Topology Prediction of Helical Transmembrane Proteins: How Far Have We Reached?

Abstract

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.

Human salivary proteome — a resource of potential biomarkers for oral cancer

“…The secretory features of the salivary proteins (n = 3449) were examined by mapping to Exocarta database for their exosomal association or to SignalP and TMHMM database for the presence of signal peptide sequence or transmembrane domain, respectively. Since, signal peptides control the proper targeting of virtually all proteins to the secretory pathway; its mapping also represents glycosylated proteins that are secreted [42,43]. We observed that 1920 (56%) salivary proteins have secretory features based on any one of the above three criteria thereby supporting their detection in saliva (Supplementary Table 2).…”

Topography Prediction of Helical Transmembrane Proteins by a New Modification of the Sliding Window Method

“…Hence, they soon were supplied by novel statistical, machine-learning methods, which use hundreds of free parameters extracted from databases of experimentally mapped topologies [13, 27]. However, as it is stated in [27], the translocons (cellular machineries) responsible for membrane-protein biogenesis do not have access to statistical data but rather exploit molecular interactions to ensure that membrane proteins attain their correct topology.…”

“…Therefore, it is necessary to develop novel approaches in structurally resolving aspects of their biology [11–13]. One of such useful approaches is to screen these proteins with help of structural bioinformatics and methods of computer modeling to identify those of them with the best characteristics for structural studies and for crystallography trials.…”

X‐Ray Crystallography for Biotherapeutics

“…To do this they used a hydrophobic index for each of the amino acids and a sliding window against the primary sequence [28] to define a "GRAVY" index at each point of the sequence. Derivatives of this index are the cornerstone of all subsequent TM prediction algorithms [29].…”