Why molecular modeling?

1. Why molecular modeling? • Experiments are currently impossible • Eg Alzheimer’s Disease & protein aggregation • We need more detail • Polymerase, protein folding • We need results faster • Eg small molecule drug design

2. Challenges • Computer power • New hardware, new algorithms • Experiments to test • Experimentalists to become more familiar with computational methods

3. Recommendations • How to accelerate acceptance of modeling • Infrastructure • Education • More experiments, more systematically

5. Issues • The current perception of modeling in the biomedical and clinical research community, what needs to change to encourage more acceptance? • Future biomedical and clinical applications for models, based on current success stories, things that couldn't be solved w/o models, time to cure – e.g. comparative effectiveness research

6. 100000 TZ2 tu BBA5 (GB/SA) 10000 BBA5 (TIP3P) TZ2 tf villin (TIP3P) villin (GB/SA) Trp cage simulation (ns) 1000 alpha helix (Fs peptide) 100 PPA 10 1.0106 y = 0.7853x 2 R = 0.9684 1 1 10 100 1000 10000 100000 experiment (ns) Quantitative prediction of experiments • Important to us: • Validation • Model quality is still a challenge • Important for acceptance • Natural way to show a model’s value • Surprise: These ideas are not well accepted

7. Bond vibration Isomer- ation Water dynamics Helix forms Fastprotein folding Slowconf change proteinoligomerization 10-15 femto 10-12 pico 10-9 nano 10-6 micro 10-3 milli 100 seconds 103 seconds MD step long MD run where we’d love to be Major challenge at the atomic scale: long timescales • Fundamental problem for simulation • typical fast processor can simulate ~1 ns/day • a second of dynamics requires 1 billion CPU-days • even a millisecond is hard (million CPU-days) • however, scaling is hard -- not easy to break the problem down into a billion pieces

8. What can be done about this challenge?Two key advances in simulation methodology • New computational paradigms • Specialized hardware (GPU, Larrabee, Anton) • 100x to 1000x over a CPU • Cloud/Grid/HPC computing • Large unmet needs for computing resources • Enables greater data and model sharing • New algorithms, new approaches needed ATI 4870 (1000 GFlops peak, ~$200 + of a cost computer) For example: Folding@home ~400,000 CPUs ~8 petaflops total performance

9. NTL9 λ-repressor Solution: must combining technologies.Millisecond timescale today, seconds next seconds milliseconds villin microseconds

10. Natural areas to apply computational methods (especially when combined with HPC) • Alzheimer’s Disease • Small molecule inhibitors • Nature of misfolding • Viral infection • Critical mutants • Biophysical mechanism • Why are these natural areas to tackle with simulation? • experiments are extremely challenging • Simulations are easier in some ways Direct simulation ofAlzheimer’s Disease Direct simulation of Viral infection

11. “Useful” testable hypotheses WT after 1 day mutant after 14 days mutant after 30 days • Beyond validation • Combine validation and acceptance of models • Most useful for us if tests are themselves key results 200nm 100nm 100nm

12. For discussion • The current perception of modeling in the biomedical and clinical research community, what needs to change to encourage more acceptance? • going “over the top” in terms of connection to experiment • education on methods: many experimentalists can’t judge model quality • looking for experimentalists who need computational help

13. For discussion • Role of High Performance Computing (HPC) in biomedical computation • insatiable need, challenge in accessing • common approaches between scales will help in many ways (advances from one scale and transform another) • Additional traditional supercomputer resources • Ways to share distributed resources like Folding@home more broadly could transform several fields

15. Summary • a) How modeling has impacted various research fields (success stories and mechanisms)? Is it the onus on modelers to prove that their models are useful to someone else? • b) To what extent has the broader research communities accepted modeling as a critical tool for driving research or policy (what has worked and what hasn't worked)? • c) In what ways can modeling further impact the broader research communities (how far can we go)? • d) What are the major challenges to overcome (how do we get there)?

16. How modeling has impacted various research fields? • Culture exists in chemistry, natural impact • Biologists becoming more physical

17. Has the research communities accepted modeling as a critical tool for driving research • Interest

18. How modeling has impacted various research fields? • Culture exists in chemistry, natural impact • Biologists becoming more physical

20. Other success stories • computational drug design • Several labs and biotechs have examples • computation is not “all or nothing” but has become well integrated (almost silently) into many areas of drug design • protein design • inverse of protein folding: predict sequence from structure • numerous examples of successes • novel catalysts • SARS computational effort • also why failed -- push from failures • models too simple • sampling too little • many failures derive from too little computational power to achieve the desired goals

21. Broader impacts • Pharmaceutical company interest • specific collaborations (Acumen, Vertex, Numerate) • simulations driving experiments • understand limitations

22. A statistical approach to simulation:Markov State Models • Define states in the conformation space • Define transition probabilities between states • Utilize a large number of statistical sampling methods to do this very efficiently • Yields a full kinetic/thermodyanmic model of the original system