kPROT: a knowledge-based scale for the propensity of residue orientation in transmembrane segments. Application to membrane protein structure prediction.
Modeling of integral membrane proteins and the prediction of their functional sites requires the identification of transmembrane (TM) segments and the determination of their angular orientations. Hydrophobicity scales predict accurately the location of TM helices, but are less accurate in computing angular disposition. Estimating lipid-exposure propensities of the residues from statistics of solved membrane protein structures has the disadvantage of relying on relatively few proteins. As an alternative, we propose here a scale of knowledge-based Propensities for Residue Orientation in Transmembrane segments (kPROT), derived from the analysis of more than 5000 non-redundant protein sequences. We assume that residues that tend to be exposed to the membrane are more frequent in TM segments of single-span proteins, while residues that prefer to be buried in the transmembrane bundle interior are present mainly in multi-span TMs. The kPROT value for each residue is thus defined as the logarithm of the ratio of its proportions in single and multiple TM spans. The scale is refined further by defining it for three discrete sections of the TM segment; namely, extracellular, central, and intracellular. The capacity of the kPROT scale to predict angular helical orientation was compared to that of alternative methods in a benchmark test, using a diversity of multi-span alpha-helical transmembrane proteins with a solved 3D structure. kPROT yielded an average angular error of 41 degrees, significantly lower than that of alternative scales (62 degrees -68 degrees ). The new scale thus provides a useful general tool for modeling and prediction of functional residues in membrane proteins. A WWW server (http://bioinfo.weizmann.ac.il/kPROT) is available for automatic helix orientation prediction with kPROT.
Pilpel
Y
Y
Department of Molecular Genetics and the Crown Genome Center, The Weizmann Institute of Science, Rehovot, 76100, Israel. bnpilpel@membran1.weizmann.ac.il
Ben-Tal
N
N
Lancet
D
D
D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D016208:Databases, Factual; D020407:Internet; D008565:Membrane Proteins / Q000737:chemistry*; D008956:Models, Chemical; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary
D008565:Membrane Proteins