1 - 6 of 6 results

OCTOPUS / obtainer of correct topologies for uncharacterized sequences

Predicts transmembrane protein topology. OCTOPUS uses a combination of hidden Markov models (HMM) and artificial neural networks. In particular, OCTOPUS fully integrates modeling of reentrant/membrane-dipping regions and transmembrane hairpins in the topological grammar. It first performs a homology search using BLAST to create a sequence profile. This is used as the input to a set of neural networks that predict both the preference for each residue to be located in a transmembrane (M), interface (I), close loop (L) or globular loop (G) environment and the preference for each residue to be on the inside (i) or outside (o) of the membrane. In the third step, these predictions are used as input to a two-track HMM, which uses them to calculate the most likely topology. OCTOPUS is available online and can be downloaded as part of the TOPCONS software package.

CSmetaPred / Catalytic Site META Preditor

Ranks residues based on the mean of normalized residue scores (meta-score) obtained from four catalytic residue prediction methods. CSmetaPred is a meta-predictor that includes the predicted pocket information with the mean residue score or meta-score to further improve prediction performance in another meta-predictor: CSmetaPred_poc. It can assist experimentalists in identifying and characterizing catalytic residues by prioritizing residues for mutational studies.

HMPAS / Human Membrane Protein Analysis System

A human membrane protein analysis system containing 36,585 proteins and their important characteristics. HMPAS integrates known membrane proteins from 19 different publicly available resources. The collected proteins are classified based on their interaction type with membrane and used them separately to build more accurate prediction rules for the membrane proteins. They are also hierarchically classified based on their subcellular localization and molecular function and integrated with diverse sequence features, biological process and pharmaceutical information.


Predicts membrane protein types. MemHyb-SVM considers both evolutionary and physicochemical features. It provides to a classification system based on support vector machine (SVM) with error correction code. MemHyb-SVM employs a powerful sequence encoding scheme by fusing position specific scoring matrix and split amino acid composition to effectively discriminate membrane protein types. This proposed approach is an effective tool for the discrimination of membrane protein types and might be helpful to researchers/academicians working in the field of Drug Discovery, Cell Biology, and Bioinformatics.