Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case

Abstract

The Quantitative Structure–Property Relationships (QSPRs) based on Graph or Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845–3853; Polymer 46 (2005) 2791–2798; and Polymer 46 (2005) 6461–6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the study of complex mixtures of artificial polymers or biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (πk) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BP. We used the MARCH-INSIDE approach to calculate the πk(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome–Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on πk(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.