Computational Science: Computational Chemistry in the

FAMU Chemistry Department

Jesse EdwardsAssociate Professor Chemistry

Florida A&M University Tallahassee, FL 32307

June 15, 2010MSEIP 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

F maF Vi

dv

drim

d2ridt 2

advdt

v at vo

v dx

dtx v t xox a t 2 vo t xo

V (rN ) bonds

ki /2(li lio)2 ki /2(i i)

2 Vn /2(1 cos(n torsions

angles

)

i1

N

(4ijji1

N

ij

rij

12

ij

rij

6

qiq j

40rij)

Mathematics , Physics, Chemistry Theories

Algorithms

Properties

StructuresM

ore

Theo

ries

Computational Chemistry

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

Method Type Advantages Disadvantages Best for

Molecular Mechanics •uses classical physics •relies on force-field with embedded empirical parameters

•Computationally least intensive - fast and useful with limited computer resources •can be used for 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

•large systems (thousands of atoms) •systems or processes with no breaking or forming of bonds

Semi-Empirical

•uses quantum physics •uses experimentally derived empirical parameters •uses approximation extensively

•less demanding computationally than ab initio methods •capable of calculating transition states and excited states

•requires experimental data (or data from ab initio) for parameters •less rigorous than ab initio) methods

•medium-sized systems (hundreds of atoms) •systems involving electronic transitions

Ab Initio

•uses quantum physics •mathematically rigorous, no empirical parameters •uses approximation extensively

•useful for a broad range of systems •does not depend on experimental data •capable of calculating transition states and excited states

•computationally expensive

•small systems (tens of atoms) •systems involving electronic transitions •molecules or systems without available experimental data ("new" chemistry) •systems requiring rigorous accuracy

http://www.shodor.org/chemviz/overview/compsci.html

Mesoscale Modeling

Large scaleCoarse Grain Modeling

Engineering Applications

Edwards Group Research ProjectsTissue Engineering Scaffolding

PEG ACID WITH CHOLESTEROL NMR

1H-NMR of PEG Maleic Cholesterol conjugate

( )n

Drug Delivery Systems

Tail H12 Loop

Rotated Image of Figure 2

OH

HO

2

i

ii

OH

ONR

R 4-9HCl salt

Reagents: i. alkylamino, sodium ethoxide stirred at reflux; ii. sat. HCl etherate

R = ethyl, methyl, isopropyl, morpholinyl, piperidinyl,pyrrolidinyl

Drug Discovery and Protein Folding

Estrogen Receptor LBD

SERM’s HIV -1 Protease

<-Synthetic Wet Lab->

Molecular Mechanics

And Molecular Dynamics

Common Molecular Mechanics Forcefield Components

Non-bonded Interactions

P

r

P= - (A/r6) + (B/r12)

van der WaalsCoulombic Interaction

1/r

Ecol = E1E2

r

E

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

hER Mechanism

Estradiol

Tail H12 Loop

Rotated Image of Figure 2

Simulation of the Estrogen Receptor Ligand Binding Domain

Overlay Structure Copeptide Helix 12 portion with LXXLL motif.

Red Simulation Average StructureBlue Antagonist Starting Structure

LXXLL/Copeptide Motif

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 Interest1. How do these differences affect the size binding cavity?

2. 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

X-ray Crystal Structure provided by Dunn et al.

T12S

I15V

L19I

M36I

S37A

H69K

L89M

I93L

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 Universityc 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.

NH2.nHCl

aOH

NH2.HClNH2

.HCl

b CStructure; 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

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

m

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

Swelling of 50-70% at 1-hr exposure to a pH solution of 1 to 7.

Observed swelling of dry particles… …exposed to an acidic solution at pH = 1…

…followed by additional exposure to a pH = 7 solution

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 System4 PO4

25% swelling observed within a single molecule

No Phosphates 4 Phosphates0

200

400

600

800

1000

733923

Size of Monomer Unit (Angstrom Cubed)

Computational Studies of Anti-Tumor Agents

(Drug Discovery)J. Edwards

J. CooperwoodJ. 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

500 ps

Experiment (SEM Image CNT-Epoxy Composite)

Small Model Simulation

Large Model Simulation

Coarse-Grain Modeling of Micelle Formation

(Drug Delivery)Scott Shell, UCSB, Chemical

EngineeringJ. 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 of California at Santa Barbara, Materials Research Laboratory, Santa Barbara CA93106

2 Florida A&M University, College of Arts and Science, Tallahassee, Florida 32307

Synthetic Strategy

n n

Reacting the peg-acid with ethylcholorformate and attaching the cholesterol

The reaction of poly (ethylene glycol) with various cyclic anhydrides

R = CH2, CH2-CH2, CH2-C-(CH3)2 .......

( )

( )

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 EdwardsDepartment of ChemistryFlorida A&M University

Jesse.edwards@famu.eduDr. Serena Roberts

Curriculum & Evidence Coordinator, Teachers for a New EraFlorida A&M University

Serena.roberts@famu.eduDr. Gita Wijesinghe Pitter

Associate Vice President, Institutional EffectivenessFlorida A&M UniversityGita.pitter@famu.edu

 

IntroductionFlorida 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)

Pearson r Coefficient

Study Hours 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 EdwardsDepartment of ChemistryFlorida A&M University

Jesse.edwards@famu.eduMs. Christy Chatmon

Department of Computer and Information SystemsFlorida A&M University

cchatmon@cis.famu.eduDr. Mark Howse

Associate Dean, College of EducationFlorida A&M UniversityMark.howse@famu.edu

Dr. Serena RobertsCurriculum & Evidence Coordinator, Teachers for a New Era

Florida A&M UniversitySerena.roberts@famu.edu

COURSE CHAPTERS

1

2

3

4

5

6

7

8

9

10

REVISEDFundamentals of Chemistry CHM1020 X X X X

ORIGINALFundamentals of Chemistry CHM1020 X X X X X X

General Chemistry I CHM1045 X X X X X X 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

HS_EnjoyScience 1.705548 1.617547 1.054404 0.294721

HS_EnjoyMath -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

Academic_Scholarship 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

Attribute Coef. std t(84) p-value

Intercept -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

HS_EnjoyMath -1.057114 1.276432 -0.828178 0.409916

Weekend_HomeTown 0.057828 1.323717 0.043686 0.965258

Weekend_Events -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

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

HS_EnjoyScience 1.826630 1.475552 1.237930 0.219191

HS_EnjoyMath -1.641537 1.404260 -1.168970 0.245722

Weekend_HomeTown 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

Attribute Coef. std t(84) p-value

Intercept 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 Coef. std t(99) p-value

Intercept 18.396112 9.108313 2.019706 0.046116

HS_EnjoyScience 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

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-valueIntercept -8.034930 8.472926 -0.948306 0.345283HS_EnjoyScience 0.528980 1.331610 0.397249 0.692039HS_EnjoyMath -0.399838 1.280027 -0.312366 0.755419Weekend_Working 0.815636 1.668834 0.488746 0.626103Took_CHM1020 6.989924 2.179924 3.206499 0.001810Grade_CHM1020 1.410309 0.406767 3.467117 0.000780Worked_Enrolled 0.054066 0.611907 0.088356 0.929772Grade_Desire -0.599739 0.584239 -1.026532 0.307143Fear_Course -0.103086 1.123123 -0.091785 0.927054

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)