Prediction and interpretation of the lipophilicity of small peptides.
J Comput Aided Mol Des
; 29(4): 361-70, 2015 Apr.
Article
em En
| MEDLINE
| ID: mdl-25577035
ABSTRACT
Peptide-based drug discovery has considerably expanded and solid in silico tools for the prediction of physico-chemical properties of peptides are urgently needed. In this work we tested some combinations of descriptors/algorithms to find the best model to predict [Formula see text] of a series of peptides. To do that we evaluate the models statistical performances but also their skills in providing a reliable deconvolution of the balance of intermolecular forces governing the partitioning phenomenon. Results prove that a PLS model based on VolSurf+ descriptors is the best tool to predict [Formula see text] of neutral and ionised peptides. The mechanistic interpretation also reveals that the inclusion in the chemical structure of a HBD group is more efficient in decreasing lipophilicity than the inclusion of a HBA group.
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
Base de dados:
MEDLINE
Assunto principal:
Peptídeos
/
Desenho de Fármacos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
J Comput Aided Mol Des
Assunto da revista:
BIOLOGIA MOLECULAR
/
ENGENHARIA BIOMEDICA
Ano de publicação:
2015
Tipo de documento:
Article