Evaluated on a new non-redundant protein set with 224 chains, the method has 80.7% sensitivity and 82.9% specificity in the 5-fold cross-validation test. In addition, it predicts DNA-binding sites with 85.1% sensitivity and 85.3% specificity when tested on a dataset with 62 protein-DNA complexes. Compared with a recently published method, BindN+, our method predicts DNA-binding sites with a 7% better area under the receiver operating characteristic curve value when tested on the same dataset. Many important problems in cell biology require the dense non-linear interactions between functional modules be considered. Thus, our prediction method will be useful in detecting such complex interactions.

Defensins as one of the most abundant classes of antimicrobial peptides are an essential part of the innate immunity that has evolved in most living organisms from lower organisms to humans. To identify specific defensins as interesting antifungal leads, in this study, we constructed a more rigorous benchmark dataset and the iDPF-PseRAAAC server was developed to predict the defensin family and subfamily. Using reduced dipeptide compositions were used, the overall accuracy of proposed method increased to 95.10% for the defensin family, and 98.39% for the vertebrate subfamily, which is higher than the accuracy from other methods. The jackknife test shows that more than 4% improvement was obtained comparing with the previous method. A free online server was further established for the convenience of most experimental scientists at http://wlxy.imu.edu.cn/college/biostation/fuwu/iDPF-PseRAAAC/index.asp. A friendly guide is provided to describe how to use the web server. We anticipate that iDPF-PseRAAAC may become a useful high-throughput tool for both basic research and drug design.

Knowledge of the submitochondria location of protein is integral to understanding its function and a necessity in the proteomics era. In this work, a new submitochondria data set is constructed, and an approach for predicting protein submitochondria locations is proposed by combining the amino acid composition, dipeptide composition, reduced physicochemical properties, gene ontology, evolutionary information, and pseudo-average chemical shift. The overall prediction accuracy is 93.57% for the submitochondria location and 97.79% for the three membrane protein types in the mitochondria inner membrane using the algorithm of the increment of diversity combined with the support vector machine. The performance of the pseudo-average chemical shift is excellent. For contrast, the method is also used to predict submitochondria locations in the data set constructed by Du and Li; an accuracy of 94.95% is obtained by our method, which is better than that of other existing methods.

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