FurinDB (freely available online at http://www.nuolan.net/substrates.html) is a database of furin substrates. This database includes experimentally verified furin cleavage sites, substrates, species, experimental methods, original publications of experiments and associated drugs targeting furin substrates. The current database release contains 126 furin cleavage sites from three species: mammals, bacteria and viruses. A main feature of this database is that all furin cleavage sites are recorded as a 20-residue motif, including one core region (eight amino acids, P6–P2′) and two flanking solvent accessible regions (eight amino acids, P7–P14, and four amino acids, P3′–P6′), that represent our current understanding of the molecular biology of furin cleavage. This database is important for understanding the molecular evolution and relationships between sequence motifs, 3D structures, cellular functions and physical properties required by furin for cleavage, and for elucidating the molecular mechanisms and the progression of furin cleavage associated human diseases, including pathogenic infections, neurological disorders, tumorigenesis, tumor invasion, angiogenesis, and metastasis. FurinDB database will be a solid addition to the publicly available infrastructure for scientists in the field of molecular biology.

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.