Rapid retrieval of protein structures from databases

Fingerprinting protein structures effectively and efficiently

MOTIVATION

One common task in structural biology is to assess the similarities and differences among protein structures. A variety of structure alignment algorithms and programs has been designed and implemented for this purpose. A major drawback with existing structure alignment programs is that they require a large amount of computational time, rendering them infeasible for pairwise alignments on large collections of structures. To overcome this drawback, a fragment alphabet learned from known structures has been introduced. The method, however, considers local similarity only, and therefore occasionally assigns high scores to structures that are similar only in local fragments.

METHOD

We propose a novel approach that eliminates false positives, through the comparison of both local and remote similarity, with little compromise in speed. Two kinds of contact libraries (ContactLib) are introduced to fingerprint protein structures effectively and efficiently. Each contact group of the contact library consists of one local or two remote fragments and is represented by a concise vector. These vectors are then indexed and used to calculate a new combined hit-rate score to identify similar protein structures effectively and efficiently.

RESULTS

We tested our method on the high-quality protein structure subset of SCOP30 containing 3297 protein structures. For each protein structure of the subset, we retrieved its neighbor protein structures from the rest of the subset. The best area under the Receiver-Operating Characteristic curve, archived by ContactLib, is as high as 0.960. This is a significant improvement compared with 0.747, the best result achieved by FragBag. We also demonstrated that incorporating remote contact information is critical to consistently retrieve accurate neighbor protein structures for all- query protein structures.

AVAILABILITY AND IMPLEMENTATION

https://cs.uwaterloo.ca/∼xfcui/contactlib/.

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.

Multi-view methods for protein structure comparison using latent dirichlet allocation

Motivation: With rapidly expanding protein structure databases, efficiently retrieving structures similar to a given protein is an important problem. It involves two major issues: (i) effective protein structure representation that captures inherent relationship between fragments and facilitates efficient comparison between the structures and (ii) effective framework to address different retrieval requirements. Recently, researchers proposed vector space model of proteins using bag of fragments representation (FragBag), which corresponds to the basic information retrieval model.

Results: In this article, we propose an improved representation of protein structures using latent dirichlet allocation topic model. Another important requirement is to retrieve proteins, whether they are either close or remote homologs. In order to meet diverse objectives, we propose multi-viewpoint based framework that combines multiple representations and retrieval techniques. We compare the proposed representation and retrieval framework on the benchmark dataset developed by Kolodny and co-workers. The results indicate that the proposed techniques outperform state-of-the-art methods.

Availability:http://www.cse.iitm.ac.in/~ashishvt/research/protein-lda/.

Contact:ashishvt@cse.iitm.ac.in

Searching protein 3-D structures in linear time

One of the most important issues in the post-genomic molecular biology is the analysis of protein three-dimensional (3-D) structures, and searching over the 3-D structure databases of them is becoming more and more important. The root mean square deviation (RMSD) is the most popular similarity measure for comparing two molecular structures. In this article, we propose new theoretically and practically fast algorithms for the basic problem of finding all the substructures of structures in a structure database of chain molecules (such as proteins), whose RMSDs to the query are within a given constant threshold. The best-known worst-case time complexity for the problem is O(N log m), where N is the database size and m is the query size. The previous best-known expected time complexity for the problem is also O(N log m). We also propose a new breakthrough linear-expected-time algorithm. It is not only a theoretically significant improvement over previous algorithms, but also a practically faster algorithm, according to computational experiments. Our experiments over the whole Protein Data Bank (PDB) database show that our algorithm is 3.6-28 times faster than previously known algorithms, to search for similar substructures whose RMSDs are within 1A to queries of ordinary lengths. We also propose a series of preprocessing algorithms that enable faster queries, though there have been no known indexing algorithm whose query time complexity is better than the above O(N log m) bound. One is an O(N log(2)N)-time and O(N log N)-space preprocessing algorithm with expected query time complexity of O(m + N given complex square root of m). Another is an O(N log N)-time and O(N)-space preprocessing algorithm with expected query time complexity of O(N given complex square root of m + m log (N given m)).(1)

FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately

Fast identification of protein structures that are similar to a specified query structure in the entire Protein Data Bank (PDB) is fundamental in structure and function prediction. We present FragBag: An ultrafast and accurate method for comparing protein structures. We describe a protein structure by the collection of its overlapping short contiguous backbone segments, and discretize this set using a library of fragments. Then, we succinctly represent the protein as a "bags-of-fragments"-a vector that counts the number of occurrences of each fragment-and measure the similarity between two structures by the similarity between their vectors. Our representation has two additional benefits: (i) it can be used to construct an inverted index, for implementing a fast structural search engine of the entire PDB, and (ii) one can specify a structure as a collection of substructures, without combining them into a single structure; this is valuable for structure prediction, when there are reliable predictions only of parts of the protein. We use receiver operating characteristic curve analysis to quantify the success of FragBag in identifying neighbor candidate sets in a dataset of over 2,900 structures. The gold standard is the set of neighbors found by six state of the art structural aligners. Our best FragBag library finds more accurate candidate sets than the three other filter methods: The SGM, PRIDE, and a method by Zotenko et al. More interestingly, FragBag performs on a par with the computationally expensive, yet highly trusted structural aligners STRUCTAL and CE.

Linear-time protein 3-D structure searching with insertions and deletions

BACKGROUND

Two biomolecular 3-D structures are said to be similar if the RMSD (root mean square deviation) between the two molecules' sequences of 3-D coordinates is less than or equal to some given constant bound. Tools for searching for similar structures in biomolecular 3-D structure databases are becoming increasingly important in the structural biology of the post-genomic era.

RESULTS

We consider an important, fundamental problem of reporting all substructures in a 3-D structure database of chain molecules (such as proteins) which are similar to a given query 3-D structure, with consideration of indels (i.e., insertions and deletions). This problem has been believed to be very difficult but its exact computational complexity has not been known. In this paper, we first prove that the problem in unbounded dimensions is NP-hard. We then propose a new algorithm that dramatically improves the average-case time complexity of the problem in 3-D in case the number of indels k is bounded by a constant. Our algorithm solves the above problem for a query of size m and a database of size N in average-case O(N) time, whereas the time complexity of the previously best algorithm was O(Nm(k+1)).

CONCLUSIONS

Our results show that although the problem of searching for similar structures in a database based on the RMSD measure with indels is NP-hard in the case of unbounded dimensions, it can be solved in 3-D by a simple average-case linear time algorithm when the number of indels is bounded by a constant.

Progress in the PRIDE technique for rapidly comparing protein three-dimensional structures

Background

Accurate and fast tools for comparing protein three-dimensional structures are necessary to scan and analyze large data sets.

Findings

The method described here is not only very fast but it is also reasonable precise, as it is shown by using the CATH database as a test set. Its rapidity depends on the fact that the protein structure is represented by vectors that monitors the distribution of the inter-residue distances within the protein core and the structure of which is optimized with the Freedman-Diaconis rule.

Conclusion

The similarity score is based on a χ² test, the probability density function of which can be accurately estimated.