QSAR Models for Proteins of Parasitic Organisms, Plants and Human Guests: Theory, Applications, Legal Protection, Taxes, and Regulatory Issues
The Quantitative Structure-Property Relationship (QSPR) models based on Graph or Network theory are important to represent and predict interesting properties of low-molecular-weight compounds. The graph parameters called Topological Indices (TIs) are useful to link the molecular structure with physicochemical and biological properties. However, there have been recent efforts to extend these methods to the study of proteins and whole proteomes as well. In this case, we are in the presence of Quantitative Protein/Proteome-Property Relationship (QPPR) models, by analogy to QSPR. In the present work we review, discuss, and outline some perspectives on the use of these QPPR techniques applied to single proteins of Parasitic Organisms, Plants and Human Guests. We make emphasis on the different types of graphs and network representations of proteins, the structural information codified by different protein TIs, the statistical or machine learning techniques used and the biological properties predicted. This article also provides a reference to the various legal avenues that are available for the protection of software used in proteins QSAR; as well as the acceptance and legal treatment of scientific results and techniques derived from such software. We also make reference to the recent implementation by Munteanu and Gonzalez-Diaz of the internet portal called BioAims freely available for the use of the international research community. This portal includes the web-server packages TargetPred with two new Protein-QSAR servers: ATCUNPred (http://miaja.tic.udc.es/Bio-AIMS/ATCUNPred.php) for prediction of ATCUN-mediated DNAclevage anticancer proteins and EnzClassPred for prediction of enzyme classes (http://miaja.tic.udc.es/Bio- AIMS/EnzClassPred.php). Last we included an overview of relevant topics related to legal protection, regulation, and international tax issues involved in practical use of this type of models and software in proteomics.
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Document Type: Research Article
Publication date: December 1, 2009
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