Exploring the Chemical Space of Antiparasitic Peptides and Discovery of New Promising Leads through a Novel Approach based on Network Science and Similarity Searching

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs’ chemical space (AMPCS), represented by more than 1065 unique sequences of 50 residues long or less, has represented a big challenge for the discovery of new promising sequences and for the identification of common structural motifs and even relevant biological functions. Here, we present a new approach based on network science and similarity searching to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs’ chemical space (APPCS) to retrieve valuable information by using three types of networks: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). In the network analysis, some centrality measures were applied to identify in each network the most important and non-redundant nodes/peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against an existing collection of known bioactive compounds, stored in the graph database starPepDB, to discover new potential APPs. The performance of the resulting multi-query similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (> 0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external datasets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs with statistically significant differences. After applying the best mQSSM and additional filters on the non-APP space, 95 AMPs were repurposed as potential antiparasitic leads. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new promising APPs. These results support that network-based similarity searches identify APPs with high effectivity and reliability. The proposed models and pipeline are freely available through the starPep toolbox software at http://mobiosd-hub.com/starpep.