title: PPRODO: prediction of protein domain boundaries using neural networks.
abstract: Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multidomain proteins but also for the experimental structure determination. Since protein sequences of multiple domains may contain much information regarding evolutionary processes such as gene-exon shuffling, this information can be detected by analyzing the position-specific scoring matrix (PSSM) generated by PSI-BLAST. We have presented a method, PPRODO (Prediction of PROtein DOmain boundaries) that predicts domain boundaries of proteins from sequence information by a neural network. The network is trained and tested using the values obtained from the PSSM generated by PSI-BLAST. A 10-fold cross-validation technique is performed to obtain the parameters of neural networks using a nonredundant set of 522 proteins containing 2 contiguous domains. PPRODO provides good and consistent results for the prediction of domain boundaries, with accuracy of about 66% using the +/-20 residue criterion. The PPRODO source code, as well as all data sets used in this work, are available from http://gene.kias.re.kr/ approximately jlee/pprodo/.
authors.affiliation: School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.
authors.forename: Jaehyun
authors.initials: J
authors.lastname: Sim
authors.forename: Seung-Yeon
authors.initials: SY
authors.lastname: Kim
authors.forename: Jooyoung
authors.initials: J
authors.lastname: Lee
mesh_terms: D000596:Amino Acids / Q000032:analysis; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results
chemical_list: D000596:Amino Acids; D011506:Proteins