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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. 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. 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.

CHNOSZ

Performs thermodynamic calculations in geochemistry and compositional biology. CHNOSZ serves for facilitating calculations in: (1) the standard molal thermodynamic properties of chemical species and reactions as a function of temperature and pressure; (2) the standard molal thermodynamic properties and equations of state parameters of neutral and ionized proteins using group additivity algorithms; and (3) the chemical affinities of formation reactions of species of interest from basis species describing the system. It also generates metastable equilibrium activity diagrams for systems of biomolecules and/or other species.

MemType-2L

Provides a 2-layer predictor. MemType-2L consists of the following steps: the 1st layer prediction engine identifies a query protein as membrane or non-membrane. If it is a membrane protein, the process will automatically continue with the 2nd-layer prediction engine to further identify its type among the following eight categories: (1) type I, (2) type II, (3) type III, (4) type IV, (5) multipass, (6) lipid-chain-anchored, (7) GPI-anchored, and (8) peripheral. MemType-2L is featured by incorporating the evolution information through representing the protein samples with the Pse-PSSM (Pseudo Position-Specific Score Matrix) vectors, and by containing an ensemble classifier formed by fusing many powerful individual OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbor) classifiers. The success rates obtained by MemType-2L on a new-constructed stringent dataset by both the jackknife test and the independent dataset test are quite high, indicating that MemType-2L may become a very useful high throughput tool.