Computational Science: Computational Chemistry in the FAMU Chemistry Department

1. 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

2. Computational Science http://www.shodor.org/chemviz/overview/compsci.html

3. Computer Science and Chemistry Instrumentation/Computer Interface Visualization Computational Chemistry Computer Aided Instruction

4. Computational Chemistry Mathematics , Physics, Chemistry Theories Algorithms More Theories Properties Structures

5. 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

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

7. Mesoscale Modeling Large scale Coarse Grain Modeling Engineering Applications

8. Edwards Group Research Projects Drug Delivery Systems Tissue Engineering Scaffolding <-Synthetic Wet Lab-> Estrogen Receptor LBD SERM’s HIV -1 Protease Drug Discovery and Protein Folding

9. Molecular Mechanics And Molecular Dynamics

10. Common Molecular Mechanics Forcefield Components

11. P= - (A/r6) + (B/r12) Ecol = E1E2 r P E r 1/r Coulombic Interaction van der Waals Non-bondedInteractions

14. Molecular Dynamic Simulations of the Estrogen Receptor a LBD T. Dwight McGee Jr.1, Jesse Edwards1, Adrian E. Roitberg21Department of Chemistry, Florida A & M University, Tallahassee, FL, 32307.2Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL 32608

15. hER Mechanism

16. Estradiol

17. Simulation of the Estrogen Receptor Ligand Binding Domain

18. Overlay Structure Copeptide Helix 12 portion with LXXLL motif. LXXLL/Copeptide Motif Red Simulation Average Structure Blue Antagonist Starting Structure

19. 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.

20. Molecular Dynamic Study on the Conformational Dynamics of HIV-1 Protease Subtype B vs. C T. Dwight McGee Jr. Florida A&M University

21. 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.

22. 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 • How do these differences affect the size binding cavity? • How do these differences affect the flap orientation?

23. HIV Life Cycle http://pathmicro.med.sc.edu/lecture/hivstage.gif

24. Semiopen Closed Open

25. Subtype B vs. C T12S I15V L19I M36I S37A H69K L89M I93L X-ray Crystal Structure provided by Dunn et al.

26. Histogram of ILE50-ILE50 PR C- RED PR B- BLACK

27. Histogram ASP25-ILE50 PR C- RED PR B- BLACK

28. 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

29. 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

30. 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

31. 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

32. 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.

33. 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

34. Modeled System4 PO4

35. 25% swelling observed within a single molecule

36. Computational Studies of Anti-Tumor Agents(Drug Discovery) J. Edwards J. Cooperwood J. Robinson Mindi L. Buckles

37. SERM’s Bond Rotational Barriers

38. CNT-Epoxy Resin Composites Materials • D. Thomas, FAMU, Chemistry • R. Parker, 510nano Inc. Baltimore, MD • J. Edwards, FAMU, Chemistry • C. Liu, FAMU/FSU Engineering

39. 500 ps Comparing Exp. To Simulation Experiment (SEM Image CNT-Epoxy Composite) Small Model Simulation Large Model Simulation

40. Coarse-Grain Modeling of Micelle Formation(Drug Delivery) Scott Shell, UCSB, Chemical Engineering J. Edwards, FAMU, Chemistry Craig Hawker, UCSB, Chemisrty/MRL

41. 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

42. 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

43. Computation and Science Education Research • Using computer software to do analysis on student performance • Data driven pedagogy • Data driven curriculum changes

44. 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

45. 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.