title: Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data.
abstract: BACKGROUND
  Prediction of interactions between proteins and chemical compounds is of great benefit in drug discovery processes. In this field, 3D structure-based methods such as docking analysis have been developed. However, the genomewide application of these methods is not really feasible as 3D structural information is limited in availability.
  
  
  RESULTS
  We describe a novel method for predicting protein-chemical interaction using SVM. We utilize very general protein data, i.e. amino acid sequences, and combine these with chemical structures and mass spectrometry (MS) data. MS data can be of great use in finding new chemical compounds in the future. We assessed the validity of our method in the dataset of the binding of existing drugs and found that more than 80% accuracy could be obtained. Furthermore, we conducted comprehensive target protein predictions for MDMA, and validated the biological significance of our method by successfully finding proteins relevant to its known functions.
  
  
  BACKGROUND
  Available on request from the authors.
authors.affiliation: Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Japan.
authors.forename: Nobuyoshi
authors.initials: N
authors.lastname: Nagamine
authors.forename: Yasubumi
authors.initials: Y
authors.lastname: Sakakibara
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D013058:Mass Spectrometry / Q000379:methods*; D008956:Models, Chemical; D010449:Peptide Mapping / Q000379:methods*; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
chemical_list: D011506:Proteins