Predicting the Molecular Targets of Conopeptides by using Principal Component Analysis and Multiclass Logistic Regression

Xavier Eugenio Asuncion1,4, Abdul-Rashid Sampaco III2,4,
Henry Adorna2,4, Joselito Magadia3,4, Vena Pearl Boñgolan2,4, and Arturo Lluisma1,4*


*Corresponding Author: This email address is being protected from spambots. You need JavaScript enabled to view it.




Computational tools for inferring molecular targets from the primary structure would be crucial in exploiting the wealth of sequence data. In this work, we have developed a computational method in predicting the molecular targets of conopeptides given only their primary structures. Our proposed method makes use of descriptors calculated from the primary structure, and machine learning to create a model that can identify the most likely target among five target types. Our proposed method is based on principal component analysis (PCA) and multiclass logistic regression algorithms. PCA was used to reduce the dimensionality of the data, which resulted in the improvement of the model’s performance. By using nested cross-validation, a multiclass logistic regression with PCA was able to achieve an accuracy of 89% – outperforming other classical machine learning algorithms. We also compared our proposed method to a basic sequence similarity search and found that our method produced better overall results. These results suggest that our proposed method may be used as a complementary method to sequence similarity search in identifying candidate targets of newly sequenced and isolated conopeptides.



With around 800 cone snail species, Conus has emerged as one of the most promising sources of marine drugs (Himaya and Lewis 2018, Gao et al. 2017, Prashanth et al. 2014). Like other venomous organisms, cone snails use their extremely potent venom to capture their prey and to protect themselves from potential predators. Over the course of their evolution, each Conus species has developed a unique set of bioactive peptides, which are commonly referred to as conotoxins or conopeptides. Due to their small size (typically less than 5 kDa), diversity (around 100 per species with little overlap between species), and their high specificity to an array of biological targets, conopeptides serve as excellent templates for the design of novel drugs (Prashanth et al. 2014, Lewis et al. 2012). In 2004, the first conopeptide-derived drug – Prialt – was approved by the US Food and Drug Administration (Pope and Deer 2013) and, since then, several other conopeptides reached the advanced stages of clinical trials (Nielsen et al. 2005, Lubbers et al. 2005, Barton et al. 2004, Sandall et al. 2003). Thus, with more than 80,000 estimated conopeptides and only 6260 conopeptides known to date (Kaas et al. 2007, 2011), cone snail venoms still hold great promise for the discovery of new drug leads. . . . . read more




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