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Article

3D-MEDNEs: An Alternative “in Silico” Technique for Chemical Research in Toxicology. 2. Quantitative Proteome−Toxicity Relationships (QPTR) based on Mass Spectrum Spiral Entropy

Supporting Info

Maykel Cruz-Monteagudo, ^†^‡, Humberto González-Díaz, *, Fernanda Borges, ^†, Elena Rosa Dominguez, ^‡ and M. Natália D.S. Cordeiro, ^§

Physico-Chemical Molecular Research Unit, Department of Organic Chemistry, Faculty of Pharmacy, University of Porto, 4150-047 Porto, Portugal, Applied Chemistry Research Center, Faculty of Chemistry and Pharmacy, Central University of Las Villas (UCLV), Santa Clara 54830, Cuba, Unit of Bioinformatics & Connectivity Analysis (UBICA), Institute of Industrial Pharmacy, and Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain, and REQUIMTE, Department of Chemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal

Chem. Res. Toxicol., 2008, 21 (3), pp 619–632

DOI: 10.1021/tx700296t

Publication Date (Web): February 8, 2008

†

Physico-Chemical Molecular Research Unit, University of Porto.

‡

UCLV.

* To whom correspondence should be addressed. Tel: +34-981-563100 . Fax: +34-981 594912. E-mail: gonzalezdiazh@yahoo.es or qohumbe@usc.es.

University of Santiago de Compostela.

REQUIMTE, University of Porto.

Abstract

Low range mass spectra (MS) characterization of serum proteome offers the best chance of discovering proteome−(early drug-induced cardiac toxicity) relationships, called here Pro-EDICToRs. However, due to the thousands of proteins involved, finding the single disease-related protein could be a hard task. The search for a model based on general MS patterns becomes a more realistic choice. In our previous work ( González-Díaz, H., et al. Chem. Res. Toxicol.2003, 16, 1318– 1327), we introduced the molecular structure information indices called 3D-Markovian electronic delocalization entropies (3D-MEDNEs). In this previous work, quantitative structure−toxicity relationship (QSTR) techniques allowed us to link 3D-MEDNEs with blood toxicological properties of drugs. In this second part, we extend 3D-MEDNEs to numerically encode biologically relevant information present in MS of the serum proteome for the first time. Using the same idea behind QSTR techniques, we can seek now by analogy a quantitative proteome−toxicity relationship (QPTR). The new QPTR models link MS 3D-MEDNEs with drug-induced toxicological properties from blood proteome information. We first generalized Randic’s spiral graph and lattice networks of protein sequences to represent the MS of 62 serum proteome samples with more than 370100 intensity ( I _i ) signals with m/ z bandwidth above 700–12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCH-INSIDE. After that, we developed several QPTR models using different machine learning and MS representation algorithms to classify samples as control or positive Pro-EDICToRs samples. The best QPTR proposed showed accuracy values ranging from 83.8% to 87.1% and leave-one-out (LOO) predictive ability of 77.4–85.5%. This work demonstrated that the idea behind classic drug QSTR models may be extended to construct QPTRs with proteome MS data.