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• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Integrated Computational andExperimental Approach to
Characterization and Modeling ofPolymeric Biomaterials
Prof. Doyle Knight
Dept. of Mechanical and AerospaceEngineering , Rutgers University
October 31, 2008
The 9th New Jersey Symposium on Biomaterials Sciences and Regenerative Medicine
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Prof. Doyle KnightMechanicalEngineering
Prof. Robert LatourBioengineering
Protein-SurfaceInteractions
Dr. Xianfeng LiPolymer Science& Computational
Chemistry
Prof. Alan WindleMaterials Science
Dr. James ElliottMaterials Science
Dr. Aurora CostacheComputational
Chemistry
RESBIO Core C – Computational Modeling
Dr. Mathieu BouvilleMaterials Science
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
RESBIO-Core C
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Outline
Motivation, Goal and Approach
The Combinatorial-Computational Method
Semi-empirical models
Biomaterials Store
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
ChemspeedSLT100
BiomaterialsCluster
Motivation, Goal and Approach
Motivation– Modern synthesis techniques can produce
polymer libraries of extraordinary size (~1000sto ~10,000s)
– Characterization of the bioresponse to suchlarge libraries of polymers by experimentalmethods is infeasible
Goal– Develop efficient methods for identifying lead
polymers for specific biomedical research orclinical application
Approach– Combinatorial Computational Method (CCM)
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
The Combinatorial Computational Method(CCM)
Synthesis of arepresentativesubset fromthe library
CCM
Descriptor Generation-physical properties-biological properties-calculated descriptors
Experimental validationof model predictions,evaluation of leadpolymers
Librarydesign
ResultorProduct
A materialsrequirement
Computational modeling,development ofcorrelations and identifylead polymers
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Semi-empirical models
Predict all stages of bioresponse to polymericbiomaterial, e.g.,– Protein adsorption– Cellular response– Degradation
using the descriptors generated by– Rapid screening and high throughput characterization– Mechanical interactions between cells and substratum– Biological response of polymeric materials– All-atom and mesoscale molecular dynamics simulations
Use the semi-empirical models in theCombinatorial Computational Method
Sawyer, A. et al,Biomaterials, 2007
Beyer, M. et al,Biomaterials, 2006
Zhang, Z. et al, Biomaterials,2006
CCM
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
2D and 3DDescriptorsOf Polymer
• Chemical Structure Prediction forENTIRE Library• Rank ordering
Main Steps to Build Semi-empirical Models
Experimental Data•SUBSET ofLibrary
Example:•Protein adsorption•Cell proliferation
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
TopologicalTopological
2-D structural formula
(Kier & Hall indices)
ElectrostaticElectrostatic
Charge distribution (partialcharges, PCSA)
GeometricGeometric
3-D structure of molecule(SA, Molecular Volume)
Quantum-chemicalQuantum-chemical
Molecular orbital structure (HOMO-LUMO energies, dipole moment)
*
O
CH2
CH2
O
NH CH CH2
O
O
O
O
CH2 O
CH2
OH
CH2 *
n
Constitutional descriptors
Molecular composition(Mw, # of atoms and bonds)
Calculated Molecular Descriptors –2D and 3D
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Examples of 2D and 3Ddescriptors
Starting from 3D structure ofpolymer, not relaxed-12 repeatunits• Simplest model• Fast• Computationally less expensive
Calculated molecular descriptors - 2D and 3D
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
3D conformation of polymer is important
Molecular Dynamics Simulations used prior 3D
descriptors calculation
Calculated molecular 3D descriptors –Molecular Dynamics Simulations
Final configurations of 1 ns MD simulations in implicit water. (a) Tetramer of poly(DTEglutarate) and (b) tetramer of poly(DTE dodecandioate).
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
2D and 3DDescriptorsOf Polymer
• Chemical Structure Prediction forENTIRE Library• Rank ordering
Main Steps to Build Semi-empirical Models
Experimental Data•SUBSET ofLibrary
Example:•Protein adsorption•Cell proliferation
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Semi-empirical Models
Semi-empirical models are mathematicalcorrelations between inputs (descriptors) andoutcomes
Require experimental data for calibration
Examples
Decision Tree
Artificial Neural Network
Kriging function
Polynomial Neural Network
Radial Basis Functions
Support Vector Machines
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
2D and 3DDescriptorsOf Polymer
• Chemical Structure Prediction forENTIRE Library• Rank ordering
Main Steps to Build Semi-empirical Models
Experimental Data•SUBSET ofLibrary
Example:•Protein adsorption•Cell proliferation
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Prediction of Fibrinogen Adsorption
Fibrinogen adsorption correctlypredicted for 38 of 45 polymers(I.e., within experimentaluncertainty)
Average rms percent error inprediction of validation set is 35%
Most significant descriptors
Tg: glass transition temperature
a_nH: number of hydrogen atoms in themolecule
logP(o/w): logarithm of theoctanol/water partition coefficient
Smith, J., Seyda, A., Weber, N., Knight, D., Abramson, S., and Kohn, J., Macromolecular RapidCommunications, Vol. 25, 2004, pp. 127-140.
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Prediction of Rat Lung FibroblastProliferation
RLF proliferation correctlypredicted for 41 of 48 polymers(I.e., within experimentaluncertainty)
Average rms percent error inprediction of validation set is 28%
Most significant descriptors
SlogP_VSA9: Van der Waals surface arealogP(o/w) > 0.4
hydrophilic_factor: number of hydrophilicgroups
SlogP_VSA5: Van der Walls surface area
0.15 < logP(o/w) < 0.2
Smith, J., Seyda, A., Weber, N., Knight, D., Abramson, S., and Kohn, J., Macromolecular RapidCommunications, Vol. 25, 2004, pp. 127-140.
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Fibrinogen adsorption to polyarylates
Gubskaya, A. et al, "The Prediction of Fibrinogen Adsorption for Biodegradable Polymers:Integration of Molecular Dynamics and Surrogate Modeling", Polymer, Vol. 48, 2007, pp. 5788-5801.
50
100
150
200
250
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45
experimental
predicted
Fib
rin
og
en
Ad
so
rp
tio
n
Polymer number
R
R
R
RR
DTH diglycolate
Fibrinogen Adsorption for PolyarylatesLibrary
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
20
70
120
170
220
20 70 120 170 220
Training setTest set
Actual
Predicted
R2=0.95
Cell growth
10
30
50
70
90
110
130
150
10 30 50 70 90 110 130 150
Training set
Test set
Actual
Predicted
R2=0.8
Cell attachment
100
120
140
160
180
200
220
100 120 140 160 180 200 220
R2 = 0.86
Training setTest set
Fibrinogen adsorptionActual
Predicted
3 models: were built using 20-23 experimental points they were validated using splitting data
protocolPrediction was generated for 1584 copolymers(for each type of bioresponse).
Polymethacrylates Library-CopolymersCopolymers
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Data flowing from MySQL to WEKA environment
Easy to use GUI (Graphical User Interface) for doing modeling using WEKA –machine learning algorithms for solving real-world data mining problems
Biomaterials Store
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Biomaterials Store
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Crystal Structure ofFragment D of Fibrinogen
Applications of Biomaterials StorePrediction of Fibrinogen Adsorption
m
diacid component
diphenol component
R
O
C
C NH OO (CH2)nC
O
CH2
O
CHC
O
Y
O
n=1,2
PolymerPredicted Fibrinogen Adsorption
Experimental data
Descriptors for polymers
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Applications of Biomaterials StorePrediction of Protein Retention
Hydrophobic Interaction Chromatography (HIC)
Apply Biomaterials Store to any kind of data
Accurate predictions of protein retention in HIC systems
Butyl Sepharose ValidationSet Predictions
Experimental
Predicted
Ladiwala’s predicted
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Availability of Biomaterials Store
Ready to be used on our cluster
Soon available through our website
http://www.njbiomaterials.org/web/index.php?p=resbio&s=9757
Short courses will be available
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Summary
Predict all stages of bioresponse to polymericbiomaterials:– Protein adsorption– Cellular response– Degradation – next step
Develop tools for identifying lead polymers forspecific biomedical research or clinicalapplication
- Semi-empirical Models
- Combinatorial - Computational Method
- Biomaterials Store
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
THANK YOU!
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
HO2CCO2H
HO2C CO2H
HO2CCO2H
HO2CCO2H
HO2CCO2H
HO2C O CO2H
HO2CCO2H
HO2C OO CO2H
3-Methyl-Adipic Acid
Diglycolic AcidGlutaric Acid
Sebacic Acid
Adipic Acid
Suberic Acid Dioxaoctanedioic Acid
Succinic Acid
C
O
OHYC
O
HO
Combinatorial Polymer Libraries Today
OH OH
OH OH
OH
OO
OH
OH
OH
OH
OH
OH
HO CH2
C
C
O
NH CH CH2
O
OR
OH
Isopropanol
Benzyl Alcohol
Butanol
Hexanol
iso-Butanol
2-(2-Ethoxyethoxy)ethanol
sec -ButanolEthanolMethanol
Dodecanol
Octanol
n=1,2
n
diacid component
diphenol component
R
O
C
C NH OO CH2CH2C
O
CH2
O
CHCO
Y
O
Siz
e o
f li
bra
ry
CombinatorialExplosion!!!
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
DECISION TREE
ANALYSISDescriptors generation…
P1
P2
Pn
ChemicalStructure ofn Polymers
2D and 3D molecular descriptors (Dn,1…Dn,102)+ two (Tg, ) experimentally measured quantities (Dn,103,Dn,104)+ TFI (Dn,105) for each of n polymers
D1,1…D1,105
D2,1…D2,105
Dn,1…Dn,105
…
…
E1 E2 En
ExperimentalDataset for npolymers
Identity of3 mostsignificantdescriptors:Dx,A,Dx,B,Dx,C
Main Steps to Build Semi-empirical Models
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
ANN
…D1,A, D1,B,D1,C
Three bestdescriptors foreach of the npolymers
Prediction ofbioresponse forn/2 polymers inthe validation set
Pn/2+1
Pn/2+2
Pn
…
…
E1 E2 En/2
Experimental Dataset forn/2 polymers (training set)
D2,A, D2,B,D2,C
Dn,A, Dn,B,Dn,C
Main Steps to Build Semi-empirical Models
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Knowledge-Based Design of Polymeric
Biomaterials Beginning at the Atomic Level
Prof. Robert A. Latour
Dr. Xianfeng Li
Clemson University
Clemson, SC
NJ Biomaterials Symposium 2008
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Efficient searching over polymer compositional space
– Local vs. global compositional optimization
– Potential provided by molecular simulation
• Molecular simulation
– Need for advanced sampling methods
• Development of molecular models of RESBIO polymers
– Advanced sampling methods
– Use as predictive tool for both local and global optimization
Overview
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Efficient searching over polymer compositional space
– Local vs. global compositional optimization
– Potential provided by molecular simulation
• Molecular simulation
– Need for advanced sampling methods
• Development of molecular models of RESBIO polymers
– Advanced sampling methods
– Use as predictive tool for both local and global optimization
Overview
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Efficient searching over polymer compositional space
– Local vs. global compositional optimization
– Potential provided by molecular simulation
• Molecular simulation
– Need for advanced sampling methods
• Development of molecular models of RESBIO polymers
– Advanced sampling methods
– Use as predictive tool for both local and global optimization
Overview
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Current Approach to Biomaterials Design
Local
parameter space
for biomaterials
design within a
polymer library
Optimal composition
Selected sampling within library
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Local
parameter space
for biomaterials
design within a
polymer library
Optimal composition
Selected sampling within library
Current Approach to Biomaterials Design
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Local
parameter space
for biomaterials
design within a
polymer library
Optimal composition
Selected sampling within library
Current Approach to Biomaterials Design
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Local
parameter space
for biomaterials
design within a
polymer library
Optimal composition
Selected sampling within library
Current Approach to Biomaterials Design
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Optimal composition
Local
parameter space
for biomaterials
design within a
polymer library
Selected sampling within library
RESBIO Approach – Interpolation within Polymer Library
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Optimal composition
Local
parameter space
for biomaterials
design within a
polymer library
Selected sampling within library
RESBIO Approach – Interpolation within Polymer Library
Local Optimization
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Selected sampling within library
Optimal composition – local space
1
Library – randomly selected
Global
Parameter Space
for Polymeric
Biomaterial Design
Optimal composition – global space
RESBIO Approach
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Selected sampling within library
Optimal composition – local space
1
II2
Library – randomly selected
Global
Parameter Space
for Polymeric
Biomaterial Design
Optimal composition – global space
RESBIO Approach
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Selected sampling within library
Optimal composition – local space
1
II2
Library – randomly selected
Global
Parameter Space
for Polymeric
Biomaterial Design
Optimal composition – global space
II3
RESBIO Approach
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Molecular Simulation: Cause-and-Effect Relationships
Selected sampling within library
Optimal composition – local space
1
II2
Library – randomly selected
Global
Parameter Space
for Polymeric
Biomaterial Design
Optimal composition – global space
II3
RESBIO Approach
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Vision for Future – Extrapolation to New Polymer Libraries
Selected sampling within library
Optimal composition – local space
1
II2
Library – randomly selected
Optimal composition – global space
Global
Parameter Space
for Polymeric
Biomaterial Design
II3
Molecular Simulation: Cause-and-Effect Relationships
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Optimal composition
Library surrounding
parameter space
containing
global optimum
composition
Selected sampling within library
Global Optimization
Vision for Future – Extrapolation to New Polymer Libraries
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Objectives
• Development of molecular models of RESBIO polymers
– Bulk-phase models (water/drug transport and partitioning)
– Surface-phase models (protein adsorption and cellular response)
• Generate advanced descriptors from 3-D polymer structure
– Enhanced capabilities for local optimization
• Develop cause-and-effect understanding governing system
behavior
– Guide for global optimization
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Objectives
• Development of molecular models of RESBIO polymers
– Bulk-phase models (water/drug transport and partitioning)
– Surface-phase models (protein adsorption and cellular response)
• Generate advanced descriptors from 3-D polymer structure
– Enhanced capabilities for local optimization
• Develop cause-and-effect understanding governing system
behavior
– Guide for global optimization
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Objectives
• Development of molecular models of RESBIO polymers
– Bulk-phase models (water/drug transport and partitioning)
– Surface-phase models (protein adsorption and cellular response)
• Generate advanced descriptors from 3-D polymer structure
– Enhanced capabilities for local optimization
• Develop cause-and-effect understanding governing system
behavior
– Guide for global optimization
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Molecular Simulation
• Basic equations– Force field equation
– Newton’s laws of motion
– Statistical mechanics {properties}
• Predict 3-D structure of polymers– Bulk-phase models
– Surface-phase models
• Advanced descriptors– Improved correlations with expt. data sets
• Investigate how functional groups of
polymer influence system behavior
)},({ bondingpositionfF =v
}{ amFvv
=
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Molecular Simulation
• Basic equations– Force field equation
– Newton’s laws of motion
– Statistical mechanics
• Predict 3-D structure of polymers– Bulk-phase models
– Surface-phase models
• Advanced descriptors– Improved correlations with expt. data sets
• Investigate how functional groups of
polymer influence system behavior
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Molecular Simulation
• Basic equations– Force field equation
– Newton’s laws of motion
– Statistical mechanics
• Predict 3-D structure of polymers– Bulk-phase models
– Surface-phase models
• Advanced descriptors– Improved correlations with expt. data sets
• Investigate how functional groups of
polymer influence system behavior
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Molecular Simulation
• Basic equations– Force field equation
– Newton’s laws of motion
– Statistical mechanics
• Predict 3-D structure of polymers– Bulk-phase models
– Surface-phase models
• Advanced descriptors– Improved correlations with expt. data sets
• Investigate how functional groups of
polymer influence system behavior
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Molecular Simulation of Peptide-PLA Interactions
O’Brien, Langmuir, in press
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
O’Brien, Langmuir, in press
Molecular Simulation of Peptide-PLA Interactions
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
PDB: 1FIB
Macrophage binding site
PDB: 1M1J, fibrinogen, 340 kDa
Biomaterials Design to Control Cellular Response
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
PDB: 1FIB
Macrophage binding site
PDB: 1M1J, fibrinogen, 340 kDa
COOH / COO- Surface
CH3 Surface
COOH / COO- Surface
CH3 Surface
Agashe, Langmuir, 2005
Biomaterials Design to Control Cellular Response
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Simulation of Fibrinogen – Polymer Interactions
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Need for advanced sampling methods
Simulation of Fibrinogen – Polymer Interactions
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Development all-atom molecular models of RESBIOpolymers (existing all-atom polymer force field - PCFF)
• Application of advanced sampling methods• Translation of all-atom model into coarse-grained (CG) model
- Force field parameterization for CG models (Clemson Univ.)
• On-lattice configurational sampling (Cambridge Univ.)
• Reverse mapping back to all-atom polymer models
• Final equilibrated all-atom models• Bulk-phase models
• Surface-phase models
Approach: Molecular modeling of RESBIO polymers
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Development all-atom molecular models of RESBIOpolymers (existing all-atom polymer force field - PCFF)
• Application of advanced sampling methods• Translation of all-atom model into coarse-grained (CG) model
- Force field parameterization for CG models (Clemson Univ.)
• On-lattice configurational sampling (Cambridge Univ.)
• Reverse mapping back to all-atom polymer models
• Final equilibrated all-atom models• Bulk-phase models
• Surface-phase models
Approach: Molecular modeling of RESBIO polymers
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Development all-atom molecular models of RESBIOpolymers (existing all-atom polymer force field - PCFF)
• Application of advanced sampling methods• Translation of all-atom model into coarse-grained (CG) model
- Force field parameterization for CG models (Clemson Univ.)
• On-lattice configurational sampling (Cambridge Univ.)
• Reverse mapping back to all-atom polymer models
• Final equilibrated all-atom models• Bulk-phase models
• Surface-phase models
Approach: Molecular modeling of RESBIO polymers
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Coarse-Grained Model of DTB Succinate
Bond Bond Angle Dihedral Angle
Connection Label Connection Label Connection Label
PhF–Et1 l1 PhF-Et1-nEF 1 PhF-Et1-nEF-MeF 1
Et1–nEF l2 Et1-nEF-MeF 2 PhF-Et1-nEF-PhF 2
nEF–MeF l3 Et1-nEF-PhF 3 Et1-nEF-MeF-Pro 3
MeF–Pro l4 nEF-MeF-Pro 4 Et1-nEF-PhF-Et2 4
nEF–PhF l5 nEF-PhF-Et2 5 MeF-nEF-PhF-Et2 5
PhF–Et2 l6 MeF-nEF-PhF 6 Pro-MeF-nEF-PhF 6
PhF-Et2-PhF 7 nEF-PhF-Et2-PhF 7
Et2-PhF-Et1 8 PhF-Et2-PhF-Et1 8
Et2-PhF-Et1-nEF 9
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Mapping CG model onto Atomistic Model
DTB Succinate Tetramer
• Radius of gyrationModel Atomistic CG
<Rg> (Å) 7.66 ± 0.06 7.81 ± 1.49
• Displacement of the center of mass of as a function of time
0 2 4 6 8 10 120.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
D=0.302 (Å2/ns)
<[Rg
c(t
)-Rg
c(0
)]2
> (
Å2
)
Time (ns)0 2 4 6 8 10 12
0
100
200
300
400
D=36.444 (Å2/ns)
Time (ns)
Atomistic model CG model
[ ]2gcgc
t)0,z,y,x(R)t,z,y,x(R
t6
1 limD =
D=36.4 Å2/nsD=0.30 Å2/ns
Statistical Mechanics
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
DTB Succinate Tetramer
• Radius of gyrationModel Atomistic CG
<Rg> (Å) 7.66 ± 0.06 7.81 ± 1.49
• Displacement of the center of mass of as a function of time
0 2 4 6 8 10 120.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
D=0.302 (Å2/ns)
<[Rg
c(t
)-Rg
c(0
)]2
> (
Å2
)
Time (ns)0 2 4 6 8 10 12
0
100
200
300
400
D=36.444 (Å2/ns)
Time (ns)
Atomistic model CG model
1 CG time unit 120 atomistic time units
> 2 orders of magnitude acceleration
[ ]2gcgc
t)0,z,y,x(R)t,z,y,x(R
t6
1 limD =
Mapping CG model onto Atomistic Model
D=36.4 Å2/nsD=0.30 Å2/ns
Statistical Mechanics
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Development of equilibrated models of RESBIO polymers
– Bulk-phase models
– Water/drug transport and partitioning
– Surface-phase models
– Protein adsorption and cellular response
• Advanced descriptors: 3-D structure
– Interpolation within existing library
– Enhanced local optimization
• Molecular-level cause-and-effect relationships
– Guide for design of new libraries
– Global optimization
Concluding Remarks
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Development of equilibrated models of RESBIO polymers
– Bulk-phase models
– Water/drug transport and partitioning
– Surface-phase models
– Protein adsorption and cellular response
• Advanced descriptors: 3-D structure
– Interpolation within existing library
– Enhanced local optimization
• Molecular-level cause-and-effect relationships
– Guide for design of new libraries
– Global optimization
Concluding Remarks
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• Development of equilibrated models of RESBIO polymers
– Bulk-phase models
– Water/drug transport and partitioning
– Surface-phase models
– Protein adsorption and cellular response
• Advanced descriptors: 3-D structure
– Interpolation within existing library
– Enhanced local optimization
• Molecular-level cause-and-effect relationships
– Guide for design of new libraries
– Global optimization
Concluding Remarks
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Knowledge-Based Design of Polymeric
Biomaterials Beginning at the Atomic Level
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
Npp
0 1 2 3 4 5 6 7 8 ...0 1 2 3 4 5 6 7 ...
Npw
free
adsorbed
Density of states matrix contains
probabilities of all possible
macrostates (Npp, Npw)
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
• The Combinatorial-Computational Method for Biomaterials Optimization •• Integrated Technologies for Polymeric Biomaterials • funded by NIH EB001046 •
SEMIEMPIRICAL
MODEL