Improving prediction of hydrophobic segments along a transmembrane protein sequence using adaptive multiscale lifting

Established methods for transmembrane protein segment prediction are often based upon hydrophobicity analysis. Classical wavelet multiscale methods have proved successful in the prediction task. However, they implicitly model protein chain residues on being equally spaced. Our main motivation is to challenge this assumption by developing a new multiscale 'lifting' technique that utilizes irregularly spaced residues, where the spacing is derived from resolved 3D information obtained from similar aligned proteins. For different protein families we calculate asymmetrical dissimilarity matrices of order 20 that estimate the 'distance' between residue types. We use our new adaptive lifting technique to regress the Kyte and Doolittle hydrophobicity index upon residues (now irregularly spaced using information from the distance matrices) and use the regression to predict transmembranar segments. We compare the results obtained through our method with the ones obtained through the usage of classical wavelets, and show that incorporating 3D resolved structure improves overall prediction (both in terms of the existence of predicted segments compared to experimentally determined ones and also the proportion of correctly predicted segments). The software is available from http://www.maths.bris.ac.uk/~maxmp/proteomics.html