3D-MEDNEs: an alternative "in silico" technique for chemical research in toxicology. 2. Quantitative Proteome-Toxicity Relationships (QPTR) based on mass spectrum spiral entropy

• Type: Article in International Scientific Journal
• Date: 2008

Authors

Maykel Cruz Monteagudo (Author) Other; Humberto Gonzalez Diaz (Author) Other; Fernanda Borges (Author) FFUP; Elena Rosa Dominguez (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: Chemical Research in Toxicology

Vol. 21

Pages: 619-632

ISSN: 0893-228X

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 39 Citations

Scopus - 43 Citations

Scientific classification

FOS: Natural sciences > Chemical sciences

Other information

Authenticus ID: P-004-11Q

DOI: 10.1021/tx700296t

Abstract (EN): 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 (Gonzalez-Diaz, 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 370 100 intensity (I(i)) signals with m/z bandwidth above 700-12000 each. Next, we calculated the 3D-MEDNEs for each MS using the software MARCA-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.

Language: English

Type (Professor's evaluation): Scientific