1) How DOE type “big iron” computing could in principle help biology

1. 1) How DOE type “big iron” computing could in principle help biology (I am leading with things that I believe are sensible rather than starting from within the DOEs organismic and scientific constraints) A) Molecular dynamic type stuff, structure prediction, etc. B) Docking small molecules and proteins Small molecules to proteins “Guided docking” of proteins from starting compounds to easily synthesized derivative pharmacophores using genetic and biochemical QSAR information One grand challenge here: use computational methods to identify uM inhibitors of all the enzymes encoded by a microbial genome. You get new drug leads and it helps build up national rapid response capability, and it helps educate computer type people in DOE more about single major industrial application of biology Another: use the structural insight coupled with evolution to change specificity and catalytic properties of enzymes that make stuff (hydrogen, useful polymers), or break it down (cleanup) C) Vaccines Use sequence analysis and structure prediction to pick all the good B cell epitopes from a microbial genome. Use sequence analysis + structural information about human Class 1 and Class 2 to pick all the good T cell epitopes from a microbial genome. D) Any simulation work, particularly “whizzing molecule” simulations, should us would be simulationists succeed. Roger Brent

2. 2) Barriers to above, hardware, software, algorithms Yes 3) How would you measure success? a) Predicted structure of majority of proteins in newly sequenced bacterium or virus in one week of sequence, with predictions validated by experiment (2006) b) Validated lead drug compounds against new targets in virus or bacterium one year after work start (2005) c) Predicted vaccine one week after new microorganism sequenced, B and T cell epitopes going into validation steps (2006) d) Simulation would have to work, give nontrivial insight, be deemed to do so by majority of academic bioloogists, NIH, HHMI, and NAS (2010) 4) Resources a) Many questions seem to bear on simulation. Almost moot until simulation works. b) The structure/ drug/ vaccine ideas would require an increase in DOE internal competency. A 20 year commitment would be completely appropriate. NIH, NSF and industry fund some efforts along these lines now. A serious effort on tne the structure / drug/ vaccine front would require circa $1/2-$1B/ year, would probably need to be spent t a new, urban center rather than a national lab, and most of it wouldn’t be computation. Could be complementary with NIH. 5) Why undertake the work? a) Better security against biological attack on people, animals, plants, materiel, our ecology b) This capability is part of stewardship of the planetary ecology, with DOE handling the microbial ecology 6) ) A general consideration that would help MSI interact with DOE and DOE interact with the current research envirnoment outside of the national labs. ll DOE software should be open source under LGLP or equiv, all biological and chemical reagents freely licensed using standard academic “treaty type” MTAs. JGI delays data release for a year, not NIH or MRC/ Wellcome standard Roger Brent

3. From the DOE’s report on the GTL mathematics workshop “DOE’s current responsibility for remidiating 1.7 trillion gallonms of contaminated groundwater and 40 million cubic meters of contaminated soil demonstrates the significance and scale of the need for a new computational biology program” Most academic “biomedical” biologists won’t buy this, will consider it a non-sequitar. Roger Brent