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.

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.

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Protein secondary structure describe protein construction in terms of regular spatial shapes, including alpha-helices, beta-strands, and loops, which protein amino acid chain can adopt in some of its regions. This information is supportive for protein classification, functional annotation, and 3D structure prediction. The relevance of this information and the scope of its practical applications cause the requirement for its effective storage and processing. Relational databases, widely-used in commercial systems in recent years, are one of the serious alternatives honed by years of experience, enriched with developed technologies, equipped with the declarative SQL query language, and accepted by the large community of programmers. Unfortunately, relational database management systems are not designed for efficient storage and processing of biological data, such as protein secondary structures. In this paper, we present a new search method implemented in the search engine of the PSS-SQL language. The PSS-SQL allows formulation of queries against a relational database in order to find proteins having secondary structures similar to the structural pattern specified by a user. In the paper, we will show how the search process can be accelerated by multiple scanning of the Segment Index and parallel implementation of the alignment procedure using multiple threads working on multiple-core CPUs.

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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.