Transmembrane proteins (TMP) play important roles in living cells: they are involved in material and information transport, in adhesion and move of cells, as well as in the energy production too. The importance of their biological roles is well reflected by the facts that more than half of the drugs currently on the market interact with TMPs, and about 25-30% of the coded protein in the genome belong to this class of proteins. However, investigation of their structure and function is complicated, because of their special physico-chemical properties, and therefore only 3% of the known protein structures are TMP. Even their expressions bump into several complications, because of the various lipid composition of biological membranes, the toxicity of TMPs and the improper post translational modifications. The former results of the experiments made within the Protein Structure Initiative are publicly available (1-2), but this information has not been investigated by computational biological methods. In the current research we would like to use this data in machine learning methods in order to predict TMPs that can be easily expressed in various expression systems and validate the developed method on expression of several transmembrane proteins (3-4).

1. Pieper U, Schlessinger A, Kloppmann E, Chang GA, Chou JJ, Dumont ME, Fox BG, Fromme P, Hendrickson WA, Malkowski MG, Rees DC, Stokes DL, Stowell MH, Wiener MC, Rost B, Stroud RM, Stevens RC, Sali A. (2013) Coordinating the impact of structural genomics on the human α-helical transmembrane proteome. Nat Struct Mol Biol. 20 135-8. doi: 10.1038/nsmb.2508.
2. Kloppmann E1, Punta M, Rost B. (2012) Structural genomics plucks high-hanging membrane proteins. Curr Opin Struct Biol. 22 326-32. doi: 10.1016/
3. Varga J, Dobson L, Remenyi I and Tusnady GE (2017) TSTMP: target selection for structural genomics of human transmembrane proteins. Nucleic Acids Res 45, D325-330.
4. Nagy GN, Marton L, Contet A, Ozohanics O, Ardelean LM, Révész A, Vékey K, Irimie FD, Vial H, Cerdan R, Vértessy BG (2014) Composite aromatic boxes for enzymatic transformations of quaternary ammonium substrates. Angew Chem Int Ed Engl. 2014 Dec 1;53(49):13471-6.

Beáta Vértessy, Gábor Tusnády

Result_May 2020