Peptipedia: a user-friendly web application and a comprehensive database for peptide research supported by Machine Learning approach

Abstract

Peptides have attracted attention during the last decades due to their extraordinary therapeutic properties. Different computational tools have been developed to take advantage of existing information, compiling knowledge and making available the information for common users. Nevertheless, most related tools available are not user-friendly, present redundant information, do not clearly display the data, and usually are specific for particular biological activities, not existing so far, an integrated database with consolidated information to help research peptide sequences. To solve these necessities, we developed Peptipedia, a user-friendly web application and comprehensive database to search, characterize and analyse peptide sequences. Our tool integrates the information from 30 previously reported databases with a total of 92 055 amino acid sequences, making it the biggest repository of peptides with recorded activities to date. Furthermore, we make available a variety of bioinformatics services and statistical modules to increase our tool’s usability. Moreover, we incorporated a robust assembled binary classification system to predict putative biological activities for peptide sequences. Our tools’ significant differences with other existing alternatives become a substantial contribution for developing biotechnological and bioengineering applications for peptides. Peptipedia is available for non-commercial use as an open-access software, licensed under the GNU General Public License, version GPL 3.0. The web platform is publicly available at peptipedia.cl. Database URL: Both the source code and sample data sets are available in the GitHub repository https://github.com/ProteinEngineering-PESB2/peptipedia

MultiPep: a hierarchical deep learning approach for multi-label classification of peptide bioactivities

“…Peptipedia is a tool that can predict many different bioactivity classes simultaneously. The authors describe how they have trained 44 different RF models that each can predict belonging to a specific bioactivity class [ 27 ]. As described for the comparisons above, we used the MultiPep model trained using our “save the individual network class-clade” approach that produced the lowest overall loss on the test set ( Supplementary Tables S15 and S25 ).…”

“…We used the test set of this model to generate a test-subset that was used as input to the Peptipedia models ( Supplementary Table S30 ). Noteworthy, as the Peptipedia webtool was unavailable during the writing of this article, we retrieved the Peptipedia predictions via personal communication with the authors of the article presenting Peptipedia [ 27 ]. In the prediction data received by the Peptipedia authors, 41 bioactivity classes were included.…”

“…Altogether, this suggests that the problem does not lie with the bioactivity classes defined in the data presented in this work. In addition, the peptide data used by MultiPep and Peptipedia stem from a number of identical databases [ 27 ]. Therefore, it is very unlikely that MultiPep’s and Peptipedia’s defined bioactivity classes are of completely different origin.…”

“…PEPred-Suite uses adaptive feature representation strategy, where they, among other things, use 10 feature encoding algorithms, which together efficiently capture local and global compositional information and well as position-specific residue information and physiochemical information [ 26 ]. Peptipedia encodes peptides using representations of physicochemical properties and transforms them using Fourier transforms [ 27 , 79 ]. Although some general selection rules have been suggested, it is difficult to find a single universally optimal peptide encoding technique [ 77 , 78 ].…”

“…PEPred-suite [ 26 ] uses eight random forest (RF) models to predict how peptides belong to eight different classes. Peptipedia [ 27 ] is another tool that, among other things, can predict different bioactivities of peptides by using 44 RF models trained on data from a large number of peptide databases. A combination of several machine learning algorithm types has been utilized for the prediction of peptide bioactivities ( Table 1 ).…”