2 research outputs found
Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides
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 AMPsâ
chemical space (AMPCS), represented by more than 1065 unique
sequences, has represented a big challenge for the discovery of new
promising therapeutic peptides and for the identification of common
structural motifs. Here, we introduce network science and a similarity
searching approach to discover new promising AMPs, specifically antiparasitic
peptides (APPs). We exploited the network-based representation of
APPsâ chemical space (APPCS) to retrieve valuable information
by using three network types: chemical space (CSN), half-space proximal
(HSPN), and metadata (METN). Some centrality measures were applied
to identify in each network the most important and nonredundant peptides.
Then, these central peptides were considered as queries (Qs) in group
fusion similarity-based searches against a comprehensive collection
of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting
multiquery 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 data sets. 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. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high
sequence diversity of these peptides, different computational approaches
were applied to identify relevant motifs for searching and designing
new APPs. Lastly, we identified 11 promising APP lead candidates by
using our best mQSSMs together with diversity-based
network analyses, and 24 web servers for activity/toxicity and drug-like
properties. These results support that network-based similarity searches
can be an effective and reliable strategy to identify APPs. The proposed
models and pipeline are freely available through the StarPep
toolbox software at http://mobiosd-hub.com/starpep
<i>Dry</i> selection and <i>wet</i> evaluation for the <i>rational</i> discovery of new anthelmintics
<p>Helminths infections remain a major problem in medical and public health. In this report, atom-based 2D bilinear indices, a <i>TOMOCOMD-</i><i>CARDD</i> (QuBiLs-MAS module) molecular descriptor family and linear discriminant analysis (LDA) were used to find models that differentiate among anthelmintic and non-anthelmintic compounds. Two classification models obtained by using non-stochastic and stochastic 2D bilinear indices, classified correctly 86.64% and 84.66%, respectively, in the training set. Equation 1(2) correctly classified 141(135) out of 165 [85.45%(81.82%)] compounds in external validation set. Another LDA models were performed in order to get the most likely mechanism of action of anthelmintics. The model shows an accuracy of 86.84% in the training set and 94.44% in the external prediction set. Finally, we carry out an experiment to predict the biological profile of our âin-houseâ collections of indole, indazole, quinoxaline and cinnoline derivatives (âŒ200 compounds). Subsequently, we selected a group of nine of the theoretically most active structures. Then, these chemicals were tested in an <i>in</i> <i>vitro</i> assay and one good candidate (VA5-5c) as fasciolicide compound (100% of reduction at concentrations of 50 and 10 mg/L) was discovered.</p