Efficient SCOP-fold classification and retrieval using index-based protein substructure alignments

CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval

The number of entries in a structural database of proteins is increasing day by day. Methods for retrieving protein tertiary structures from such a large database have turn out to be the key to comparative analysis of structures that plays an important role to understand proteins and their functions. In this paper, we present fast and accurate methods for the retrieval of proteins having tertiary structures similar to a query protein from a large database. Our proposed methods borrow ideas from the field of computer vision. The speed and accuracy of our methods come from the two newly introduced features- the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient- and the use of Euclidean distance as the distance measure. Experimental results clearly indicate the superiority of our approach in both running time and accuracy. Our method is readily available for use from this website: http://research.buet.ac.bd:8080/Comograd/.

Effective moment feature vectors for protein domain structures

Imaging processing techniques have been shown to be useful in studying protein domain structures. The idea is to represent the pairwise distances of any two residues of the structure in a 2D distance matrix (DM). Features and/or submatrices are extracted from this DM to represent a domain. Existing approaches, however, may involve a large number of features (100-400) or complicated mathematical operations. Finding fewer but more effective features is always desirable. In this paper, based on some key observations on DMs, we are able to decompose a DM image into four basic binary images, each representing the structural characteristics of a fundamental secondary structure element (SSE) or a motif in the domain. Using the concept of moments in image processing, we further derive 45 structural features based on the four binary images. Together with 4 features extracted from the basic images, we represent the structure of a domain using 49 features. We show that our feature vectors can represent domain structures effectively in terms of the following. (1) We show a higher accuracy for domain classification. (2) We show a clear and consistent distribution of domains using our proposed structural vector space. (3) We are able to cluster the domains according to our moment features and demonstrate a relationship between structural variation and functional diversity.

Accelerating large-scale protein structure alignments with graphics processing units

Background

Large-scale protein structure alignment, an indispensable tool to structural bioinformatics, poses a tremendous challenge on computational resources. To ensure structure alignment accuracy and efficiency, efforts have been made to parallelize traditional alignment algorithms in grid environments. However, these solutions are costly and of limited accessibility. Others trade alignment quality for speedup by using high-level characteristics of structure fragments for structure comparisons.

Findings

We present ppsAlign, a parallel protein structure Alignment framework designed and optimized to exploit the parallelism of Graphics Processing Units (GPUs). As a general-purpose GPU platform, ppsAlign could take many concurrent methods, such as TM-align and Fr-TM-align, into the parallelized algorithm design. We evaluated ppsAlign on an NVIDIA Tesla C2050 GPU card, and compared it with existing software solutions running on an AMD dual-core CPU. We observed a 36-fold speedup over TM-align, a 65-fold speedup over Fr-TM-align, and a 40-fold speedup over MAMMOTH.

Conclusions

ppsAlign is a high-performance protein structure alignment tool designed to tackle the computational complexity issues from protein structural data. The solution presented in this paper allows large-scale structure comparisons to be performed using massive parallel computing power of GPU.

ProteinDBS v2.0: a web server for global and local protein structure search

ProteinDBS v2.0 is a web server designed for efficient and accurate comparisons and searches of structurally similar proteins from a large-scale database. It provides two comparison methods, global-to-global and local-to-local, to facilitate the searches of protein structures or substructures. ProteinDBS v2.0 applies advanced feature extraction algorithms and scalable indexing techniques to achieve a high-running speed while preserving reasonably high precision of structural comparison. The experimental results show that our system is able to return results of global comparisons in seconds from a complete Protein Data Bank (PDB) database of 152,959 protein chains and that it takes much less time to complete local comparisons from a non-redundant database of 3276 proteins than other accurate comparison methods. ProteinDBS v2.0 supports query by PDB protein ID and by new structures uploaded by users. To our knowledge, this is the only search engine that can simultaneously support global and local comparisons. ProteinDBS v2.0 is a useful tool to investigate functional or evolutional relationships among proteins. Moreover, the common substructures identified by local comparison can be potentially used to assist the human curation process in discovering new domains or folds from the ever-growing protein structure databases. The system is hosted at http://ProteinDBS.rnet.missouri.edu.