Computational Science:
Computational Chemistry in the
FAMU Chemistry Department
Jesse Edwards
Associate Professor Chemistry
Florida A&M University
Tallahassee, FL 32307
June 15, 2010
MSEIP C-STEM Workshop
Computational Science
http://www.shodor.org/chemviz/overview/compsci.html
Computer Science and Chemistry
•Instrumentation/Computer
Interface
•Visualization
•Computational Chemistry
•Computer Aided Instruction
Computational
Chemistry
V (r ) 
N
 k /2(l  l
i
bonds
N

i1
i
io
 k /2(   )
) 
2
i
i
angles
i
2

V
n
F  ma
/2(1 cos(n  )
F  Vi
torsions

 12  6 


qq
 (4ij  r ij    r ij   4i jr )
0 ij
j i1
 ij   ij  
N
a
v
Mathematics , Physics, Chemistry Theories
v
x
Algorithms
x  a  t 2  vo  t  xo

More Theories

Properties
Structures
dv
d 2 ri
 m
dri
dt 2
dv

dt
 at  v o
dx

dt
 v  t  xo
Computational Chemistry
•Use of computers and algorithms based on chemistry and physics to predict
structures, and properties of chemical systems
•Properties Include:
•electronic structure determinations
•geometry optimizations
•frequency calculations
•transition structures
•protein calculations, i.e. docking
•electron and charge distributions
•potential energy surfaces (PES)
•rate constants for chemical reactions (kinetics)
•thermodynamic calculations- heat of reactions, energy of activation
•Molecular dynamics
•Conformational Energies
• Binding Energies
•Protein Folding
Best for
Method Type
Advantages
Molecular Mechanics
•Computationally least
intensive - fast and useful
•uses classical physics
with limited computer
•relies on force-field with
resources
embedded empirical
•can be used for
parameters
molecules as large as
enzymes
•particular force field
applicable only for a
limited class of molecules
•does not calculate
electronic properties
•requires experimental
data (or data from ab
initio) for parameters
Semi-Empirical
•uses quantum physics
•uses experimentally
derived empirical
parameters
•uses approximation
extensively
•requires experimental
data (or data from ab
initio) for parameters
•less rigorous than ab
initio) methods
Ab Initio
Disadvantages
•less demanding
computationally than ab
initio methods
•capable of calculating
transition states and
excited states
•useful for a broad range
•uses quantum physics
of systems
•mathematically rigorous, •does not depend on
no empirical parameters experimental data
•uses approximation
•capable of calculating
extensively
transition states and
excited states
•computationally
expensive
http://www.shodor.org/chemviz/overview/compsci.html
•large systems (thousands
of atoms)
•systems or processes
with no breaking or
forming of bonds
•medium-sized systems
(hundreds of atoms)
•systems involving
electronic transitions
•small systems (tens of
atoms)
•systems involving
electronic transitions
•molecules or systems
without available
experimental data ("new"
chemistry)
•systems requiring
rigorous accuracy
Mesoscale Modeling
Large scale
Coarse Grain Modeling
Engineering Applications
Edwards Group Research Projects
Drug Delivery Systems
Tissue Engineering Scaffolding
PEG ACID WITH CHOLESTEROL NMR
<-Synthetic Wet Lab->
(
Estrogen Receptor LBD
SERM’s
OH
i
HO
2
R
N
R
1H-NMR of PEG Maleic Cholesterol conjugate
HIV -1 Protease
OH
ii
)n
Tail H12
Loop
O
HCl salt
4-9
R = ethyl, methyl, isopropyl, morpholinyl, piperidinyl,
pyrrolidinyl
Reagents: i. alkylamino, sodium ethoxide stirred at reflux; ii. sat. HCl etherate
Rotated Image of Figure 2
Drug Discovery and Protein Folding
Molecular
Mechanics
And
Molecular
Dynamics
Common Molecular Mechanics Forcefield
Components
Non-bondedInteractions
P= - (A/r6) + (B/r12)
Ecol = E1E2
r
P
E
r
1/r
Coulombic Interaction
van der Waals
Molecular Dynamic Simulations of
the Estrogen Receptor a LBD
T. Dwight McGee Jr.1, Jesse Edwards1, Adrian E. Roitberg2
1Department of Chemistry, Florida A & M University, Tallahassee, FL,
32307.
2Department of Chemistry and Quantum Theory Project, University of
Florida, Gainesville, FL 32608
hERa Mechanism
Estradiol
Simulation of the Estrogen Receptor Ligand Binding Domain
Tail H12
Loop
Rotated Image of Figure 2
Overlay Structure Copeptide Helix 12
portion with LXXLL motif.
LXXLL/Copeptide Motif
Red Simulation Average Structure
Blue Antagonist Starting Structure
Summary of Dynamics
• Residue chain at the head of H12 begins an almost immediate
translation >10ns after the removal of the 4-hydoxytamoxifen.
• Residue chain at the end of H12 migrate towards the top of
Helices 3 and 4 and remain there.
• Residue chain at the beginning of H12 oscillates between the
antagonist (initial position) and the antagonist conformation
throughout the entire 121ns simulation.
Molecular Dynamic Study on the Conformational
Dynamics of HIV-1 Protease Subtype B vs. C
T. Dwight McGee Jr.
Florida A&M University
Global Effect of AIDS
Map shows HIV-1 subtype prevalence in 2002 based on Osmanov S, Pattou C, Walker N, Schwardlander
B, Esparza J; WHO-UNAIDS Network for HIV Isolation and Characterization. (2002) Estimated
global distribution and regional spread of HIV-1 genetic subtypes in the year 2000. J Acquir Immune
Defic Syndr. 29(2):184-90.
Purpose
The results gained from this project could help expand the
limited knowledge on the effects of PR C and aid the
improvement or the cultivation of new drugs.
Questions of Interest
1.
2.
How do these differences affect the size binding cavity?
How do these differences affect the flap orientation?
HIV Life Cycle
http://pathmicro.med.sc.edu/lecture/hivstage.gif
Semiopen
Closed
Open
Subtype B vs. C
T12S
I15V
L19I
M36I
S37A
H69K
L89M
I93L
X-ray Crystal Structure provided by Dunn et al.
Histogram of ILE50-ILE50
PR C- RED
PR B- BLACK
Histogram ASP25-ILE50
PR C- RED
PR B- BLACK
Molecular Modeling Studies of the Binding Characteristics
of Phosphates to Sevelamar Hydrochloride – Assessing a
Novel Technique to Reduce Phosphates Contamination
R. Parkera, J. Edwardsb, A. A. Odukalec, C. Batichc, E. Rossc
a Department of Industrial and Manufacturing Engineering FAMU/FSU College of Engineering
b Department of Chemistry Florida A&M University
c Department of Materials Engineering University of Florida
Our approach
• Sevelamar hydrochloride is used in Renagel® to
reduce the level of phosphates in the body.
• A Sevelamar hydrochloride-pyrrole composite can be
formed to build a self-monitoring phosphate
contamination system and removal system
• Molecular dynamics and monte carlo methods will
be used to determine key design parameters for the
composite system
Sevelamar hydrochloride
• a crosslinked poly(allylamine
hydrochloride)
• binds phosphates by ionic
interactions between protonated
amide groups along the polymer
backbone.
Structure;
a, b = number of primary amine groups
a+b =9;
c = number of cross-linking groups)
c= 1;
n = fraction of protonated amines)
n = 0.4;
m = large number to indicate extended polymer network
m
R. A. Swearingen, X. Chen, J. S. Petersen, K. S. Riley, D. Wang, E. Zhorov,
Determination of the Binding Parameter Constants of Renagel® Capsules and
Tablets Utilizing the Langmuir Approximation at Various pH by Ion Chromatograhpy,
Journal of Pharmaceutical and Biomedical Analysis, 2002, 29, 195-201
Objectives
• Build a system with high Phosphate binding
efficiency
• Understand how uptake and binding are affected by
pH, swelling, swelling & concentration of Phosphate
groups
• Understand binding efficiency and mechanism of
Phosphates with Sevelamer Hydrochloride
Observed Swelling due to pH
Observed swelling of dry particles…
…exposed to an acidic solution at pH = 1…
…followed by additional exposure
to a pH = 7 solution
Swelling of 50-70% at 1-hr exposure to a pH solution of 1 to 7.
Modeling Methods
Molecular Dynamics
• Used to determine average structure
• Means of capturing phosphates
Monte Carlo Simulations
• Determine the overall volume of model
system
• Compare results with swelling data
Modeled System
4 PO4
25% swelling observed within a single
molecule
Size of Monomer Unit (Angstrom Cubed)
923
1000
900
800
700
600
500
400
300
200
100
0
733
No Phosphates
4 Phosphates
Computational Studies of AntiTumor Agents
(Drug Discovery)
J. Edwards
J. Cooperwood
J. Robinson
Mindi L. Buckles
SERM’s Bond Rotational Barriers
CNT-Epoxy Resin Composites
Materials
•
•
•
•
D. Thomas, FAMU, Chemistry
R. Parker, 510nano Inc. Baltimore, MD
J. Edwards, FAMU, Chemistry
C. Liu, FAMU/FSU Engineering
Comparing Exp. To Simulation
Experiment (SEM Image
CNT-Epoxy Composite)
500
ps
Small Model
Simulation
Large Model
Simulation
Coarse-Grain Modeling of
Micelle Formation
(Drug Delivery)
Scott Shell, UCSB, Chemical
Engineering
J. Edwards, FAMU, Chemistry
Craig Hawker, UCSB, Chemisrty/MRL
Polymeric Micelle Systems for
Delivery of Steroidal Derivatives
Antoinette Addison2, Jos M.J. Paulusse1,Roey Amir1
Jesse Edwards2,Craig J. Hawker1
1Univeristy
2 Florida
of California at Santa Barbara, Materials Research
Laboratory, Santa Barbara CA93106
A&M University, College of Arts and Science, Tallahassee,
Florida 32307
Synthetic Strategy
 The reaction of poly (ethylene glycol) with various cyclic anhydrides
n
n
R = CH2, CH2-CH2, CH2-C-(CH3)2 .......
 Reacting the peg-acid with ethylcholorformate and attaching the
cholesterol
n
(
(
)n
)n
(
)n
Computation and Science
Education Research
• Using computer software to do analysis on
student performance
– Data driven pedagogy
– Data driven curriculum changes
A Formula for Success in General Chemistry:
Increasing Student Performance in a Barrier
Course
Dr. Jesse Edwards
Department of Chemistry
Florida A&M University
Jesse.edwards@famu.edu
Dr. Serena Roberts
Curriculum & Evidence Coordinator, Teachers for a New Era
Florida A&M University
Serena.roberts@famu.edu
Dr. Gita Wijesinghe Pitter
Associate Vice President, Institutional Effectiveness
Florida A&M University
Gita.pitter@famu.edu
Introduction
Florida A&M University is an 1890 land-grant HBCU with an enrollment of
approximately 12,000 students. Many of the students are first generation
in college and 66% are Pell grant recipients. The Chemistry Department at
Florida A&M University has taken on the serious challenge of addressing
poor performance in General Chemistry I (CHM 1045), a course for majors
in Chemistry and a required prerequisite course for majors in other natural
sciences, engineering, health professions, agriculture and science
education. The class sizes range from 30 – 140 students and there is no
teaching assistant support. An overwhelming majority of the students
taking General Chemistry I and II are freshman; however, a significant
number are more advanced students due to high repeat rates in the
course. During fall 2005 and fall 2006, the pass rates for CHM 1045 were
32% and 30% respectively. In an intensive effort to improve the pass
rates, the Department of Chemistry, in collaboration with the Teaching
Learning Institute, founded in part through a Teachers for a New Era grant,
a Carnegie Corporation of New York sponsored program, undertook a
variety of strategies to improve student learning and studied the impact.
The body of the paper describes the strategies which had a dramatic
impact. The paper also describes recent efforts to increase the pass rates
in General Chemistry II (CHM 1046), using study sessions that are based
on Bloom’s Taxonomy.
Correlated Variables (Correlated with Final Grade)
Study Hours
Pearson r Coefficient
0.07892
Planned Grade
0.349
High School Math and Science
0.352
Age
0.321
Science Fears
0.199
Work
-0.317
Study Groups
-0.129
High School Experience
-0.208
Chemistry Grades
0.109
Pass Placement Test
0.259
Weekend Activities
-0.136
Academic Scholarship
0.125
Classification
0.198
Parents’ Education
0.07865
Chemistry 1020 Grade
0.09963
Correlated Variables
Pearson r Coefficient
High School Math Science
0.384
Work
-0.283
Planned Grade
0.249
Science Fears
-0.349
Chemistry 1020
0.105
An Ever Improving Formula for Success in General
Chemistry: Increasing Student Performance in a
Barrier Course
Dr. Jesse Edwards
Department of Chemistry
Florida A&M University
Jesse.edwards@famu.edu
Ms. Christy Chatmon
Department of Computer and Information Systems
Florida A&M University
cchatmon@cis.famu.edu
Dr. Mark Howse
Associate Dean, College of Education
Florida A&M University
Mark.howse@famu.edu
Dr. Serena Roberts
Curriculum & Evidence Coordinator, Teachers for a New Era
Florida A&M University
Serena.roberts@famu.edu
COURSE
CHAPTERS
1
2
3
4
REVISED
Fundamentals of
Chemistry
CHM1020
X
X
X
X
ORIGINAL
Fundamentals of
Chemistry
CHM1020
X
X
X
X
X
X
General
Chemistry I
CHM1045
5
6
X
X
X
X
X
X
7
8
9
10
X
X
X
X
Attribute
Coef.
std
t(84)
p-value
Intercept
9.577252
11.313743
0.846515
0.399671
Classification
0.214665
0.915253
0.234542
0.815135
Age
-0.654987
0.958133
-0.683607
0.496105
Mother_Edu
-0.131065
0.404543
-0.323984
0.746756
Father_Edu
-0.282018
0.348493
-0.809249
0.420658
HS_Rating
-0.444184
0.801084
-0.554479
0.580725
1.705548
1.617547
1.054404
0.294721
-2.793239
1.539394
-1.814506
0.073170
Weekend_HomeTown
2.802901
1.596420
1.755741
0.082778
Weekend_Events
1.691546
1.627785
1.039170
0.301707
Weekend_Working
-1.941183
2.157021
-0.899937
0.370727
Weekend_Studying
-0.183461
1.925145
-0.095297
0.924306
Weekend_Relaxing
-1.652045
1.878853
-0.879284
0.381756
1.661499
1.198701
1.386082
0.169390
Took_GenCHM
-0.460392
2.777393
-0.165764
0.868741
Grade_GenCHM
-0.470130
0.573717
-0.819444
0.414852
Took_CHM1020
3.858988
2.614395
1.476054
0.143668
Grade_CHM1020
0.833647
0.488029
1.708194
0.091294
Worked_Enrolled
-1.232070
0.755338
-1.631151
0.106602
Hrs_Studied
0.779866
0.603342
1.292578
0.199701
Study_Time
0.042850
0.429707
0.099720
0.920804
Group_Study
0.447246
0.637228
0.701862
0.484705
Grade_Desire
-0.078313
0.712309
-0.109942
0.912718
Fear_Course
0.449181
1.311016
0.342621
0.732740
HS_EnjoyScience
HS_EnjoyMath
Academic_Scholarship
Attribute
Intercept
Coef.
std
t(84)
p-value
-14.006652
9.381113
-1.493069
0.139166
Classification
-0.786433
0.758908
-1.036270
0.303050
Age
-1.273700
0.794464
-1.603221
0.112640
Mother_Edu
0.200904
0.335438
0.598930
0.550832
Father_Edu
0.381887
0.288963
1.321578
0.189897
HS_Rating
0.015861
0.664242
0.023878
0.981006
HS_EnjoyScience
0.605478
1.341235
0.451433
0.652841
-1.057114
1.276432
-0.828178
0.409916
0.057828
1.323717
0.043686
0.965258
-1.462876
1.349724
-1.083834
0.281540
Weekend_Working
2.036179
1.788556
1.138449
0.258170
Weekend_Studying
1.909911
1.596289
1.196469
0.234880
Weekend_Relaxing
0.123657
1.557905
0.079374
0.936924
Academic_Scholarship
1.994659
0.993937
2.006826
0.047984
Took_GenCHM
-1.363995
2.302954
-0.592280
0.555254
Grade_GenCHM
-0.171405
0.475714
-0.360311
0.719519
Took_CHM1020
6.881522
2.167800
3.174426
0.002099
Grade_CHM1020
1.413573
0.404663
3.493212
0.000764
Worked_Enrolled
0.148654
0.626310
0.237349
0.812964
Hrs_Studied
0.661691
0.500278
1.322646
0.189543
Study_Time
0.467392
0.356304
1.311781
0.193168
Group_Study
-0.067746
0.528376
-0.128216
0.898285
Grade_Desire
-0.599127
0.590631
-1.014384
0.313313
Fear_Course
-0.197231
1.087066
-0.181434
0.856464
HS_EnjoyMath
Weekend_HomeTown
Weekend_Events
Attribute
Coef.
std
t(84)
p-value
Intercept
-3.622664
10.320577
-0.351014
0.726457
Classification
-0.357715
0.834908
-0.428449
0.669421
Age
-1.054274
0.874024
-1.206229
0.231115
Mother_Edu
0.054586
0.369031
0.147916
0.882763
Father_Edu
-0.118510
0.317901
-0.372789
0.710245
HS_Rating
-0.414277
0.730761
-0.566912
0.572286
1.826630
1.475552
1.237930
0.219191
-1.641537
1.404260
-1.168970
0.245722
0.574552
1.456280
0.394534
0.694187
Weekend_Events
-0.771327
1.484891
-0.519450
0.604813
Weekend_Working
-0.953065
1.967669
-0.484363
0.629389
Weekend_Studying
2.315495
1.756148
1.318508
0.190917
Weekend_Relaxing
0.029411
1.713920
0.017160
0.986350
Academic_Scholarship
1.650282
1.093474
1.509210
0.134998
Took_GenCHM
2.302886
2.533582
0.908945
0.365980
Grade_GenCHM
-0.788298
0.523354
-1.506242
0.135756
Took_CHM1020
3.007006
2.384893
1.260856
0.210852
Grade_CHM1020
1.076196
0.445187
2.417401
0.017795
Worked_Enrolled
-1.312521
0.689031
-1.904879
0.060218
Hrs_Studied
0.415488
0.550378
0.754913
0.452412
Study_Time
0.472366
0.391985
1.205060
0.231564
Group_Study
0.326373
0.581290
0.561464
0.575976
Grade_Desire
-0.356639
0.649779
-0.548862
0.584556
Fear_Course
0.601862
1.195930
0.503259
0.616100
HS_EnjoyScience
HS_EnjoyMath
Weekend_HomeTown
Attribute
Intercept
Coef.
std
t(84)
p-value
9.638107
21.762190
0.442883
0.658989
Classification
-0.527143
1.760505
-0.299427
0.765354
Age
-2.789317
1.842987
-1.513476
0.133912
Mother_Edu
0.836886
0.778146
1.075488
0.285236
Father_Edu
0.014554
0.670332
0.021711
0.982730
HS_Rating
0.926435
1.540899
0.601230
0.549306
HS_EnjoyScience
-1.507075
3.111380
-0.484375
0.629380
HS_EnjoyMath
-3.336111
2.961052
-1.126664
0.263093
Weekend_HomeTown
-1.720746
3.070744
-0.560368
0.576720
Weekend_Events
-0.501238
3.131073
-0.160085
0.873198
Weekend_Working
0.291631
4.149069
0.070288
0.944131
Weekend_Studying
7.490619
3.703052
2.022823
0.046272
Weekend_Relaxing
-3.003067
3.614008
-0.830952
0.408357
Academic_Scholarship
0.524296
2.305724
0.227389
0.820674
Took_GenCHM
3.152822
5.342365
0.590155
0.556671
Grade_GenCHM
-0.724360
1.103556
-0.656388
0.513369
Took_CHM1020
10.181885
5.028836
2.024700
0.046075
Grade_CHM1020
2.148517
0.938732
2.288745
0.024605
Worked_Enrolled
-3.386691
1.452905
-2.330978
0.022151
Hrs_Studied
0.236178
1.160539
0.203507
0.839231
Study_Time
1.075887
0.826549
1.301662
0.196591
Group_Study
0.328732
1.225720
0.268195
0.789207
Grade_Desire
-2.157592
1.370139
-1.574725
0.119079
Fear_Course
-1.161138
2.521763
-0.460447
0.646385
Attribute
Intercept
Coef.
std
t(99)
p-value
18.396112
9.108313
2.019706
0.046116
0.365923
1.431468
0.255628
0.798768
HS_EnjoyMath
-2.680548
1.376017
-1.948049
0.054241
Weekend_Working
-2.864158
1.793980
-1.596539
0.113556
Took_CHM1020
4.173953
2.343397
1.781155
0.077954
Grade_CHM1020
0.799879
0.437271
1.829254
0.070372
Worked_Enrolled
-0.990712
0.657794
-1.506113
0.135222
Grade_Desire
-0.243394
0.628051
-0.387538
0.699190
Fear_Course
-0.036081
1.207346
-0.029884
0.976219
HS_EnjoyScience
Attribute
Intercept
Coef.
std
p-value
8.472926
-0.948306
0.345283
0.528980
1.331610
0.397249
0.692039
HS_EnjoyScience
HS_EnjoyMath
t(99)
-8.034930
-0.399838
1.280027
-0.312366
0.755419
Weekend_Working
0.815636
1.668834
0.488746
0.626103
Took_CHM1020
6.989924
2.179924
3.206499
0.001810
Grade_CHM1020
1.410309
0.406767
3.467117
0.000780
Worked_Enrolled
0.054066
0.611907
0.088356
0.929772
Grade_Desire
-0.599739
0.584239
-1.026532
0.307143
Fear_Course
-0.103086
1.123123
-0.091785
0.927054
Attribute
Coef.
std
t(99)
p-value
Intercept
5.898188
8.418353
0.700634
0.485175
HS_EnjoyScience
0.970415
1.323033
0.733477
0.465001
HS_EnjoyMath
-1.092100
1.271783
-0.858716
0.392572
Weekend_Working
-1.790329
1.658085
-1.079757
0.282874
Took_CHM1020
2.374046
2.165883
1.096110
0.275690
Grade_CHM1020
1.106913
0.404147
2.738887
0.007312
Worked_Enrolled
-1.399156
0.607966
-2.301374
0.023467
Grade_Desire
-0.108972
0.580476
-0.187728
0.851474
Fear_Course
0.381603
1.115889
0.341972
0.733097
Attribute
Coef.
std
t(99)
p-value
Intercept
15.123846
17.654345
0.856664
0.393699
HS_EnjoyScience
-0.761362
2.774567
-0.274408
0.784343
HS_EnjoyMath
-3.565828
2.667089
-1.336974
0.184295
Weekend_Working
0.985681
3.477213
0.283469
0.777410
Took_CHM1020
8.972420
4.542130
1.975377
0.051010
Grade_CHM1020
2.110436
0.847548
2.490051
0.014438
Worked_Enrolled
-2.652377
1.274980
-2.080328
0.040078
Grade_Desire
-1.303498
1.217330
-1.070784
0.286870
Fear_Course
-0.834123
2.340160
-0.356438
0.722271
Acknowledgments
• Roitberg Group
• SEAGEP Program
• NIH/RCMI Faculty Development Award Grant 2 G12
RR003020-19, RCMI
• University of Florida Chemistry Department
• Quantum Theory Project
• Florida Supercomputer Center
• NCSA University of Illinois Urbana-Champaigne
• National Oceanic and Atmospheric Administration
(NOAA) Climate and Global Change program, CFDA
Number: 11.431
Additional Acknowledgements
•Student Participants
•Antoinette Addison (M.S. Candidate FAMU, Chemistry)
•T. Dwight McGee (PhD. Candidate U of F /QTP)
•Jamar Robinson (Recently Rickards High School)
•Dabrisha Thomas (Scientist, Dept. of Energy)
•Dr. Craig Hawker (UCSB MRL/MRFN program NSF award #
0520415)
•Dr. Adrian Roitberg (UF/QTP)
•Dr. Scott Shell UCSB
•Dr. John Cooperwood (FAMU)
•Dr. Anne Donnelly (AGEP-SEAGEP)
•FAMU Chemistry Department
•MSEIP-CSTEM (Dr. Hongmei Chi Principal Investigator)