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.

a protein sequence, and its NMR spectra, automatically interpret the NMR spectra and do backbone resonance assignment. We then propose a solution to solve this problem. The core of the solution is a novel integer linear programming model, which is a general framework for many versions of the structure-based assignment problem. As a proof of concept, our system has generated an automatic assignment on a real protein TM1112 with 91 % recall and 99 % precision, starting from scratch. When we restrict ourselves to the special case where perfect peak lists are given, we are able to compare our results with existing results in the field. In particular, we reduced the assignment error of Xiong-Pandurangan-Bailey-Kellogg’s method by 5 folds on average, with over a thousand fold speed up. Our system also achieves 91 % assignment accuracy on real experimental data for Ubiquitin. These results have direct practical implications. For example, in the protein design process, a protein is modified slightly and its structure is again measured by NMR experiments. Our method automates this process, saving time on tedious peak-picking and resonance assignment. As another example, when there is a homologous protein with known structure, our method increases the assignment accuracy and hence enables automated NMR structure determination. ⋆ The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. The NMR resonance assignment problem has been extensively studied for twenty years [1–19]. Traditional

a protein sequence, and its NMR spectra, automatically interpret the NMR spectra and do backbone resonance assignment. We then propose a solution to solve this problem. The core of the solution is a novel integer linear programming model, which is a general framework for many versions of the structure-based assignment problem. As a proof of concept, our system has generated an automatic assignment on a real protein TM1112 with 91 % recall and 99 % precision, starting from scratch. When we restrict ourselves to the special case where perfect peak lists are given, we are able to compare our results with existing results in the field. In particular, we reduced the assignment error of Xiong-Pandurangan-Bailey-Kellogg’s method by 5 folds on average, with over a thousand fold speed up. Our system also achieves 91 % assignment accuracy on real experimental data for Ubiquitin. These results have direct practical implications. For example, in the protein design process, a protein is modified slightly and its structure is again measured by NMR experiments. Our method automates this process, saving time on tedious peak-picking and resonance assignment. As another example, when there is a homologous protein with known structure, our method increases the assignment accuracy and hence enables automated NMR structure determination. ⋆ The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. The NMR resonance assignment problem has been extensively studied for twenty years [1–19]. Traditional

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”.

Abstract. All practical contemporary protein NMR structure determination methods use molecular dynamics coupled with a simulated annealing schedule. The objective of these methods is to minimize the error of deviating from the NOE distance constraints. However, this objective function is highly nonconvex and, consequently, difficult to optimize. Euclidean distance geometry methods based on semidefiniteprogramming(SDP)provideanaturalformulationfor thisproblem.However,complexityof SDPsolversandambiguousdistance constraintsare major challenges tothisapproach. Thecontribution of this paper is to provide a new SDP formulation of this problem that overcomes these two issues for the first time. We model the protein as a set of intersecting two- and three-dimensional cliques, then we adapt and extend a technique called semidefinite facial reduction to reduce the SDP problem size to approximately one quarter of the size of the original problem. The reduced SDP problem can not only be solved approximately 100 times faster, but is also resistant to numerical problems from having erroneous and inexact distance bounds.

WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering