1. TEXTAL Progress Basic modeling of side-chain and backbone coordinates seems to be working well. –even for experimental MAD maps, 2.5-3A –using pattern-recognition with feature-extracted database –assuming C-alpha coordinates are correct Use sequence-alignment to match fragments after prediction & correct identities

2. CAPRA Progress Picks C  ’s using neural network, connect into chains Re-implement based on new tracing routine Does good job with 2Fo-Fc maps, secondary structure is apparent, RMS<0.8A Has harder time with low-quality maps Sec. Str. recognition from trace geometry

3. Prelim. Design for Xtal Agent Decision-making in structure solution Which program to use? Params? Iterations? PHASES, SOLVE, SHARP, DM, TNT, CNS, TEXTAL, WARP… Local decision-making: input params, when to stop iterating - for 1 program at a time Try a statistical approach (Terwilliger)

4. Global Decision-Making When to back-track? What to make of information gained by exploring 1 path? Example: select initial, conservative mask for solvent-flattening; if doesn’t lead to good model, go back and re-flatten When to “throw out” data (e.g. low FOM)? Use NCS or not? Alternative paths compete

5. AI Search Problem Choice-points form branches in tree Initial data collection at root Try to find path (sequence of computational actions) that produces a solved structure Question: when to continue down one path versus re-start from a previous branch-pt?

6. Sequential Decision Procedures Branch of Decision Theory Focus on utility of information gained in earlier steps to make better choices later Attempt to optimize “long-term payoff” Define a target utility function that measures model goodness, e.g. combination of Rfree, completeness, consistency...

7. Parameter Estimation Need quantitative estimates of probabilistic effects of running a program on quality of model Fit equations from synthetic experiments: –Prob[Rfree(S’)=x | FOM(S)=y] –where S’ is result of running program on S –Prob[Rfree*(flatten(S,50%))=x | Rfree*(flatten(S,40%))=y]

8. Utility and Risk Utility of an action A is integral over expected value of outcomes, weighted by prob: U(S,A)=  v(S’) x Prob(S’|S) Can use to compare different actions & states, provided v is “final model quality” Risk-aversion: modify the values in integral to prefer possibility of higher reward over average loss - for handling uncertainty

9. Computational Cost Why not just run all programs with all params? Want to minimize CPU time. At any given moment, pick the action that produces a state with highest expected utility minus estimated cost of runtime: –gain: G(A,S)=U(A,S)-f(T(A,S)) –where T(A,S) is estimated time to run A on S –and f(.) correlates effort to model quality scale