MOTIVATION: Most integral membrane proteins form dimeric or oligomeric complexes. Oligomerization is frequently supported by the non-covalent interaction of transmembrane helices. It is currently not clear how many high-affinity transmembrane domains (TMD) exist in a proteome and how specific their interactions are with respect to preferred contacting faces and their underlying residue motifs. RESULTS: We first identify a threshold of 55% sequence similarity, which demarcates the border between meaningful alignments of TMDs and chance alignments. Clustering the human single-span membrane proteome using this threshold groups ~40% of the TMDs. The homotypic interaction of the TMDs representing the 33 largest clusters was systematically investigated under standardized conditions. The results reveal a broad distribution of relative affinities. High relative affinity frequently coincides with (i) the existence of a preferred helix-helix interface and (ii) sequence specificity as indicated by reduced affinity after mutating conserved residues. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transmembrane helices and the helical bundles which they form are the major building blocks of membrane proteins. Since helices are characterized by a given periodicity, it is possible to search for patterns of traits which typify one side of the helix and not the other (e.g. amphipathic helices contain a polar and apolar sides). Using Fourier transformation we have analyzed solved membrane protein structures as well as sequences of membrane proteins from the Swiss-Prot database. The traits searched included aromaticity, volume and ionization. While a number of motifs were already recognized in the literature, many were not. One particular example involved helix VII of lactose permease which contains seven aromatic residues on six helical turns. Similarly six glycine residues in four consecutive helical turns were identified as forming a motif in the chloride channel. A tabulation of all the findings is presented as well as a possible rationalization of the function of the motif.

SUMMARY: The genomic abundance and pharmacological importance of membrane proteins have fueled efforts to identify them based solely on sequence information. Previous methods based on the physicochemical principle of a sliding window of hydrophobicity (hydropathy analysis) have been replaced by approaches based on hidden Markov models or neural networks which prevail due to their probabilistic orientation. In the current study, an optimization of the hydrophobicity tables used in hydropathy analysis is performed using a genetic algorithm. As such, the approach can be viewed as a synthesis between the physicochemically and statistically based methods. The resulting hydrophobicity tables lead to significant improvement in the prediction accuracy of hydropathy analysis. Furthermore, since hydropathy analysis is less dependent on the basis set of membrane proteins is used to hone the statistically based methods, as well as being faster, it may be valuable in the analysis of new genomes. Finally, the values obtained for each of the amino acids in the new hydrophobicity tables are discussed.