In Silico Analysis of Peptide Potential Biological Functions

摘要

Over the past decade, tools of omics technologies have generated a large amount of data in various repositories, which are of interest for meta-analysis today. Now, researchers in the field of proteomics and peptidomics focus not on sequencing, but on functions performed by molecules and metabolic interactions, in which the proteins or peptides participate. As a result of a single LC-MS/MS analysis, several thousand unique peptides can be identified, each of which may be bioactive. A classic technique for determining the peptide function is a direct experiment. Bioinformatics approaches as a preliminary analysis of potential biological functions are an important step and are able to significantly reduce time and cost of experimental verification. This article provides an overview of computational methods for predicting biological functions of peptides. Approaches based on machine learning, which are the most popular today, algorithms using structural, evolutionary, or statistical patterns, as well as methods based on molecular docking, are considered. Databases of bioactive peptides are reported, providing information necessary to construct new algorithms for predicting biological functions. Attention is paid to the characteristics of peptides, on the basis of which it is possible to draw conclusions about their bioactivity. In addition, the report provides a list of online services that may be used by researchers to analyze potential activities of peptides with which they work.