QSAR Models for Proteins of Parasitic Organisms, Plants and Human Guests: Theory, Applications, Legal Protection, Taxes, and Regulatory Issues

Abstract

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 González-Díaz 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.

A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0

“…According to its original definition, the PseAAC is actually formulated by a set of discrete numbers [16] as long as it is different from the classical amino acid composition (AAC) and that it is derived from a protein sequence that is able to harbor some sort of its sequence order and pattern information, or able to reflect some physicochemical and biochemical properties of the constituent amino acids. Since the concept of PseAAC was proposed, it has been widely used to deal with many protein-related problems and sequence-related systems (see, e.g., [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] and a long list of PseAAC-related references cited in a recent review [20] ). As summarized in [20] , until now 16 different PseAAC modes have been used to represent the samples of proteins for predicting their attributes.…”

Computer-Aided Drug Design Methodologies Toward the Design of Anti-Hepatitis C Agents

“…In all these cases we can find models that use the structural parameters of molecular systems like drugs, protein structure, RNA secondary structure, protein-protein interaction networks (PINs), genes network as input to predict the properties of such system (output). That is why, the editor González-Díaz has extended the discussion to different collective of authors editing special issues on CADD techniques (including QSAR and others), which have been published on journals such as the Current Topics in Medicinal Chemistry [19][20][21][22][23][24][25][26][27][28], Current Proteomics [29][30][31][32][33][34][35][36], Current Drug Metabolism [37][38][39][40][41][42][43][44][45], Current Pharmaceutical Design [46][47][48][49][50][51][52][53][54][55], and Current Bioinformatics [56][57][58][59][60][61][62][63]…”

Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

“…It may help to found new interactions for these drugs or discard possible toxicological effects depending on the other interactions predicted and/or discarded for these compounds. This type of experiment is of the major importance due to the cost in terms of animal sacrifice, time, materials and human resources of the experimental assay of all compounds against all these targets, see recent reviews by Duardo-Sanchez et al [45][46][47][48]. Using this model we can predict the different relationships between the drug-protein interactions [49,50].…”