Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues.
Quantitative Structure-Activity Relationship (QSAR) models have been used in Pharmaceutical design and Medicinal Chemistry for the discovery of anti-parasite drugs. QSAR models predict biological activity using as input different types of structural parameters of molecules. Topological Indices (TIs) are a very interesting class of these parameters. We can derive TIs from graph representations based on only nodes (atoms) and edges (chemical bonds). TIs are not time-consuming in terms of computational resources because they depend only on atom-atom connectivity information. This information expressed in the molecular graphs can be tabulated in the form of adjacency matrices easy to manipulate with computers. Consequently, TIs allow the rapid collection, annotation, retrieval, comparison and mining of molecular structures within large databases. The interest in TIs has exploded because we can use them to describe also macromolecular and macroscopic systems represented by complex networks of interactions (links) between the different parts of a system (nodes) such as: drug-target, protein-protein, metabolic, host-parasite, brain cortex, parasite disease spreading, Internet, or social networks. In this work, we review and comment on the following topics related to the use of TIs in anti-parasite drugs and target discovery. The first topic reviewed was: Topological Indices and QSAR for antiparasitic drugs. This topic included: Theoretical Background, QSAR for anti-malaria drugs, QSAR for anti-Toxoplasma drugs. The second topic was: TOMO-COMD approach to QSAR of antiparasitic drugs. We included in this topic: TOMO-COMD theoretical background and TOMO-COMD models for antihelmintic activity, Trichomonas, anti-malarials, anti-trypanosome compounds. The third section was inserted to discuss Topological Indices in the context of Complex Networks. The last section is devoted to the MARCH-INSIDE approach to QSAR of antiparasitic drugs and targets. This begins with a theoretical background for drugs and parameters for proteins. Next, we reviewed MARCH-INSIDE models for Pharmaceutical Design of antiparasitic drugs including: flukicidal drugs and anti-coccidial drugs. We close MARCH-NSIDE topic with a review of multi-target QSAR of antiparasitic drugs, MARCH-INSIDE assembly of complex networks of antiparasitic drugs. We closed the MARCH-INSIDE section discussing the prediction of proteins in parasites and MARCH-INSIDE web-servers for Protein-Protein interactions in parasites: Plasmod-PPI and Trypano-PPI web-servers. We closed this revision with an important section devoted to review some legal issues related to QSAR models.
González-Díaz
Humberto
H
Department of Microbiology & Parasitology, University of Santiago de Compostela, Spain. humberto.gonzalez@usc.es
Romaris
Fernanda
F
Duardo-Sanchez
Aliuska
A
Pérez-Montoto
Lázaro G
LG
Prado-Prado
Francisco
F
Patlewicz
Grace
G
Ubeira
Florencio M
FM
D000818:Animals; D000977:Antiparasitic Agents / Q000737:chemistry / Q000494:pharmacology; D003198:Computer Simulation; D016208:Databases, Factual / Q000331:legislation & jurisprudence; D030562:Databases, Protein; D015195:Drug Design; D006801:Humans; D008390:Markov Chains; D008958:Models, Molecular; D015394:Molecular Structure; D058990:Molecular Targeted Therapy; D010272:Parasitic Diseases / Q000145:classification / Q000188:drug therapy*; D025941:Protein Interaction Mapping; D021281:Quantitative Structure-Activity Relationship; D013329:Structure-Activity Relationship
D000977:Antiparasitic Agents