein binding sites Predicts possible binding targets of a small chemical molecule via a divideand-conquer docking approach Based on a cloudcomputing system for TCM intelligent screening system Protein-ligand docking using conformational space annealing Based on a detailed analysis of interatomic contacts and interface complementarity Based on a non-

Free for academic use

Freeware

Academic

Freeware

Freeware

Academic

Freeware

Academic

University Medical Center MEDock

2007

SIGMBI

MolDock

2006

Molegro ApS

MS-DOCK

2008

INSERM

ParDOCK

2007

Indian Institute of Technology

PatchDock

2002

Tel Aviv University

PLANTS

2006

University of Konstanz

PLATINUM

2008

PRODOCK

1999

Moscow Institute of Physics and Technology (State University) Cornell University

PSI-DOCK

2006

PSO@AUTODO CK

2007

Peking University University of Leipzig

conventional Monte Carlo simulation technique Maximum-Entropy based Docking web server is aimed at providing an efficient utility for prediction of ligand binding site Based on a new heuristic search algorithm that combines differential evolution with a cavity prediction algorithm Multi-stage docking/scoring protocol All-atom energy based Monte Carlo, rigid protein ligand docking The algorithm carries out rigid docking, with surface variability/flexibility implicitly addressed through liberal intermolecular penetration Based on a class of stochastic optimization algorithms (ant colony optimization) Analysis and visualization of hydrophobic/hydrophili c properties of biomolecules supplied as 3D-structures Based on Monte Carlo method plus energy minimization Pose-Sensitive Inclined (PSI)-DOCK Particle Swarm Optimization (PSO) algorithms varCPSO and varCPSO-ls are suited for rapid docking of highly flexible

Freeware

Academic

Academic

Freeware

Freeware

Free for academic use

Freeware

Academic

Academic Academic

PythDock

2011

Hanyang University

Q-Dock

2008

Georgia Institute of Technology

QXP

1997

Novartis Pharmaceutical s Corporation

rDock

2013

University of York/ Open source project

SANDOCK

1998

Score

2004

University of Edinburgh Alessandro Pedretti & Giulio Vistoli

SODOCK

2007

SOFTDocking

1991

SwissDock

2011

VoteDock

2011

University of Warsaw

YUCCA

2005

Virginia Tech

MOLS 2.0

2016

University of Madras

Feng Chia University (Taiwan) University of California, Berkeley Swiss Institute of Bioinformatics

ligands Heuristic docking program that uses Python programming language with a simple scoring function and a population based search engine Low-resolution flexible ligand docking with pocket-specific threading restraints Monte Carlo perturbation with energy minimization in Cartesian space HTVS of small molecules against proteins and nucleic acids Guided matching algorithm The Score service allows to calculate some different docking scores of ligandreceptor complex Swarm optimization for highly flexible proteinligand docking Matching of molecular surface cubes Webservice to predict interaction between a protein and a small molecule ligand Consensus docking method for prediction of protein-ligand interactions Rigid protein-smallmolecule docking Rigid protein-smallmolecule docking, Flexible protein-peptide docking

Academic

Freeware

Academic

Open source

Academic Freeware

Academic

Academic

Free webservice for academic use

Academic

Academic Open Source

AutoDoc AutoDock k STEP 1: Preparing Coor Coordinates dinates The first step is to prepare the ligand and receptor coordinate files to include the information needed by AutoGrid and AutoDock. These coordinate files are created in an AutoDock-specific coordinate file format, termed PDBQT, w...