Current Status and Future Directions for TEXTAL March 2, 2003 The TEXTAL Group at Texas A&M: Thomas R. Ioerger James C. Sacchettini Tod Romo Kreshna Gopal Reetal Pai Vinod

Current model-building capabilities: targeting “medium-res” maps ( A) builds 80-90% of models in medium-quality maps typically 2-10 chains RMSD error ~1A for C-alphas and side-chains a.a. identity accuracy –near % for “good” maps recommended: make 2.8A map around 1 molecule time: several hours (depends on size of map)

Progress Update Integration with PHENIX Sequence Alignment –almost there (Tod) –80-100% acc. for good maps (IF5a, CzrA: hi-res data) –still face challenges with lower-quality density incorrect chain connections cause confusion truncated/unrepresentative side-chain density New algorithms: –connecting chain-breaks using fragment lookup (Reetal) –new density-scaling technique (experimental) –feature-weighting algorithm (Kreshna)

Current Developments Trying different databases –real vs. back-transformed maps (phase error?) –different resolutions! –clustering (rotamers, Kmeans, SVD, reduce size) –have to recalculate feature vectors and feature weights iterating between model-building and phase refinement –(not much progress since Sept...)

Graphics Desired: an interactive interface to TEXTAL –semi-automated, rather than black-box –give advice to user, but get their help –generate alternatives, but let them decide Approach: small targeted apps –special-purpose, customized graphical tools –e.g. link Capra chains, select better residues... Extend to VR and other interfaces

Textal Assistance

Virtual Benefits Vestibular cues reinforce stereo cues Immersive environment fosters rapid understanding of complex topologies Enabling technology for “low spatial” people Collaborative tool –Shared virtual environment –CAVElib support for connecting remote cave sessions

Longer-term Ideas Take structure factors as input, generate maps Extensions of pattern-recognition –apply to nucleotides, carbohydrates? –can use same features capturing shape of density? –train neural network to recognize phosphodiester bonds same as C-alpha’s –use linear/helical geometric constraints to build backbone; then fit bases like LOOKUP –how to distinguish DNA/RNA from protein?

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