Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials’ Discovery

Abstract

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks’ mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.

StarPep Toolbox: an open-source software to assist chemical space analysis of bioactive peptides and their functions using complex networks

“… Aguilera-Mendoza et al (2019) provided a detailed explanation of this module, and further information is available in the online documentation of the software ( https://grupo-medicina-molecular-y-traslacional.github.io/StarPep_doc/networks.html ). In addition, specific use cases have been reported by Ayala-Ruano et al (2022) , Romero et al (2022) , Castillo-Mendieta et al (2023) , and Agüero-Chapin et al (2023) .…”

“…These important nodes facilitate the identification of shared motifs among potential AMP families organized into distinct communities. Previous research identified such motifs for different AMPs, including antiparasitic ( Ayala-Ruano et al 2022 ), tumor-homing ( Romero et al 2022 ), hemolytic ( Castillo-Mendieta et al 2023 ), and antibiofilm peptides ( Agüero-Chapin et al 2023 ). In addition, these nodes serve as the basis for the subsequent creation of mQSSMs, as detailed in the next section.…”

Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence

“…Our method is based on complex network science and multiquery similarity searching (MQSS) models. This procedure has demonstrated not only to accurately predict tumor-homing and antiparasitic peptide activities but also to get a deeper insight into the peptide chemical space, , hence improving peptide-based drug design. Furthermore, a software specifically designed for this purpose, named StarPep toolbox v0.8.5 has been developed to facilitate the workflow …”

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning

“…Machine learning models have emerged as a time-saving and cost-effective tool for screening large data sets to identify potential AMPs. To date, machine learning models − based on amino acid sequences have mainly been built using traditional − and deep learning (DL) − techniques, as well as using similarity networks. , However, the outstanding results of deep neural network-based approaches, such as trRosetta, AlphaFold, RoseTTAFold, ESMFold, and HelixFold-Single, in the prediction of tertiary (3D) structures of proteins from their amino acid sequences have unlocked new opportunities to build better predictive models. In this regard, non-DL based models using 3D protein descriptors − as well as Graph Neural Network-based models , (e.g., equivariant network) are promissory strategies to be developed.…”