title: iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.
abstract: Knowledge of protein-protein interactions and their binding sites is indispensable for in-depth understanding of the networks in living cells. With the avalanche of protein sequences generated in the postgenomic age, it is critical to develop computational methods for identifying in a timely fashion the protein-protein binding sites (PPBSs) based on the sequence information alone because the information obtained by this way can be used for both biomedical research and drug development. To address such a challenge, we have proposed a new predictor, called iPPBS-Opt, in which we have used: (1) the K-Nearest Neighbors Cleaning (KNNC) and Inserting Hypothetical Training Samples (IHTS) treatments to optimize the training dataset; (2) the ensemble voting approach to select the most relevant features; and (3) the stationary wavelet transform to formulate the statistical samples. Cross-validation tests by targeting the experiment-confirmed results have demonstrated that the new predictor is very promising, implying that the aforementioned practices are indeed very effective. Particularly, the approach of using the wavelets to express protein/peptide sequences might be the key in grasping the problem's essence, fully consistent with the findings that many important biological functions of proteins can be elucidated with their low-frequency internal motions. To maximize the convenience of most experimental scientists, we have provided a step-by-step guide on how to use the predictor's web server (http://www.jci-bioinfo.cn/iPPBS-Opt) to get the desired results without the need to go through the complicated mathematical equations involved.
authors.affiliation: Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China. jjia@gordonlifescience.org.|Gordon Life Science Institute, Boston, MA 02478, USA. jjia@gordonlifescience.org.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Jia
authors.affiliation: Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China. liuzi189836@163.com.
authors.forename: Zi
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China. xxiao@gordonlifescience.org.|Gordon Life Science Institute, Boston, MA 02478, USA. xxiao@gordonlifescience.org.
authors.forename: Xuan
authors.initials: X
authors.lastname: Xiao
authors.affiliation: Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen 333403, China. lbx1966@163.com.
authors.forename: Bingxiang
authors.initials: B
authors.lastname: Liu
authors.affiliation: Gordon Life Science Institute, Boston, MA 02478, USA. kcchou@gordonlifescience.org.|Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia. kcchou@gordonlifescience.org.
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D002352:Carrier Proteins / Q000737:chemistry* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D066264:Datasets as Topic; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software
chemical_list: D002352:Carrier Proteins; D011506:Proteins
keywords: IHTS; KNNC; Optimize training dataset; PseAAC; physicochemical property; protein-protein binding sites; stationary wavelet transform; target cross-validation