ProteinDBS: a real-time retrieval system for protein structure comparison

Shyu, Chi‐Ren; Chi, Pin-Hao; Scott, Grant; Xu, Dong

doi:10.1093/nar/gkh436

Cited by 29 publications

(21 citation statements)

References 12 publications

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“…ProteinDBS uses computer vision techniques to align two protein chains based on the two-dimensional distance matrix generated from the 3D coordinates of the alpha-carbon atoms on the protein backbones. The two chains 1o7j.a and 1hfj.c are examples given in the ProteinDBS paper 16 . According to the query result from the ProteinDBS website, the seven chains (1hfj.c, 1qd1.b, 1toh, 4eca.c, 1d9q.d, 4eca.b, 4eca.d) have global tertiary structures most similar to 1o7j.a.…”

Section: The Experimentsmentioning

confidence: 99%

Protein Structure-Structure Alignment With Discrete Fréchet Distance

Jiang

Zhu

2007

Proceedings of the 5th Asia-Pacific Bioinformatics Conference

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Matching two geometric objects in 2D and 3D spaces is a central problem in computer vision, pattern recognition and protein structure prediction. In particular, the problem of aligning two polygonal chains under translation and rotation to minimize their distance has been studied using various distance measures. It is well known that the Hausdorff distance is useful for matching two point sets, and that the Fréchet distance is a superior measure for matching two polygonal chains. The discrete Fréchet distance closely approximates the (continuous) Fréchet distance, and is a natural measure for the geometric similarity of the folded 3D structures of bio-molecules such as proteins. In this paper, we present new algorithms for matching two polygonal chains in 2D to minimize their discrete Fréchet distance under translation and rotation, and an effective heuristic for matching two polygonal chains in 3D. We also describe our empirical results on the application of the discrete Fréchet distance to the protein structure-structure alignment.

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Section: The Experimentsmentioning

confidence: 99%

Protein Structure-Structure Alignment With Discrete Fréchet Distance

Jiang

Zhu

2007

Proceedings of the 5th Asia-Pacific Bioinformatics Conference

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“…Another comparison technique based on computer vision algorithm was published by Shyu an co-workers [82] and was made available as a web-based server that allows one to retrieve information from the Protein Data Bank or from e SCOP database. The technique was validated against several hundreds of SCOP entries and shows a precision of 84% at the 10% recall rate, and of 49% at the 100% recall rate.…”

Section: Proteindbsmentioning

confidence: 99%

Recent Progress in Measuring Structural Similarity Between Proteins

Carugo

2007

CPPS

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Surprisingly, after at least two decades of researches focused on the analysis of the similarity between protein three-dimensional structures, several new comparison methods were proposed during the last few years. These are briefly summarized and commented below. Some of the newly developed techniques are fast and were designed to handle large amounts of data and vast structural databases. Other methods are much slower and were developed to gain biological information by comparing distantly related protein structures. Additional studies were devoted to the problem of the multiple structural alignments and to strategies of comparison between alternative structural alignments.

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“…A much faster search engine for protein structure comparison, ProteinDBS [121], was developed recently. Based on the alignment between the query protein structure and the hits in the structure database, one may find biologically interesting similarities that are not detectable by sequence comparison or threading.…”

Section: Structure-structure Comparisonmentioning

confidence: 99%

Computational Methods for Remote Homolog Identification

Wan

2005

CPPS

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As more and more protein sequences are available, homolog identification becomes increasingly important for functional, structural, and evolutional studies of proteins. Many homologous proteins were separated a very long time ago in their evolutionary history and thus their sequences share low sequence identity. These remote homologs have become a research focus in bioinformatics over the past decade, and some significant advances have been achieved. In this paper, we provide a comprehensive review on computational techniques used in remote homolog identification based on different methods, including sequence-sequence comparison, and sequence-structure comparison, and structure-structure comparison. Other miscellaneous approaches are also summarized. Pointers to the online resources of these methods and their related databases are provided. Comparisons among different methods in terms of their technical approaches, their strengths, and limitations are followed. Studies on proteins in SARS-CoV are shown as an example for remote homolog identification application.

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ProteinDBS: a real-time retrieval system for protein structure comparison

Cited by 29 publications

References 12 publications

Protein Structure-Structure Alignment With Discrete Fréchet Distance

Protein Structure-Structure Alignment With Discrete Fréchet Distance

Recent Progress in Measuring Structural Similarity Between Proteins

Computational Methods for Remote Homolog Identification

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