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TMHcon

Predicts helix-helix contacts specifically within the transmembrane parts of membrane proteins. TMHcon is a neural-network based approach. It integrates sequence profiles, correlated mutations, protein topology, sequence separation, and predicted scores for lipid-exposure. It also includes predictions by two neural networks, one developed for helix-helix contacts on all transmembrane helix pairs, and the second specifically tailored for the prediction of helix-helix contacts lying on non-neighboring transmembrane helices.

TMhhcp

Predicts residue-residue contacts in alpha-helix transmembrane (TM) proteins. TMhhcp is a random forest (RF) algorithm that can be used as a valuable tool for predicting the structural properties of TM proteins and can help to gain useful insights into their structure and function. It also uses the machine learning method, which integrates Position-Specific Scoring Matrix (PSSM), sequence distance, residue covariance, residue conservation, relative residue distance of two helices and helix number to predict helix-helix contacts.

MemConP / Membrane Contact Prediction

A contact prediction method for α-helical transmembrane proteins, in which evolutionary couplings are combined with a machine learning approach. MemConP achieves a substantially improved accuracy (precision: 56.0%, recall: 17.5%, MCC: 0.288) compared to the use of either machine learning or co-evolution methods alone. The method also achieves 91.4% precision, 42.1% recall and a MCC of 0.490 in predicting helix-helix interactions based on predicted contacts. The approach was trained and rigorously benchmarked by cross-validation and independent testing on up-to-date non-redundant datasets of 90 and 30 experimental three dimensional structures, respectively.

TMhit / TM helix– helix interaction prediction

Obsolete
Predicts transmembrane (TM) interhelical residue contacts. TMhit is a method based on a hierarchical architecture with two levels via support vector machines (SVMs) classifiers. It estimates the propensities of residues or pairs in contact and incorporates them as input features for prediction. It could also be applied to select potentially interacting peptides and key residues for improving the binding specificity by protein engineering.