Evaluation of drug-human serum albumin binding interactions with support vector machine aided online automated docking.

TitleEvaluation of drug-human serum albumin binding interactions with support vector machine aided online automated docking.
Publication TypeJournal Article
Year of Publication2011
AuthorsZsila, F, Bikadi, Z, Malik, D, Hari, P, Pechan, I, Berces, A, Hazai, E
JournalBioinformatics
Volume27
Issue13
Pagination1806-13
Date Published2011 Jul 1
ISSN1367-4811
KeywordsArtificial Intelligence, Binding Sites, Crystallography, X-Ray, Humans, Models, Molecular, Pharmaceutical Preparations, Protein Binding, Serum Albumin
Abstract

MOTIVATION: Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform (http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.

DOI10.1093/bioinformatics/btr284
Alternate JournalBioinformatics
PubMed ID21593135