Determining Protein Topology from Skeletons of Secondary Structures

Wu, Yinghao; Chen, Mingzhi; Lu, Mingyang; Wang, Qinghua; Ma, Jianpeng

doi:10.1016/j.jmb.2005.04.064

Cited by 36 publications

(42 citation statements)

References 30 publications

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“…In many applications, the ability to accurately calculate the potential energy based solely on C α positions would certainly be useful. Typical examples are seen in recent studies on modeling protein chain topologies based on low-resolution density maps (Wu et al 2005a) and on coarse-grained folding simulations based on C α models (Wu et al 2005b).…”

Section: History Of Development Of Knowledge-based Potentialsmentioning

confidence: 99%

Statistical Contact Potentials in Protein Coarse-Grained Modeling: From Pair to Multi-body Potentials

Leelananda

Feng

Gniewek

et al. 2010

Multiscale Approaches to Protein Modeling

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The basic concepts of coarse-graining protein structures led to the introduction of empirical statistical potentials in protein computations. We review the history of the development of statistical contact potentials in computational biology and discuss the common features and differences between various pair contact potentials. Potentials derived from the statistics of non-bonded contacts in protein structures from the Protein Data Bank (PDB) are compared with potentials developed for threading purposes based on the optimization of the selection of the native structures among decoys. The energy of transfer of amino acids from water to a protein environment is discussed in detail. We suggest that a next generation of statistical contact potentials should include the effects of residue packing in proteins to improve predictions of protein native three-dimensional structures. We review existing multi-body potentials that have been proposed in the literature, including our own recent four-body potentials. We show how these are related to amino acid substitution matrices.

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Section: History Of Development Of Knowledge-based Potentialsmentioning

confidence: 99%

Statistical Contact Potentials in Protein Coarse-Grained Modeling: From Pair to Multi-body Potentials

Leelananda

Feng

Gniewek

et al. 2010

Multiscale Approaches to Protein Modeling

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“…In other words, the 3D path between successive helices in the protein sequence should follow high density regions in the volume. In the past, the helix correspondence problem has only been studied in the work of [Wu et al 2005], yet their method fails to take the density information into consideration.…”

Section: Problem Statementmentioning

confidence: 99%

Shape modeling and matching in identifying protein structure from low-resolution images

Abeysinghe

Chiu

et al. 2007

Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling

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Figure 1: Identifying α-helices in a low-resolution protein image, using the Human Insulin Receptor -Tyrosine Kinase Domain (1IRK) as an example. The inputs are the amino-acid sequence of the protein (a), where α-helices are highlighted in green, and a density volume reconstructed from electron cryomicroscopy (b), where possible locations of α-helices have been detected as cylinders shown in (c). Our method computes the correspondence between the helices in the sequence and in the density volume (e). This is achieved by extracting a skeleton from the density volume shown in (d) and matching it with the sequence in (a). Note that the matching is error-tolerant therefore the resulting correspondence does not have to be a bijection. AbstractIn this paper, we describe a novel, shape-modeling approach to recovering 3D protein structures from volumetric images. The input to our method is a sequence of α-helices that make up a protein, and a low-resolution volumetric image of the protein where possible locations of α-helices have been detected. Our task is to identify the correspondence between the two sets of helices, which will shed light on how the protein folds in space. The central theme of our approach is to cast the correspondence problem as that of shape matching between the 3D volume and the 1D sequence. We model both the shapes as attributed relational graphs, and formulate a constrained inexact graph matching problem. To compute the matching, we developed an optimal algorithm based on the A*-search with several choices of heuristic functions. As demonstrated in a suite of real protein data, the shape-modeling approach is capable of correctly identifying helix correspondences in noise-abundant volumes with minimal or no user intervention.

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“…20, 33 Wu et al enumerated all the topologies and then used geometrical screening to eliminate the less likely ones. 34 Another approach is to translate the problem into a graph matching problem aiming to find the optimal match of the two attributed related graphs. 35 One graph was created from the SSEs of the amino acid sequence.…”

Section: Introductionmentioning

confidence: 99%

Ranking Valid Topologies of the Secondary Structure Elements Using a Constraint Graph

Nasr

Ranjan

Zubair

et al. 2011

J. Bioinform. Comput. Biol.

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Electron cryo-microscopy is a fast advancing biophysical technique to derive three-dimensional structures of large protein complexes. Using this technique, many density maps have been generated at intermediate resolution such as 6-10 Å resolution. Although it is challenging to derive the backbone of the protein directly from such density maps, secondary structure elements such as helices and β-sheets can be computationally detected. Our work in this paper provides an approach to enumerate the top-ranked possible topologies instead of enumerating the entire population of the topologies. This approach is particularly practical for large proteins. We developed a directed weighted graph, the topology graph, to represent the secondary structure assignment problem. We prove that the problem of finding the valid topology with the minimum cost is NP hard. We developed an O(N(2)2(N)) dynamic programming algorithm to identify the topology with the minimum cost. The test of 15 proteins suggests that our dynamic programming approach is feasible to work with proteins of much larger size than we could before. The largest protein in the test contains 18 helical sticks detected from the density map out of 33 helices in the protein.

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Determining Protein Topology from Skeletons of Secondary Structures

Cited by 36 publications

References 30 publications

Statistical Contact Potentials in Protein Coarse-Grained Modeling: From Pair to Multi-body Potentials

Statistical Contact Potentials in Protein Coarse-Grained Modeling: From Pair to Multi-body Potentials

Shape modeling and matching in identifying protein structure from low-resolution images

Ranking Valid Topologies of the Secondary Structure Elements Using a Constraint Graph

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