Artificial neural network for prediction of antigenic activity for a major conformational epitope in the hepatitis C virus NS3 protein.
BACKGROUND
Insufficient knowledge of general principles for accurate quantitative inference of biological properties from sequences is a major obstacle in the rationale design of proteins with predetermined activities. Due to this deficiency, protein engineering frequently relies on the use of computational approaches focused on the identification of quantitative structure-activity relationship (SAR) for each specific task. In the current article, a computational model was developed to define SAR for a major conformational antigenic epitope of the hepatitis C virus (HCV) non-structural protein 3 (NS3) in order to facilitate a rationale design of HCV antigens with improved diagnostically relevant properties.


RESULTS
We present an artificial neural network (ANN) model that connects changes in the antigenic properties and structure of HCV NS3 recombinant proteins representing all 6 HCV genotypes. The ANN performed quantitative predictions of the enzyme immunoassay (EIA) Signal/Cutoff (S/Co) profiles from sequence information alone with 89.8% accuracy. Amino acid positions and physicochemical factors strongly associated with the HCV NS3 antigenic properties were identified. The positions most significantly contributing to the model were mapped on the NS3 3D structure. The location of these positions validates the major associations found by the ANN model between antigenicity and structure of the HCV NS3 proteins.


BACKGROUND
Matlab code is available at the following URL address: http://bio-ai.myeweb.net/box_widget.html
Lara
James
J
Division of Viral Hepatitis and Biotechnology Core Facility, Division of Scientific Resources, Centers for Disease Control and Prevention, 1600 Clifton Road MS A33, Atlanta, GA, 30333, USA. jlara@cdc.gov
Wohlhueter
Robert M
RM
Dimitrova
Zoya
Z
Khudyakov
Yury E
YE
D000465:Algorithms; D000595:Amino Acid Sequence; D000936:Antigen-Antibody Complex / Q000737:chemistry / Q000276:immunology* / Q000648:ultrastructure; D000941:Antigens / Q000737:chemistry / Q000276:immunology* / Q000648:ultrastructure; D018604:Epitope Mapping / Q000379:methods*; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011487:Protein Conformation; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship; D017361:Viral Nonstructural Proteins / Q000737:chemistry* / Q000276:immunology* / Q000648:ultrastructure; D012697:Serine Endopeptidases; D043583:Nucleoside-Triphosphatase; D053487:DEAD-box RNA Helicases; D000085242:Viral Proteases
D000936:Antigen-Antibody Complex; D000941:Antigens; D017361:Viral Nonstructural Proteins; C084422:NS3 protein, hepatitis C virus; D012697:Serine Endopeptidases; D043583:Nucleoside-Triphosphatase; D053487:DEAD-box RNA Helicases; D000085242:Viral Proteases