Several computational methods were developed, with a limited success, for predicting transmembrane alpha-helices and their topology. Pioneer methods utilized the fact that membrane-spanning regions contain more hydrophobic residues than other parts of the protein, however applying different hydrophobic scales altered the prediction results. Later, several statistical methods were developed to improve the topography prediction and a special alignment method was introduced. According to the positive-inside rule, cytosolic loops near the lipid bilayer contain more positively-charged amino acids. Applying this rule resulted in the first topology prediction methods. There is also a negative-outside rule in transmembrane alpha-helices from single-pass proteins, although negatively charged residues are rarer than positively charged residues in transmembrane segments of proteins. As more structures were determined, machine learning algorithms appeared. Supervised learning methods are trained on a set of experimentally determined structures, however, these methods highly depend on the training set. Unsupervised learning methods are based on the principle that topology depends on the maximum divergence of the amino acid distributions in different structural parts. It was also shown that locking a segment location based on prior knowledge about the structure improves the prediction accuracy. This feature has been added to some of the existing prediction methods. The most recent methods use consensus prediction (i.e. they use several algorithms to determine the final topology) and automatically incorporate previously determined experimental informations. HTP databaseThe human transmembrane proteome database provides a collection of topologies that are computationally predicted for human transmembrane proteins.
Several computational methods were developed, with a limited success, for predicting transmembrane alpha-helices and their topology. Pioneer methods utilized the fact that membrane-spanning regions contain more hydrophobic residues than other parts of the protein, however applying different hydrophobic scales altered the prediction results. Later, several statistical methods were developed to improve the topography prediction and a special alignment method was introduced. According to the positive-inside rule, cytosolic loops near the lipid bilayer contain more positively-charged amino acids. Applying this rule resulted in the first topology prediction methods. There is also a negative-outside rule in transmembrane alpha-helices from single-pass proteins, although negatively charged residues are rarer than positively charged residues in transmembrane segments of proteins. As more structures were determined, machine learning algorithms appeared. Supervised learning methods are trained on a set of experimentally determined structures, however, these methods highly depend on the training set. Unsupervised learning methods are based on the principle that topology depends on the maximum divergence of the amino acid distributions in different structural parts. It was also shown that locking a segment location based on prior knowledge about the structure improves the prediction accuracy. This feature has been added to some of the existing prediction methods. The most recent methods use consensus prediction (i.e they use several algorithms to determine the final topology) and automatically incorporate previously determined experimental informations. HTP databaseThe human transmembrane proteome database provides a collection of topologies that are computationally predicted for human transmembrane proteins.