Computational Simulation of Alzheimer’s Amyloid Fibril Assembly

1. Computational Simulation of Alzheimer’s Amyloid Fibril Assembly Phil LoCascioPavan GhattyvenkatakrishnaEd Uberbacher Genome Analysis and Systems Modeling Biosciences Division Research supported by the Department of Energy’s Office of ScienceOffice of Advanced Scientific Computing Research

2. Goal: understanding B-amyloid self-assembly Amyloid peptides Ordered fibrils and plaques Aggregates Chain extension

3. Fundamental amyloid assembly questions remain • What interactions and energies drive initial aggregation • How do initial aggregates achieve regular structures • How does chain growth occur • Where are the strategic steps and places where effective drugs could be attached Assembly questions remain

4. ORNL research program Visualize B-aggregate assembly by MD • Visualize the assembly of amyloid B 40 and 42 to protofibrils: followed by growth • Look at forces that cause aggregation and key steps • Look at process of structure regularization to protofiber • Look at fiber growth mechanisms and potential for growth repression • Contrast assembly in B 40 with B 42 • Expect to reach the 100-nanosecond regime for multiple runs

5. Initial amyloid folding Hydrophobic interactions cause the amyloid peptide to fold into a hairpin-like structure in about 1 nanosecond

6. Initial amyloid peptide contact • Hydrophobic interactions and hydrogen bonds are formed in initial contact between amyloid peptides • These virtually always keep the peptides from coming apart • The process takes about 5 nanoseconds

7. Amyloid aggregation • Hydrophobic interactions and hydrogen bonds are formed in initial contact between amyloid peptides • These virtually always keep the peptides from coming apart • This process takes about 15 nanoseconds

8. Preventing fibril assembly in simulation • Drug and natural compounds are being evaluated using their effects on fibril assembly • Ab initio methods are used to build drug structure • Empirical model is placed in standard molecular mechanics code

9. Summary • Unique capability to simulate amyloid molecular assemblies • Tools and infrastructure for partnering with other researchers using computational modeling • Advanced computational drug binding studies 9 Uberbacher_AAFA_0611

10. Contacts Philip F. LoCascio Biological and Environmental Sciences, Genome Analysis and Systems Modeling (865) 574-4567 locasciop@ornl.gov Pavan Ghattyvenkatakrishna Biological and Environmental Sciences, Genome Analysis and Systems Modeling (865) 574-8916 ghattyvenkpk@ornl.gov Edward C. Uberbacher Biological and Environmental Sciences, Genome Analysis and Systems Modeling (865) 574-6134 uberbacherec@ornl.gov 10 Uberbacher_AAFA_0611