Jakub Galgonek

SProt: sphere-based protein structure similarity algorithm

BackgroundSimilarity search in protein databases is one of the most essential issues in computational proteomics. With the growing number of experimentally resolved protein structures, the focus shifted from sequences to structures. The area of structure similarity forms a big challenge since even no standard definition of optimal structure similarity exists in the field.ResultsWe propose a protein structure similarity measure called SProt. SProt concentrates on high-quality modeling of local similarity in the process of feature extraction. SProt’s features are based on spherical spatial neighborhood of amino acids where similarity can be well-defined. On top of the partial local similarities, global measure assessing similarity to a pair of protein structures is built. Finally, indexing is applied making the search process by an order of magnitude faster.ConclusionsThe proposed method outperforms other methods in classification accuracy on SCOP superfamily and fold level, while it is at least comparable to the best existing solutions in terms of precision-recall or quality of alignment.

Density-based classification of protein structures using iterative TM-score

Finding similarity between a pair of protein structures is one of the fundamental tasks in many areas of bioinformatical research such as protein structure prediction, function mapping, etc. We propose a method for finding pairing of amino acids based on densities of the structures and we also propose a modification to the original TM-score rotation algorithm that assess similarity score to this alignment. Proposed modification is faster than TM and comparably robust according to non-optimal parts in the alignment. We measure the qualities of the algorithm in terms of SCOP classification accuracy. Regarding the accuracy, our solution outperforms the contemporary solutions at two out of three tested levels of the SCOP hierarchy.

On the Effectiveness of Distances Measuring Protein Structure Similarity

Determining similarity between two protein structures is one of the most fundamental problems in contemporary structural bioinformatics. With the increasing complexity of the measures, their effectiveness increases as well. However, other important observables, such as the degree of metric properties fulfilment, could rather deteriorate than improve. In this paper we introduce an effective measure and study its degree of metric properties fulfilment.

SimTandem: similarity search in tandem mass spectra

SimTandem is a tool for fast identification of protein and peptide sequences from tandem mass spectra. The identification is based on similarity search of spectra captured by a tandem mass spectrometer in databases of theoretical mass spectra generated from databases of known protein sequences. Since the number of protein sequences in the databases grows rapidly and a sequential scan over the entire database of spectra is time-consuming, the non-metric access methods are employed as the database indexing techniques. SimTandem is based on a previously proposed method and is freely available at http://www.simtandem.org or http://www.siret.cz/simtandem.

On the parallelization of the SProt measure and the TM-Score algorithm

Similarity measures for the protein structures are quite complex and require significant computational time. We propose a parallel approach to this problem to fully exploit the computational power of current CPU architectures. This paper summarizes experience and insights acquired from the parallel implementation of the SProt similarity method, its database access method, and also the wellknown TM-score algorithm. The implementation scales almost linearly with the number of CPUs and achieves 21.4× speedup on a 24-core system. The implementation is currently employed in the web application http://siret.cz/p3s.

SMILE: a framework for semantic applications

Even though the semantic web become actual topic of research recently, there are no complex solutions for building semantic applications yet to our best knowledge. We describe a complex architecture which covers storing, querying, manipulation and visualization of semantic data in a programmer-friendly way. The architecture is currently being applied in a real-life web portal.

SProt - from local to global protein structure similarity

Similarity search in protein databases is one of the most essential issues in proteomics. With the growing number of experimentally solved protein structures, the focus shifted from sequence to structure. The area of structure similarity forms a big challenge since even no standard definition of optimal similarity exists in the field. In this paper, we propose a protein structure similarity method called SProt. SProt concentrates on high-quality modeling of local similarity in the process of feature extraction. SProts features are based on spherical spatial neighborhood where similarity can be well defined. On top of the partial local similarities, global measure assessing similarity to a pair of protein structures is built. SProt outperforms other methods in classification accuracy, while it is at least comparable to the best existing solutions in terms of precision-recall or quality of alignment.

ALIGNMENT-BASED EXTENSION TO DDPIN FEATURE EXTRACTION

Finding similarity between a pair of protein structures is one of the fundamental tasks in many areas of bioinformatical research such as protein structure prediction, function mapping, etc. We propose a method for finding pairing of amino acids based on densities of the structures and we also propose a modification to the original template modeling-score (TM-Score) rotation algorithm that assess similarity score to this alignment. Proposed modification is faster than TM and comparably robust according to non-optimal parts in the alignment. We measure the qualities of the algorithm in terms of structural classification of proteins (SCOP) classification accuracy. Regarding the accuracy, our solution outperforms the contemporary solutions at two out of three tested levels of the SCOP hierarchy.

P3S: protein structure similarity search

Similarity search in protein structure databases is an important task of computational biology. To reduce the time required to search for similar structures, indexing techniques are being often introduced. However, as the indexing phase is computationally very expensive, it becomes useful only when a large number of searches are expected (so that the expensive indexing cost is amortized by cheaper search cost). This is a typical situation for a public similarity search service. In this article we introduce the P3S web application (http://siret.cz/p3s) allowing, given a query structure, to identify the set of the most similar structures in a database. The result set can be browsed interactively, including visual inspection of the structure superposition, or it can be downloaded as a zip archive. P3S employs the SProt similarity measure and an indexing technique based on the LAESA method, both introduced recently by our group. Together with the measure and the index, the method presents an effective and efficient tool for querying protein structure databases.

Protein sequences identification using NM-tree

We have generalized a method for tandem mass spectra interpretation, based on the parameterized Hausdorff distance dHP. Instead of just peptides (short pieces of proteins), in this paper we describe the interpretation of whole protein sequences. For this purpose, we employ the recently introduced NM-tree to index the database of hypothetical mass spectra for exact or fast approximate search. The NM-tree combines the M-tree with the TriGen algorithm in a way that allows to dynamically control the retrieval precision at query time. A scheme for protein sequences identification using the NM-tree is proposed.