Institute of Informatics

Background: Due to the growing number of biomedical entries in data repositories of the National Center for Biotechnology Information (NCBI), it is difficult to collect, manage and process all of these entries in one place by third-party software developers without significant investment in hardware and software infrastructure, its maintenance and administration. Web services allow development of software applications that integrate in one place the functionality and processing logic of distributed software components, without integrating the components themselves and without integrating the resources to which they have access. This is achieved by appropriate orchestration or choreography of available Web services and their shared functions. After the successful application of Web services in the business sector, this technology can now be used to build composite software tools that are oriented towards biomedical data processing.

The analysis of structural and energy features of proteins can be a key to understand how proteins work and interact to each other in cellular reactions. Potential energy is a function of atomic positions in a protein structure. The distributions of energy over each atom in protein structures can be very supportive for the studies of the complex processes proteins are involved in. Energy profiles contain distributions of different potential energies in protein molecular structures. Therefore, they constitute a full descriptor of energy properties for protein structures. The Energy Distribution Data Bank (EDB, http://edb.aei.polsl.pl) stores energy profiles for protein molecular structures retrieved from the well-known Protein Data Bank. In the paper, we describe the purpose of the EDB, a possible use of the information stored in it, query possibilities, and plans for future development.

The Energy Distribution Markup Language (EDML) is an XML-based format that we have designed and developed to exchange protein structure energy profiles between many computers and users. Energy profiles that are distributions of various potential energies over consecutive atoms in protein molecular structures can be downloaded from the Energy Distribution Data Bank (EDB, http://edb.aei.polsl.pl) in the EDML format. In the paper, we describe the purpose of the EDML, a possible use of energy profiles, and internal structure of documents created in the EDML format.

Since protein structure similarity searching is very complex and time-consuming, one of the possible acceleration methods is parallelization by distributing the calculation on multiple computers. In the paper, we present a theoretical model of the hierarchical multi-agent system dedicated to the task of protein structure similarity searching. We also show results of several numerical experiments confirming a suitability of such distribution for the similarity searching performed for the Muconate Lactonizing Enzyme (PDB ID = 1MUC) from the Protein Data Bank (PDB) against the database containing almost thousand randomly chosen molecules.

The comparative analysis of structural and energy features of proteins can be a key to understand how proteins work and interact to each other in cellular reactions. Potential energy, which is a function of atomic positions in a protein structure, can be used to describe molecule's abilities to interact with other biological molecules. Potential energy characteristics are supportive in searching similar, biologically important molecular regions that have similar functions in different organisms or organs. In the paper, we present the similarity searching method, which base on the alignment of energy patterns. In the alignment phase energy patterns are represented as sequences of fuzzy numbers. This increases the fault-tolerance of the alignment and ensures the approximate character of the method. The alignment of fuzzy sequences is one of the main steps in the new FN-EAST method (fuzzy numbers - energy alignment search tool). The FN-EAST allows to seek similar structural regions represented as energy patterns. The new alignment incorporated in the similarity searching eliminates some weaknesses of the FN-EAST predecessors.

Protein structure has a big impact on the protein's activity in the living cells. Conformational modifications as an effect of biochemical reactions or environmental influences cause changes in the conformational energy distributions. Therefore, observations of the energy characteristics allow to detect structural changes of selected proteins. Moreover, having entire sets of energy characteristics it is possible to compare given protein structure to other structures or search for similar structures in the set. In the paper, we present the method of similarity searching based on energy profiles. This method can be employed to detect protein structure modifications. In the method, we use distance matrices to find the alignment between two proteins on the energy level. Energy profiles are received in the computational process based on the molecular mechanics theory. Afterwards, these profiles can be stored in the special purpose database and used by the search engine. In the solution, additional protein features are extracted and stored. They may be used in the searching process in the preselection phase. At the end of the paper, we give some examples of the system usage regarding our recent research on protein kinases and their activity.

Secondary structure representation of proteins provides important information regarding protein general construction and shape. This representation is often used in protein similarity searching. Since existing commercial database management systems do not offer integrated exploration methods for biological data e.g. at the level of the SQL language, the structural similarity searching is usually performed by external tools. In the paper, we present our newly developed PSS-SQL language, which allows searching a database in order to identify proteins having secondary structure similar to the structure specified by the user in a PSS-SQL query. Therefore, we provide a simple and declarative language for protein structure similarity searching.

In the paper, we discuss the usage of constant and fuzzy similarity awards while establishing an optimal alignment between energy characteristics of two compared protein energy profiles. Single protein energy profile is a set of energy characteristics of various types of energy. The energy profile is determined for a given protein structure. We use these profiles to find protein molecules of the same structural protein family and inspect conformational modifications in their molecular structures as an effect of biochemical reactions or environmental influences. Energy profiles are received in the computational process based on the molecular mechanics theory. Afterwards, these profiles can be stored in the special purpose database (EDB) and used by the search engine to find similar fragments of protein structures on the energy level. To optimize the alignment path we use modified, energy-adapted Smith-Waterman method with one of the tested similarity awards.

Analysis of new trends in databases development shows that promising solution in databases gathering a huge quantities of information is approximate retrieval. This work presents the fuzzy queries processing with a special attention to context-dependent and multidimensional membership functions. In the first part, the analysis of occurrence of fuzzy values in SQL queries directed to the databases is shown. Next, the process of interpretation of filtering conditions in SQL queries is discussed and condition on compatibility degree is introduced to SQL queries notation. The main part of the paper describes application of context-dependent and multidimensional membership functions in fuzzy SQL queries as two different approaches, and a whole process of their usage interpretation. All issues considered in this work are illustrated by appropriate examples.

Fuzzy Data Warehouse (FDW) is a data repository, which contains fuzzy data and allows fuzzy processing of the data. Incorporation of fuzziness into data warehouse systems gives the opportunity to process data at higher level of abstraction and improves the analysis of imprecise data. It also gives the possibility to express business indicators in natural language using terms, like: high, low, about 10, almost all, etc., represented by appropriate membership functions. Fuzzy processing in data warehouses can affect many operations, like data selection, filtering, aggregation, and grouping. In the paper, we concentrate on various cases of data aggregation in our recently implemented fuzzy data warehouse storing consumption and requirement for global natural resources represented as crisp and fuzzy measures. We show several examples of data aggregation and filtering using the extended syntax of the SQL SELECT statement.

The inspection of structural changes (in selected proteins’ regions) that take place in particular reactions of signal pathways is very important for their analysis. Conformational modifications as an effect of biochemical reactions or environmental influences cause changes in the conformational energy distributions. Observations of the energy characteristics allow to detect structural changes of selected proteins. In the paper, we present the EAST – method of similarity searching based on energy profiles. This method can be employed to find similar proteins during the analysis of substrates of signal paths’ reactions and to detect regions of protein structure modifications.

The signal cascades are a number of successive biochemical re ac tions, occurring in the cells. In these reactions take part many proteins (often en zymes) and the entire process may be compared to the dominoes effect. The com mon term used for de fine a varied biochemical mechanisms regulating pro ces ses in the nano net works is signal transduction executed in the signal cas ca des. These processes can be realized in a closed area of space which contains pro per quan tities of substrates and a set of control molecules wor king in a pre de fined manner which was determined by their chemical con struc tion, in clu ding changes of chemical activity reached usually by the con for ma tional chan ges. Information about the sig nal cascades that happen in the various type of cells for given processes can be retrieved from the bio chemical research acce ssi ble in the biochemical data ba ses. In this work, the simulation process of the sig nal transduction in bio-nano processes using the distributed database en vi ron ment is presented and illus trated by the biological example.

Visualization of protein molecular structures is a very important part of the analysis of protein function and activity. Visualization tools allow to represent graphically the complex construction of proteins and give us an idea of the biological molecules built with hundreds or thousands of atoms linked to each other by covalent bonds. In the chapter we present the most interesting features of our Protein Molecular Viewer (PMV). The PMV is a molecular visualization tool which is used to show protein structures loaded from the well-known Protein Data Bank. With the possibility of loading and presenting protein structures from the PDBML data format the PMV becomes one of a few tools in the world having this unique function.

Popular methods for 3D protein structure similarity searching, especially those that generate high quality alignments such as Combinatorial Extension (CE) and Flexible structure AlignmenT by Chaining Aligned fragment pairs allowing Twists (FATCAT), are still very time-consuming. As a consequence, performing similarity searching against large repositories of structural data requires increased computational resources that are not always available. Cloud computing provides huge amounts of computational power that can be provisioned on a pay-as-you-go basis. We have developed the cloud-based system that allows scaling of the similarity searching process vertically and horizontally. Cloud4Psi (Cloud for Protein Similarity) was tested in the Microsoft Azure cloud environment and provided good, almost linearly proportional acceleration when scaled out onto many computational units. Availability: Cloud4Psi is available as Software as a Service (SaaS) for testing purposes at: http://cloud4psi.cloudapp.net/.

New link to  the project web site: http://zti.polsl.pl/w3/dmrozek/science/cloud4psi.htm

Searching for similar 3D protein structures is one of the primary processes employed in the field of structural bioinformatics. However, the computational complexity of this process means that it is constantly necessary to search for new methods that can perform such a process faster and more efficiently. Finding molecular substructures that complex protein structures have in common is still a challenging task, especially when entire databases containing tens or even hundreds of thousands of protein structures must be scanned. Graphics processing units (GPUs) and general purpose graphics processing units (GPGPUs) can perform many time-consuming and computationally demanding processes much more quickly than a classical CPU can. In this paper, we describe the GPU-based implementation of the CASSERT algorithm for 3D protein structure similarity searching. This algorithm is based on the two-phase alignment of protein structures when matching fragments of the compared proteins. The GPU (GeForce GTX 560Ti: 384 cores, 2GB RAM) implementation of CASSERT (“GPU-CASSERT”) parallelizes both alignment phases and yields an average 180-fold increase in speed over its CPU-based, single-core implementation on an Intel Xeon E5620 (2.40GHz, 4 cores). In this paper, we show that massive parallelization of the 3D structure similarity search process on many-core GPU devices can reduce the execution time of the process, allowing it to be performed in real time. GPU-CASSERT is available at: http://zti.polsl.pl/w3/dmrozek/science/gpucassert/cassert.htm.

New link to  the project web site: http://zti.polsl.pl/w3/dmrozek/science/gpucassert/cassert.htm