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|3D
S.C
OM
/BIO
VIA
© D
assa
ult S
ystè
mes
| Con
fiden
tial I
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| 5/2
7/20
19
Accelerate Science-Led Innovation for Competitive Advantage
BIOVIA Discovery Studio催化模擬與輔酶結構優化
創源生技分子視算中心
經理陳冠文 (Gene)
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Copyright© 2019 GGA Corp., All rights reserved.
Copyright and Disclaimer
• Copyright © 2019 GGA corp. All rights reserved.
• This presentation and/or any related documents contains statements regarding our plans or expectations for future features, enhancements or functionalities of current or future products (collectively "Enhancements"). Our plans or expectations are subject to change at any time at our discretion. Accordingly, GGA Corp. is making no representation, undertaking no commitment or legal obligation to create, develop or license any product or Enhancements.
• The presentation, documents or any related statements are not intended to, nor shall, create any legal obligation upon GGA Corp., and shall not be relied upon in purchasing any product. Any such obligation shall only result from a written agreement executed by both parties.
• In addition, information disclosed in this presentation and related documents, whether oral or written, is confidential or proprietary information of GGA Corp.. It shall be used only for the purpose of furthering our
business relationship, and shall not be disclosed to third parties.
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Copyright© 2019 GGA Corp., All rights reserved.
GGA is part of the BIONET Group (訊聯生物科技)
CEO: Christopher Tsai, Ph.D. 蔡政憲博士
Established: Nov. 2008
Main Product & Service Areas:
1. Genetic Testing
2. Molecular Diagnosis
3. Scientific Informatics & Bio IT
IPO Date: September 17, 2012
Stock Ticker: 4160 (Taiwan OTC)
用科學的知識與技術，提昇台灣產業科技創新能量。
Our Mission in 2019
✓ High throughput screening and machine learning for
electrolyte additives
✓ Design optimization for industry 4.0
✓ 整合材料模擬與電子實驗記錄系統服務 (台灣前三大石化廠)
✓ 執行國家型基因體分析平台建置計劃
✓ 連結產學界計算模擬技術
Be Your Partner
分子視算中心
我們是由物理、化工、生物、統計、資訊等超過十位不同領域的專家組成，具備多年產業經驗。
10+
我們服務全台灣超過四十所大專院校，每年舉辦超過100場以上教育訓練課程。
100+
我們擁有包含化學、材料、生物領域，超過百萬筆資料數據，並有實際處理分析案例。
1M+
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Discovery Studio
• It is an interactive 3D modeling environment
Macromolecule design
and engineeringDNA –ligand
complexes
Library design and lead optimization
Small-molecule design
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Discovery Studio: A Comprehensive Portfolio
In-situ lead enumera-
tion
Scaffold hopping
Virtual screening
QSAR, ADME and
Toxicity
Simulationand
Quantum Mechanics
Homology modeling
Protein docking
Protein Stability
and Binding affinity
Ligand profiling
Specialist: Antibody
design
Specialist: Membrane
Proteins
X-ray
Specialist: Protein
aggregation
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Outline
• 分子動力學分析應用介紹
• Standard Dynamics Cascade
• Steered Molecular Dynamics
• 結構優化與特性預測
• FEP+
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Outline
• 分子動力學分析應用介紹
• Standard Dynamics Cascade
• Steered Molecular Dynamics
• 結構優化與特性預測
• FEP+
3DS
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Simulation
• Assume……that we have a molecule on the computer screen
• It could have been
• Built from scratch
• Prepared from a PDB file
• Read in from a prepared file
• But…How do you know you have a reasonable structure?
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Simulation
• Molecular Mechanism (MM), Molecular Dynamic (MD) simulation
• Assignment of the force field
• CHARMm, charmm19/22/27/36, CHARMM Polar H, MMFF, CFF
• Additional solvent model
• implicit/explicit water molecules, Implicit Membrane
• Additional constraints
• Fix, Dihedral, Harmonic, Distance
• Energy calculation
• Time point energy calculation
• Structural optimization
• Structural optimization using QM (DFT)
• MD（CHARMm, NAMD）
• Analyze trajectory
• Plot of various properties – distance, angle,
interaction energy, PCA, RDF, Radius of
Gyration
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Molecular Mechanism (MM)
• Based on the classical molecular mechanics
• Energy calculations
• Energy minimizations
• Molecular dynamics
• Widely used in
• Structure-based ligand design
• Structure generation from NMR experiments
• Protein engineering
• Substrate recognition studies
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Force Field
“The molecule is considered to be a collection of atoms held
together by simple elastic or harmonic forces…”
Adopted from computer software applications in chemistry, Peter C. Jurs, 1986
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Force Field
“The molecule is considered to be a collection of atoms held
together by simple elastic or harmonic forces. The forces are
defined in terms of potential energy described by the internal
coordinates of the molecule…”
Adopted from Computer Software Applications in Chemistry, Peter C. Jurs, 1986
E = kq (q - q0)2E = kb(r - r0)
2
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Force Field Energy Expression
• General form
• Force fields are generally differ in:
• Function expression of each term
• Parameter calculation method
• Function parameters
• Cross terms
E = Ebond + Eangle + Etorsion + Eimpr + Enonbond
(Evdw + Eelecs)
+ Eother
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CHARMm force field
• 2 official version
• Harvard academic version (Professor Martin Karplus, the 2013
Nobel laureate in Chemistry)
• DS integration (Graphical interface)
• Integration with other programs that use the CHARMm engine
(CDOCKER, MCSS, ZDOCK)
• GBSA / IM solvation model
2, 3 months late release
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Force Field Parameters
• Atoms have different environments
O
NH2
OH
Carbon in an aromatic ring
Aliphatic sp3 carbon
with two hydrogens
Carbonyl carbon
Aliphatic sp3 carbon
with one hydrogen
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Force Field Parameters
• ...which yield different parameters.
O
NH2
OH
Ca
CT2
CC
CT1
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Molecular Mechanics
12.3 kcal/mol 13.2 kcal/mol
It is the difference in energy that matters!
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Energy Minimization
• If lower energy conformations are more favorable, we may want to find them.
• Could perform an energy minimization
• A mathematical technique to find a local minimum for the energy expression shown earlier
• Adjust Cartesian coordinates to attain the more favored conformation
• When to perform an energy minimization?
• Clean a structure that you build
• Prepare a structure from a PDB file
• Relax a modified structure
• Relax a docked protein-ligand complex
Conformation Coordinate
En
erg
y
12.3 kcal/mol
13.2 kcal/mol
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Molecular Dynamics
• Global vs Local minima
How to explore other
minima or other areas
of the potential energy
surface?
Conformation Coordinate
Energ
y
12.3 kcal/mol
13.2 kcal/mol
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Use of Molecular Dynamics
• Conformational changes of molecule
• Simple vibrations that produce spectra
• Breathing of DNA
• Hinge bending
• Allosteric transitions
• Protein folding
• Refinement of docking results
• Induced fit
• Free energy changes
• Calculation to binding energies
• Determination of thermodynamic properties
• Enthalpy and entropy changes
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Example for MD simulation
Time
En
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Temperature and Velocities
• From kinetic theory, temperature is proportional to the kinetic
energy of the particles.
• From physics, kinetic energy is proportional to velocity.
• Therefore, temperature is proportional to the velocity of the
atoms.
kTKE2
3=
2
2
3mvKE =
k
mvT
2
=
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Temperature and Dynamics
At a higher temperature…
Atoms move more…
Cover more conformational space!
• High temperature dynamics can be used to overcome barriers to explore new local minima
• Effective for reasonable length peptides and other oligomers (12 residues or less)
• Must be aware of
• Possible loss of secondary structure
• Trans-cis isomerization of peptide bonds
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Energy landscape:
searching the possible conformations
x
U
Local minimum
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Time scale of Biological events
Motion Time scale (Sec)
Bond stretching 10-14 to 10-13
Elastic vibrations 10-12 to 10-11
Rotations of surface side
chains
10-11 to 10-10
Hinge bending 10-11 to 10-7
Rotation of buried side chains 10-4 to 1 sec
Allosteric transitions 10-5 to 1 sec
Local denaturations 10-5 to 10 sec
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Usage of MD simulation
• Example: Aquaporin
• Aquaporins form a family of
pore proteins that facilitate the
efficient and selective flux of
small solutes across biological
membranes
Mechanism of selectivity in aquaporins and aquaglyceroporins. Hub JS, de
Groot BL. Proc Natl Acad Sci U S A. 2008
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Usage of MD simulation
• Example: GB1 folding
• IgG-binding protein G
Folding pathway of the b1 domain of protein G explored by multiscale
modeling. Kmiecik S, Kolinski A. Biophys J. 2008
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Outline
• 分子動力學分析應用介紹
• Standard Dynamics Cascade
• Steered Molecular Dynamics
• 結構優化與特性預測
• FEP+
3DS
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General Steps in MD
Prepare Entities
Solvation
Minimize System
Heating/Cooling
Equilibration
Production
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General Steps in MD
Prepare Entities
Solvation
Minimize System
Heating/Cooling
Equilibration
Production
Protein Preparation
• Repair deficient structure
• Identify binding site
• Modify the protonation state
Prepare Protein
Prepare Ligand
Ligand Preparation
• Add hydrogen atom
• Generate 3D structure coordinates
• Creating isomers
• Remove duplicates
• Valence modification
• Standardization charges
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SBD: Input Preparation
LigandsProteins Fragments
• Standardise atom names
• Insert missing atoms in
residues
• Remove alternate
conformations
• Insert missing loops
• Optimize short & medium size
loops
• Calculate pK and protonate
• Add hydrogens
• Calculate 3D coordinates
• Enumerate ionization states
• Ionize functional groups
• Generate tautomers and
isomers
• Remove duplicates
• Fix bad valencies
• Standardize charges for
common groups
• Retain largest fragment
• Generate fragments using
RECAP* rules
• Rule of Three filters
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Selecting the Protein Receptor
• Choosing the protein receptor
• Probably have a receptor in mind already
• Based on biological or medical problem
• Reinforced by biological data
• Requires a three-dimensional structure of the receptor
• X-ray crystal structure
• NMR structure
• Homology model
• Can be an apo form of receptor
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Protein Preparation
• Before any docking can be performed, the receptor must be properly prepared
• Particularly a concern with PDB files
• Preparation includes having...
• All residues completed
• Correct chemistry
• Correct bond orders
• Correct atom valences
• All required hydrogen atoms added
• Correct formal charges
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Protonation state
• Calculate pKa for each residue in different pH
• Modify the protonation (ionization) status
Example for His334 of Protein A in different protonation status
Protein A pH7.2 pH8.0
HIS334 1 0
HIS376 1 0
HIS1253 1 0
HIS1540 1 0
HIS1575 1 0
HIS1591 1 0
HIS1937 1 0
HIS2391 1 0
HIS2452 1 0
HIS2455 1 0
HIS2488 1 0
HIS2500 1 0
HIS2625 1 0
HIS2826 1 0
HIS2865 1 0
HIS2898 1 0
HIS2946 1 0
HIS2998 1 0
GLU1018 0 -1
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Standardize molecules
• Beautification
• Keep/Remove largest/smallest fragments (option of Standardize
Molecule)
• Add/Remove atom numbers (found in Utilities)
• Add/Remove hydrogens
• Add hetero hydrogens (option of Add Hydrogens component)
• Center molecule (option of Standardize Molecule)
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Standardize molecules cont. - Strip salts
• Strip Salts will strip a parent molecule of
its counter ions.
• Chemistry Data\Queries\Salts.sd
contains any defined salt structure
• User defined salt queries can be added
via parameter User Salts
• Further components: Identify Salts and
Generate Salts
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Ligand preparation
Performs the following steps, some of which can be controlled by
the protocol parameters:
• Generate a canonical tautomer
• Keep only the largest fragment
• Set standard formal charges on common functional groups
• Kekulize the molecule
• Enumerate ionization states at a given pH range (Optional)
• Enumerate tautomers (Optional)
• Enumerate isomers (Optional)
• By default only unspecified bonds and atoms are enumerated
• Remove duplicate structures (Optional)
• Filter structures that violate Lipinski rules (Optional)
• Generate a standard 3D conformation (Optional)
• Catalyst is used to generate a reasonable 3D conformation
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Tautomers
• Compounds whose structures differ markedly in arrangement of
atoms, but which exist in easy and rapid equilibrium, are called
tautomers.
This means:
– Possible duplicate molecules may be missed.
– Different structures may have different values for calculated properties.
NOH NO H
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General Steps in MD
Prepare Entities
Solvation
Minimize System
Heating/Cooling
Equilibration
Production
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Explicit Solvent Model
• Generate Periodic
Boundary Condition
(PBC)
• Set cell shape and
minimum distance from
PBC depends on the
entity shape
• Add Counterion
• Minimization (Optional)
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Periodic Boundary Conditions (PBC)
Every box of material is surrounded by 26 boxes More Realistic
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How to handle PBC?
• PBC made the simulations more realistic but increases the number of
calculations!! (Remember the extra 26 copies?)
• Solutions
• Non-bond cutoffs
• Atom based cutoff
• Group based cutoff
• Ewald sums
• Cell multipole
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General Steps in MD
Prepare Entities
Solvation
Minimize System
Heating/Cooling
Equilibration
Production
Standard Dynamics Cascade
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Minimisation algorithms
• Several algorithms are available
• Steepest Descent: First derivative method
• Conjugate Gradient: First derivative method
• Newton-Raphson: Second derivative method
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Energ
y
x
A
B
E
G
C
D
F
Steepest descents
• First move is random
• Next move in the direction of the local downhill gradient ➔ - E(xi, yi)
• It moves as far as possible in this direction ➔ chooses a new direction orthogonal to
the previous one
• Advantages - Avoids 'saddle points'
- Efficient outside the minimum
• Disadvantages - Slower close to minimum
- Linear search may cause problems
- Might 'zigzag' down valleys
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Conjugate gradients
• Increased efficiency of line searches by controlling the choice of new direction
• First move is random
• In addition to the gradient, it uses the previous displacement and the previous gradient ➔ It refines the direction toward the minimum
where γi = f(gi, gi+1)
• Advantages - Efficient convergence to the minimum
• Disadvantages - Unstable far away from a local minimum
– Longer time/iteration
iiii hgh += ++ 11
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Molecular Dynamics
• NVT constant volume and constant temperature
• Conformational searches of models in vacuum
• Collection phase
• NPT constant pressure and constant temperature
• Only for periodic systems
• Andersen/Berendsen – allow changes in cell volume
• Parrinello-Rahman – allows changes in cell volume and cell shape
• Equilibration phase
• NVE constant volume and constant energy
• Not for equilibration phase/Useful for collection phase
• NPH constant pressure and constant enthalpy
• Equilibration phase
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Molecular Mechanics
Forcefield Parameters
Structure
Molecular
Mechanics
Energy
MinimizationMonte Carlo
Simulations
Conformational
Analysis
Molecular
Dynamics
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New! CHARMm GPU support
• OpenMM GPU provides a highly optimized performance and cost
effective solution for running molecular simulations.
• Compared to the highly parallelized NAMD CPU, CHARMm
OpenMM running on one GPU is approximately 9 times faster
than NAMD on an 8 core CPU.
• Dynamics (Production): Added options in this protocol to support
GPU for Linux platform.
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Performance test by GGA corp.
• Test Machine:
• Motherboard: ASUS ROG STRIX Z370-G GAMING
• CPU: i7-8700K 3.7GHz 6core , 12 processors
• RAM: 16GB
• HDD: 500GB
• GPU: Nvidia GTX 1080Ti
• Power: 600W
• OS: Redhat Red Hat Enterprise Linux Server release 7.4 (Maipo)
• GPU Driver: NVIDIA-Linux-x86_64-384.111.run
• Discovery Studio Version: 19.1.0.1963
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Performance test by GGA corp.
Platform Simulation
time
Elapse time
CPU: 4 processors 500ps 06:22:24 (~382min)
CPU: 8 processors 500ps 05:58:10 (~358min)
GPU 500ps 00:08:18 (~8min)
CPU: 4 processors 1ns 13:23:55 (~804min)
CPU: 8 processors 1ns 11:52:05 (~712min)
GPU 1ns 00:16:18 (~16min)
Sample: mAB Fv region in explicit solvent model, 20414 atoms
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Outline
• 分子動力學分析應用介紹
• Standard Dynamics Cascade
• Steered Molecular Dynamics
• 結構優化與特性預測
• FEP+
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Time scale of Biological events
Motion Time scale (Sec)
Bond stretching 10-14 to 10-13
Elastic vibrations 10-12 to 10-11
Rotations of surface side
chains
10-11 to 10-10
Hinge bending 10-11 to 10-7
Rotation of buried side chains 10-4 to 1 sec
Allosteric transitions 10-5 to 1 sec
Local denaturations 10-5 to 10 sec
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Steered Molecular Dynamics
• The basic idea behind any SMD simulation is to apply an
external force to one or more atoms, which we refer to as SMD
atoms.
SMD atom
Constant Velocity or Force
Pulling
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Why SMD
• Accelerates processes to simulation time scales (ns)
• Yields explanations of biopolymer mechanics
• Complements Atomic Force Microscopy
• Finds underlying unbinding potentials
• Generates and tests Hypotheses
http://www-s.ks.uiuc.edu
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Atomic Force Microscopy Experiments
of Ligand Unbinding
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SMD of Biotin Unbinding: What We Learned
• biotin slips out in steps, guided by amino acid side groups, water
molecules act as lubricant, MD overestimates extrusion force
Israilev et al., Biophys. J., 72, 1568-
1581 (1997)
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General Steps in MD
Prepare Entities
Solvation
Set SMD atom
Set Constant Velocity or
Force Pulling
Set Simulation Time/Run
Calculating Free Energy
from multiple SMD runs
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Calculating Free Energy from multiple SMD runs
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Outline
• 分子動力學分析應用介紹
• Standard Dynamics Cascade
• Steered Molecular Dynamics
• 結構優化與特性預測
• FEP+
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Free Energy Perturbation
• ΔGcomplex – ΔGfree
• ΔG2 – ΔG1
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Predict Relative Free Energy of Binding
• Apply FEP to a congeneric analog series in four steps using NAMD
• Use either docking results, or in-situ analog series as input
• Optional: Type analogs with charmm36/GCenFF using MATCH engine
• Step 1: Generate a ligand pairs network map
• Builds a pairs network using maximal common substructures
• Step 2: Set up relative FEP calculations
• Generates explicit solvent models for each ligand pair and protein-ligand pair complex in the network map
• Step 3: Run free energy perturbation calculations
• Perform FEP simulations on each solvated ligand pair model
• Step 4: Collate the simulation results
• Summarises both relative free energy calculations and absolute free energies (if a free energy value is available for the reference lead)
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Predict Relative Free Energy of Binding
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74
Q&A
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For more information please contact…
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