* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Question 1 - University of Missouri
Protein mass spectrometry wikipedia , lookup
Protein domain wikipedia , lookup
Protein design wikipedia , lookup
Protein folding wikipedia , lookup
Implicit solvation wikipedia , lookup
Protein–protein interaction wikipedia , lookup
Rosetta@home wikipedia , lookup
Nuclear magnetic resonance spectroscopy of proteins wikipedia , lookup
Homology modeling wikipedia , lookup
Discussion of Protein Disorder Prediction Jianlin Cheng University of Missouri, Columbia, MO, USA (MULTICOM-CMFR & MULITCOM) Question 1 In you analysis of disorder do you treat short disordered regions, e.g. a missing loop in a crystal structure, differently than a disordered domain or an entirely disordered protein? No. Two reasons (laziness and principle) Question 2 Can you briefly describe your disorder analysis, i.e. is it based on physical principals, machine learning or a combination of both? Machine learning – 1D-Recursive Neural Network Input: sequence profile, predicted secondary structure, relative solvent accessibility Output: disorder (+), order (-) Question 3 Does your analysis of disorder prediction affect your template free modeling, i.e. does the disorder prediction aid your free model prediction? If so, in what way, in practice, did you use your disorder prediction for free modeling? Occasionally. T0500 (800 residues) Should be useful for both template-based and template-free modeling Question 4 Can your disorder prediction distinguish between regions predicted to be fully disordered, i.e. 'cooked spaghetti', or alternatively an ensemble of a few alternative conformations? Maybe. Strength of signal? Disorder Ensemble • Some disorder regions may be not fully disordered. • Most likely a discrete distribution of a number of conformations • Disorder regions switch from one conformation to another according to probability NMR to Determine Ensemble Conformations New NMR techniques can gather local conformations and long-range interactions even under strongly denaturing conditions to obtain plausible all-atom models of the unfolded state at increasing accuracy. S. Meier et al. J. Chemical Physics, 2008 Energy Landscape of Ordered Globular Protein Chan and Dill, Nature Structure Biology, 1997 Energy Landscape of Disordered Regions Shallow, unstable energy landscape Contacts in Ensemble • Essential contacts (conserved long-range interactions) • Non-essential contacts (transient contacts) • How to predict essential contacts? Prediction of Ensemble • Protein conformation space is significantly reduced due to essential contacts • Predict ensemble conformations using template-free modeling • Predict ensemble conformations using constrained molecular dynamics