A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

Autores

Alejandro Speck Planche (Autor) Outra; Valeria V Kleandrova (Autor) Outra; Feng Luan (Autor) Outra; Natalia N D S Cordeiro (Autor) FCUP

Revista

Título: Molecular BioSystems

Vol. 8

Páginas: 2188-2196

ISSN: 1742-206X

Editora: Royal Society of Chemistry

Indexação

ISI Web of Knowledge - 48 Citações

Scopus - 51 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências biológicas

Outras Informações

ID Authenticus: P-002-G54

DOI: 10.1039/c2mb25093d

Abstract (EN): Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9