Half-Space Proximal Networks (HSPNs): A Proxy for Multi-Query Similarity Searching Models Predicting Tumor-Homing Peptides

Tumor-homing peptides (THPs) have emerged as promising
agents in
cancer treatments. These short sequences can specifically target tumor
cells and vasculature. Here, a nontrained machine learning (ML) method
based on network science and multiquery similarity searching to predict
THPs is presented. We leverage the network-based representation of
THPs’ chemical space to extract valuable information by employing
a novel similarity-based, yet sparse, network known as the half-space
proximal network (HSPN). The HSPN of the THPs’ giant component
is composed of 12 communities that represent distinct modes of action
and/or targets, as well as sequence templates (scaffolds). In the
HSPN analysis, various centrality measures were employed to identify
the most significant and nonredundant THPs. These central THPs were
then used as queries (Qs) in group fusion similarity-based searches
against an established collection of known THPs. The performance of
the resulting multiquery similarity-based search models (MQSSMs) was
assessed using three benchmarking datasets of THPs/non-THPs. The MQSSMs
derived from the HSPNs (THP2) demonstrated superior discrimination
performance compared to the classical chemical space networks (CSNs,
namely THP1) when applied to the THPs/non-THPs datasets
Remarkably, exceptional MCC values (>0.887) were achieved when
utilizing
Qs from both CSN and HSPN networks to construct MQSSMs (THP3), employing a similarity threshold of 0.6, in external datasets.
Next, we conducted a statistical comparison between the performance
of our top-performing MQSSM, THP3, and several THP prediction
servers, including TumorHPD, THPep, SCMTHP, and NEPTUNE. Our proposed
model demonstrated its superiority by surpassing the state-of-the-art
supervised and trained ML methods for THP prediction with statistically
significant differences. These results provide strong evidence that
network-based similarity searches are highly effective and reliable
for identifying THPs.