New enumeration algorithm for protein structure comparison and classification

Ashby, Cody; Johnson, Daniel; Walker, Karl; Kanj, Iyad A.; Xia, Ge; Huang, Xiuzhen

doi:10.1186/1471-2164-14-s2-s1

Cited by 6 publications

(8 citation statements)

References 26 publications

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“…A slight variation of p-OLSE was considered in [3,25], where the linear order imposed on G and H was replaced with a partial order (directed acyclic graphs); the problem was referred to as the Graph Embedding problem in [3] and as the Generalized Subgraph Isomorphism problem in [25]. The aforementioned problems were mainly studied in [3,25] assuming no bound on ∆ H and ∆ G and, not surprisingly, only hardness results were derived. In [25], a parameterized algorithm with respect to the treewidth of G and the map width ∆ L combined was given.…”

Section: Previous Related Resultsmentioning

confidence: 99%

“…In [25], a parameterized algorithm with respect to the treewidth of G and the map width ∆ L combined was given. Most of the hardness results in [3,25] were obtained by a direct reduction from the Independent Set or Clique problems. For example, it was shown in [3] that the problem of embedding the whole graph G into H is N P-hard, but is in P if ∆ L = 2.…”

Section: Previous Related Resultsmentioning

confidence: 99%

“…The p-OLSE and p-OLISE problems, and their optimization versions opt-OLSE and opt-OLISE, have applications in the area of Bioinformatics because they model numerous protein and DNA structural comparison problems (see [3,8,9,25]).…”

Section: Introduction 1problem Definition and Motivationmentioning

confidence: 99%

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On the Ordered List Subgraph Embedding Problems

Hassan

Kanj

Lokshtanov

et al. 2013

Parameterized and Exact Computation

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In the (parameterized) Ordered List Subgraph Embedding problem (p-OLSE) we are given two graphs G and H, each with a linear order defined on its vertices, a function L that associates with every vertex in G a list of vertices in H, and a parameter k. The question is to decide if we can embed (one-to-one) a subgraph S of G of k vertices into H such that: (1) every vertex of S is mapped to a vertex from its associated list, (2) the linear orders inherited by S and its image under the embedding are respected, and (3) if there is an edge between two vertices in S then there is an edge between their images. If we require the subgraph S to be embedded as an induced subgraph, we obtain the Ordered List Induced Subgraph Embedding problem (p-OLISE). The p-OLSE and p-OLISE problems model various problems in Bioinformatics related to structural comparison/alignment of proteins.We investigate the complexity of p-OLSE and p-OLISE with respect to the following structural parameters: the width ∆ L of the function L (size of the largest list), and the maximum degree ∆ H of H and ∆ G of G. In terms of the structural parameters under consideration, we draw a complete complexity landscape of p-OLSE and p-OLISE (and their optimization versions) with respect to the computational frameworks of classical complexity, parameterized complexity, and approximation.

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Section: Previous Related Resultsmentioning

confidence: 99%

Section: Previous Related Resultsmentioning

confidence: 99%

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On the Ordered List Subgraph Embedding Problems

Hassan

Kanj

Lokshtanov

et al. 2013

Parameterized and Exact Computation

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“…Thus protein structure comparison methods are indispensable. Article S1 by Xiuzhen Huang et al (Arkansas State University) describes ePC, an accurate and fast algorithm that is able to compare whole structures as well as specific substructures [38]. As detailed in article S4 [39], Prasad Gajula (Indian Agricultural Statistics Research Institute) presented a molecular dynamics simulation of the protein Vinculin and showed that the simulation is highly consistent with local mobility as determined experimentally by electron paramagnetic resonance (EPR) spectroscopy.…”

Section: Protein Structure and Functionmentioning

confidence: 99%

Summary of talks and papers at ISCB-Asia/SCCG 2012

et al. 2013

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The second ISCB-Asia conference of the International Society for Computational Biology took place December 17-19, 2012, in Shenzhen, China. The conference was co-hosted by BGI as the first Shenzhen Conference on Computational Genomics (SCCG).45 talks were presented at ISCB-Asia/SCCG 2012. The topics covered included software tools, reproducible computing, next-generation sequencing data analysis, transcription and mRNA regulation, protein structure and function, cancer genomics and personalized medicine. Nine of the proceedings track talks are included as full papers in this supplement.In this report we first give a short overview of the conference by listing some statistics and visualizing the talk abstracts as word clouds. Then we group the talks by topic and briefly summarize each one, providing references to related publications whenever possible. Finally, we close with a few comments on the success of this conference.

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“…Several other methods have been proposed from shape-based descriptors (Røgen and Fain, 2003) to knot-fittinginspired methods (Erdmann, 2005) to qualify and quantify structural similarity. Automated protein structural classification methods have relied on machine learning (Jain and Hirst, 2010), clustering and graph theory based approaches (Ashby et al, 2013;Kim and Patel, 2006). Sequence based protein classification techniques have focused on identifying common motifs and domains and using these to cluster them together.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of protein structures using physicochemical parameters

Mohan

Rao

Sunderrajan

et al. 2014

Interdiscip Sci Comput Life Sci

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Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

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New enumeration algorithm for protein structure comparison and classification

Cited by 6 publications

References 26 publications

On the Ordered List Subgraph Embedding Problems

On the Ordered List Subgraph Embedding Problems

Summary of talks and papers at ISCB-Asia/SCCG 2012

Automatic classification of protein structures using physicochemical parameters

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