Protein Structure Comparison and Alignment Using Residue Contexts

Sayre, T.; Singh, Rahul

doi:10.1109/waina.2008.22

Cited by 3 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This direct approach to feature representation can be grouped into three general types: one based on the spatial atom distribution (Daras et al, 2006), a second on its topological structure (Anne, 2004), and a third on its geometrical shape (Sayre & Singh, 2008).…”

Section: Introductionmentioning

confidence: 99%

Protein classification using texture descriptors extracted from the protein backbone image

Nanni

Shi

Brahnam

et al. 2010

Journal of Theoretical Biology

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Protein classification using texture descriptors extracted from the protein backbone image

Nanni

Shi

Brahnam

et al. 2010

Journal of Theoretical Biology

View full text Add to dashboard Cite

show abstract

“…The indirect representation can be organized into two types: based on statistical analysis of amino acid residues [5][6][7][8], and based on amino acid indices [9,10]. Another approach executes directly analysis on protein spatial structure to obtain representation and extract feature of structure, and can be grouped into three types: based on spatial atom distribution [11,12], topological structure [13,14], and geometrical shape [15][16][17]. The indirect representation can be always obtained with lower computation cost and higher dimensional feature, but in contrast the direct representation can be acquired with higher computation cost and lower dimensional feature.…”

Section: Introductionmentioning

confidence: 99%

Fast SCOP Classification of Structural Class and Fold Using Secondary Structure Mining in Distance Matrix

Shi

Zhang

2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.It is an urgent need to understand the structure-function relationship in proteomic era. One of the important techniques to meet this demand is to analyze and represent the spatial structure of domain which is the functional unit of the whole protein, and perform fast domain classification. In this paper, we introduce a novel method of rapid domain classification. Instead of analyzing directly protein sequence or 3-D tertiary structure, the presented method maps firstly tertiary structure of protein domain into 2-D Cα-Cα distance matrix. Then, two distance functions for alpha helix and beta strand are modeled by considering their geometrical properties respectively. After that, the distance functions are further applied to mine secondary structure elements in such distance matrix with the way similar to image processing. Furthermore, composition feature and arrangement feature of secondary structure elements are presented to characterize domain structure for classification of structural class and fold in Structural Classification of Proteins (SCOP) database. Finally, the results compared with other methods show that the presented method can perform effectively and efficiently automatic classification of domain with the benefit of low dimension and meaningful features, but also no need of complicated classifier system.

show abstract

A novel approach to protein substructure matching

Vardharaj

Ranjan

Singh

et al. 2011

2011 World Congress on Information and Communication Technologies

View full text Add to dashboard Cite

The rapidly increasing volumes of structural data of proteins has led to need of algorithms which can rapidly predict functions for proteins based on structure. Similarity between protein structures can provide evidence of possible functional similarity. In this paper, an attempt is made to efficiently recognize similar protein structures in the protein database contain thousands of proteins. This paper gives an efficient heuristic algorithm for finding protein 3D substructures in a 3D protein structure that are similar to a given query 3D protein substructure. This algorithm can be used for searching a database of protein 3D structures. Our approach is to divide the protein structure into sub-structures of size of query structure and compare each sub structure with the query protein using Procrustes algorithm which is based on the root mean square distance between the structures. The division involves constructing a bounding box over both the query and protein structure and dividing the bigger box into sizes of the smaller box. The above algorithm is implemented in parallel using message passing interface. Experiments show that our algorithm can find similar 3D substructures in reasonable time. This paper also presents various statistics as how our algorithm performs against a sequential algorithm and how the algorithm performs with varying sizes of the query structure.

show abstract

Protein Structure Comparison and Alignment Using Residue Contexts

Abstract: We introduce a method for comparing protein structures using the notion of residue contexts based on protein Cα-atom

Cited by 3 publications

References 26 publications

Protein classification using texture descriptors extracted from the protein backbone image

Protein classification using texture descriptors extracted from the protein backbone image

Fast SCOP Classification of Structural Class and Fold Using Secondary Structure Mining in Distance Matrix

A novel approach to protein substructure matching

Contact Info

Product

Resources

About