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IPASS: error tolerant NMR backbone resonance assignment by linear programming
, 2009
"... 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 nov ..."
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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 interresidue information and the 15 Nedited 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.
Contact Replacement for NMR Resonance Assignment
"... Motivation: Complementing its traditional role in structural studies of proteins, nuclear magnetic resonance (NMR) spectroscopy is playing an increasingly important role in functional studies. NMR dynamics experiments characterize motions involved in target recognition, ligand binding, etc., while N ..."
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Motivation: Complementing its traditional role in structural studies of proteins, nuclear magnetic resonance (NMR) spectroscopy is playing an increasingly important role in functional studies. NMR dynamics experiments characterize motions involved in target recognition, ligand binding, etc., while NMR chemical shift perturbation experiments identify and localize proteinprotein and proteinligand interactions. The key bottleneck in these studies is to determine the backbone resonance assignment, which allows spectral peaks to be mapped to specific atoms. This paper develops a novel approach to address that bottleneck, exploiting an available xray structure or homology model to assign the entire backbone from a set of relatively fast and cheap NMR experiments. Results: We formulate contact replacement for resonance assignment as the problem of computing correspondences between a contact graph representing the structure and an NMR graph representing the data; the NMR graph is a significantly corrupted, ambiguous version of the contact graph. We first show that by combining connectivity and amino acid type information, and exploiting the random structure of the noise, one can provably determine unique correspondences in polynomial time with high probability, even in the presence of significant noise (a constant number of noisy edges per vertex). We then detail an efficient randomized algorithm and show that, over a variety of experimental and synthetic datasets, it is robust to typical levels of structural variation (1–2 ˚A), noise (250–600%) and missings (10–40%). Our algorithm achieves very good overall assignment accuracy, above 80 % in αhelices, 70 % in βsheets, and 60% in loop regions. Availability: Our contact replacement algorithm is implemented in platformindependent Python code. The software can be freely obtained for academic use by request from the authors.
Towards Automated Structurebased NMR Assignment
"... 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 structu ..."
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Cited by 1 (1 self)
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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 structurebased 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 XiongPanduranganBaileyKellogg’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 peakpicking 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
Integer Programming Model for Automated Structurebased NMR Assignment
"... 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 structu ..."
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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 structurebased 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 XiongPanduranganBaileyKellogg’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 peakpicking 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
Can We Determine a Protein Structure Quickly?
, 2009
"... 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: • Xray crystallography. While this method has pr ..."
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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: • Xray crystallography. While this method has produced the most protein structures in the PDB (Protein Data Bank), the nasty trialanderror 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”.
Edson Crusca Junior. – Araraquara: [s.n], 2010 110 f.: il.
, 2010
"... peptídeo hilina a1 e análogos. ..."
A Hierarchical GrowandMatch Algorithm for Backbone Resonance Assignments Given 3D Structure
"... Abstract—This paper develops an algorithm for NMR backbone resonance assignment given a 3D structure and a set of relatively sparse 15 Nedited NMR data, with the throughspace 15 Nedited NOESY as the primary source of information. Our approach supports highthroughput solution studies of dynamics ..."
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Abstract—This paper develops an algorithm for NMR backbone resonance assignment given a 3D structure and a set of relatively sparse 15 Nedited NMR data, with the throughspace 15 Nedited NOESY as the primary source of information. Our approach supports highthroughput solution studies of dynamics and interactions (e.g., ligand binding), when the structure has previously been determined by crystallography or modeled computationally. We employ a graph matching approach, identifying correspondence between a given contact graph and a corrupted version representing the NMR data. Our hierarchical growandmatch algorithm decomposes the contact graph into sequential fragments with relatively dense interactions, and then combines possible assignments for the fragments, searching over the combinations with effective but conservative pruning. Our algorithm is complete, guaranteed to identify all solutions consistent with the data within a likelihood threshold of the optimal solution. It also deals correctly and uniformly with missing edges, which are quite common under this formulation. Tests on a number of experimental datasets and simulations with varying noise and sparsity demonstrate that our algorithm can handle significant data corruption (2.5–6.0 noisy edges per correct one) and sparsity (10–40 % of the correct edges missing). In addition to the reference solution, the complete ensembles include a number (up to 30) of alternatives. We use these complete ensembles to characterize confidence in parts of an assignment. I.