Title
Evaluation of drug-human serum albumin binding interactions with support vector machine aided online automated docking.
Abstract
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.
Year
DOI
Venue
2011
10.1093/bioinformatics/btr284
Bioinformatics [ISMB/ECCB]
Keywords
Field
DocType
online automated docking,principal binding site,serum albumin binding interaction,albumin x-ray structure,support vector machine,human serum albumin,probable ligand binding site,svm model,extraordinary binding capacity,complex geometry,experimental binding data,albumin bind,hsa binding property
Docking (molecular),Serum albumin,Binding site,Ligand (biochemistry),Computer science,Docking (dog),ADME,Bioinformatics,Human serum albumin,In silico
Journal
Volume
Issue
ISSN
27
13
1367-4811
Citations 
PageRank 
References 
1
0.43
4
Authors
7
Name
Order
Citations
PageRank
Ferenc Zsila110.43
Zsolt Bikadi2141.70
David Malik310.77
Peter Hari410.77
Imre Pechan580.95
Attila Berces610.43
Eszter Hazai7222.80