Transmembrane protein topology prediction methods play important roles in structural biology, because the structure determination of these types of proteins is extremely difficult by the common biophysical, biochemical and molecular biological methods. The need for accurate prediction methods is high, as the number of known membrane protein structures fall far behind the estimated number of these proteins in various genomes. The accuracy of these prediction methods appears to be higher than most prediction methods applied on globular proteins, however it decreases slightly with the increasing number of structures. Unfortunately, most prediction algorithms use common machine learning techniques, and they do not reveal why topologies are predicted with such a high success rate and which biophysical or biochemical properties are important to achieve this level of accuracy. Incorporating topology data determined so far into the prediction methods as constraints helps us to reach even higher prediction accuracy, therefore collection of such topology data is also an important issue.