Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions

Abstract

Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.

Predicting Drugs Side Effects Based on Chemical-Chemical Interactions and Protein-Chemical Interactions

“…Likewise, it has been indicated by some pioneer studies [13, 16] that interactive compounds follow the similar rules. Since side effect is one part of biological functions of drugs, using the properties of interactive compounds to identify drugs side effects is a feasible scheme.…”

“…Thus, the prediction of drugs side effects is a multilabel classification problem. Like the case in dealing with compounds with multiple properties [13, 16], the proposed method would provide the order information of side effects from the most likely to the least likely.…”

“…Thus we can acquire the properties of many compounds and their other information from these databases. For those compounds not being covered by these databases, their properties can be inferred from the property-known compounds stored in the databases [13–16]. Likewise, the drugs side effects can also be inferred as elaborated below.…”

“…Recently, it was evidenced that interactive proteins are more likely to share common biological functions [17–20], and that interactive compounds are also more likely to share common biological functions [13, 16]. Since the side effects are part of biological functions of drugs, it would be feasible to use the chemical-chemical interactions to identify the drugs side effects.…”

Study of drug function based on similarity of pathway fingerprint

“…Many studies have indicated that computational approaches can timely provide very useful insights for both basic research and drug development, such as predicting drug−target interaction networks , predicting HIV cleavage sites in proteins (Chou, 1996), prediction of body fluids (Hu et al, 2011a), predicting protein metabolic stability (Huang et al 2010), predicting signal peptides (Chou and Shen, 2007), predicting the network of substrate−enzyme−product triads (Chen et al, 2010), predicting protein subcellular locations (Chou et al, 2011), predicting biological functions of compounds based on chemicalchemical interactions (Hu et al, 2011b). Actually, several novel mathematical approaches and physical concepts have been introduced into molecular biology, such as Mahalanobis distance (Chou, 1995), pseudo amino acid composition (Chou, 2001), graph and diagram analysis (Zhou and Deng, 1984;Andraos, 2008;Chou, 2010), cellular automaton (Xiao et al, 2009), grey theory (Xiao et al, 2008), low-frequency (or Terahertz frequency) phonons (Chou, 1989), which can significantly stimulate the development of biological and medical science.…”