With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

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

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and antiageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877±0.0530 and 0.873±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machine learning tools to facilitate the application of machine learning strategies to study peptide sequences. Database URL: https://peptipedia.cl/

Seaweed biomass is globally underutilized as a source of proteins despite its nutritional potential, with much of its use focused on hydrocolloid extraction. This study evaluated the nutritional quality and digestibility of protein and amino acids from two brown seaweeds (Durvillaea spp. and Macrocystis pyrifera), one green seaweed (Ulva spp.), and a novel mycoprotein derived from Durvillaea spp. through fungal fermentation. Using an in vitro gastrointestinal digestion Megazyme assay kit, protein digestibility-corrected amino acid scores (PDCAASs) and digestible indispensable amino acid scores (DIASSs) were determined. Compared with seaweeds, seaweed-derived mycoprotein presented significantly greater protein contents (~33%) and amino acid profiles (2.2 times greater than those of Durvillaea spp. and M. pyrifera), with greater digestibility (~100%) than seaweeds (<60%). The PDCAAS values were 0.37, 0.41, 0.53, and 0.89 for Ulva spp., Macrocystis pyrifera, Durvillaea spp., and mycoproteins, respectively. The DIASSs highlighted the superior nutritional quality of the mycoprotein, particularly for lysine (0.59) and histidine (0.67). SDS-PAGE revealed soluble peptides (<25 kDa) in Durvillaea spp., Macrocystis pyrifera, and mycoproteins, whereas Ulva spp. proteins exhibited limited solubility due to structural aggregation. These findings highlight the need to characterize the nutritional properties of edible seaweeds in Chile further and emphasize the importance of optimized processing techniques, such as fermentation or bioconversion, to improve the nutritional potential of seaweeds and develop high-quality food ingredients for diverse applications.