TEXTAL Progress

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 Cas using neural network, connect into
  chains
• Re-implement based on new tracing routine
• Does good job with 2Fo-Fc maps, secondary
  structure is apparent, RMSlt0.8A
• 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 doesnt 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
• ProbRfree(S)x FOM(S)y
• where S is result of running program on S
• ProbRfree(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)S
  v(S) x Prob(SS)
• 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