scholarly journals 1P249 Prediction of protein-protein interaction sites using expected solvent accessibility changes and neural networks(Bioinformatics-structural genomics,Poster Presentations)

2007 ◽  
Vol 47 (supplement) ◽  
pp. S85
Author(s):  
Chioko Nagao ◽  
Ahmad Shandar ◽  
Kenji Mizuguchi
Keyword(s):  
Neural Networks ◽  
Structural Genomics ◽  
Protein Interaction ◽  
Solvent Accessibility ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Poster Presentations ◽  
Protein Interaction Sites

scholarly journals SPRINGS: Prediction of Protein-Protein Interaction Sites Using Artificial Neural Networks

2014 ◽  
Author(s):  
Gurdeep Singh ◽  
Kaustubh Dhole ◽  
Priyadarshini P. Pai ◽  
Sukanta Mondal
Keyword(s):  
Protein Interaction ◽  
Network Architecture ◽  
Solvent Accessibility ◽  
Evolutionary Information ◽  
Relative Solvent Accessibility ◽  
Structure Information ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Artificial Neural ◽  
Protein Interaction Sites

Knowledge of protein-protein interaction sites provides an important base for deciphering novel drug targets and applications of enzyme-based studies. But on account of biological complexity and transient forms, determination of these sites is a challenge in biology. Various computational approaches are being explored for relevant prediction based on available protein sequence-structure information. Here we propose a novel method SPRINGS (Sequence-based predictor of PRotein- protein interactING Sites) for identification of interaction sites based on sequences. It uses protein evolutionary information, averaged cumulative hydropathy and predicted relative solvent accessibility from amino acid chains in artificial neural network architecture with a promising performance for protein-protein interactions sites based research and applications.

scholarly journals SPRINGS: Prediction of Protein-Protein Interaction Sites Using Artificial Neural Networks

2014 ◽  
Author(s):  
Gurdeep Singh ◽  
Kaustubh Dhole ◽  
Priyadarshini P. Pai ◽  
Sukanta Mondal
Keyword(s):  
Protein Interaction ◽  
Network Architecture ◽  
Solvent Accessibility ◽  
Evolutionary Information ◽  
Relative Solvent Accessibility ◽  
Structure Information ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Artificial Neural ◽  
Protein Interaction Sites

Knowledge of protein-protein interaction sites provides an important base for deciphering novel drug targets and applications of enzyme-based studies. But on account of biological complexity and transient forms, determination of these sites is a challenge in biology. Various computational approaches are being explored for relevant prediction based on available protein sequence-structure information. Here we propose a novel method SPRINGS (Sequence-based predictor of PRotein- protein interactING Sites) for identification of interaction sites based on sequences. It uses protein evolutionary information, averaged cumulative hydropathy and predicted relative solvent accessibility from amino acid chains in artificial neural network architecture with a promising performance for protein-protein interactions sites based research and applications.

scholarly journals SPRINGS: Prediction of Protein-Protein Interaction Sites Using Artificial Neural Networks

2014 ◽  
Author(s):  
Gurdeep Singh ◽  
Kaustubh Dhole ◽  
Priyadarshini P. Pai ◽  
Sukanta Mondal
Keyword(s):  
Protein Interaction ◽  
Network Architecture ◽  
Solvent Accessibility ◽  
Evolutionary Information ◽  
Relative Solvent Accessibility ◽  
Structure Information ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Artificial Neural ◽  
Protein Interaction Sites

Knowledge of protein-protein interaction sites provides an important base for deciphering novel drug targets and applications of enzyme-based studies. But on account of biological complexity and transient forms, determination of these sites is a challenge in biology. Various computational approaches are being explored for relevant prediction based on available protein sequence-structure information. Here we propose a novel method SPRINGS (Sequence-based predictor of PRotein- protein interactING Sites) for identification of interaction sites based on sequences. It uses protein evolutionary information, averaged cumulative hydropathy and predicted relative solvent accessibility from amino acid chains in artificial neural network architecture with a promising performance for protein-protein interactions sites based research and applications.

Sign in / Sign up

Export Citation Format

Share Document