Biologically active peptides (BAP) are increasingly in the focus of scientific research due to their widespread use in medicine, food and pharmaceutical industries. Researching and studying the properties of peptides is a laborious and expensive process. In recent years, in silico methods, including data mining or artificial intelligence, have been applied more and more to reveal biological, physicochemical and sensory properties of peptides. This significantly shortens the process of peptide sequences analysis. This article presents a software tool that uses a data mining approach to discover a number of physicochemical properties of a specific peptide. Working with it is extremely simple - it is only necessary to input the amino acid sequence of the peptide of interest. The software tool is designed to generate data in order to increase the classification and prediction accuracy, as well as to leverage the engineering of new amino acid sequences. This way, the proposed software greatly facilitates the work or scientific researchers. The software application is publicly available at www.pep-lab.info/dmpep.

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Research on food-derived bioactive peptides is expanding and the need for a convenient online platform that combines a foolproof and intuitive user interface with a reliable database and tools for prediction and analysis is rising. In this regard, this paper presents an open-access web-based platform PepLab (Peptides Laboratory). The database contains 2764 peptide sequence entries, grouped into sixteen classes according to their biological activity and into seven classes according to the source from which they were derived. Moreover, it includes bioinformatic tools for their processing and analysis. The DMpep tool allows extracting information about a number of physicochemical characteristics of a peptide or a set of peptides, including those that are not recorded in the database. The main advantages of PepLab are a user-friendly interface, a responsive design, and optimized search engines for better visibility on the Internet. Prediction of non-reported activity is available based on amino acid sequence analysis. In addition, users can download data and results in a convenient format (text and/or graphic) that was limited in the existing platforms. In this way, the presented PepLab platform will be helpful for researchers from various fields—bioinformatics, pharmaceuticals, food sciences, dietetics, biotechnology, analytical chemistry, etc.

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