Abstract. The automation of the entire NMR protein structure determination process requires a superior error tolerant backbone resonance assignment method. Although a variety of assignment approaches have been developed, none works well on noisy automatically picked peaks. IPASS is proposed as a novel integer linear programming (ILP) based assignment method. In order to reduce size of the problem, IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the 15 N-edited NOESY peaks which are then used to fix reliable fragments. The experimental results demonstrate that IPASS significantly outperforms the previous assignment methods on the synthetic data sets. It achieves an average of 99 % precision and 96 % recall on the synthesized spin systems, and an average of 96 % precision and 90 % recall on the synthesized peak lists. When applied on automatically picked peaks from experimentally derived data sets, it achieves an average precision and recall of 78 % and 67%, respectively. In contrast, the next best method, MARS, achieved an average precision and recall of 50 % and 40%, respectively. Availability: IPASS is available upon request, and the web server for IPASS is under construction.

Abstract Can we determine a high resolution protein structure quickly, say, in a week? I will show this is possible by the current technologies together with new computational tools discussed in this article. We have three potential paths to explore: • X-ray crystallography. While this method has produced the most protein structures in the PDB (Protein Data Bank), the nasty trial-and-error crystallization step remains to be an inhibitive obstacle. • NMR (Nuclear Magnetic Resonance) spectroscopy. While the NMR experiments are relatively easy to do, the interpretation of the NMR data for structure calculation takes several months on average. • In silico protein structure prediction. Can we actually predict high resolution structures consistently? If the predicted models remain to be labeled as “predicted”, and these structures still need to be experimentally verified by the wet lab methods, then this method at best can serve only as a screening tool. I investigate the question of “quick protein structure determination ” from a computer scientist point of view and actually answer the more relevant question “what can a computer scientist effectively contribute to this goal”.