TY - JOUR T1 - Detection of Alpha-Rod Protein Repeats Using a Neural Network and Application to Huntingtin A1 - Palidwor, Gareth A. A1 - Shcherbinin, Sergey A1 - Huska, Matthew R. A1 - Rasko, Tamas A1 - Stelzl, Ulrich A1 - Arumughan, Anup A1 - Foulle, Raphaele A1 - Porras, Pablo A1 - Sanchez-Pulido, Luis A1 - Wanker, Erich E. A1 - Andrade-Navarro, Miguel A. Y1 - 2009/03/13 N2 - Author Summary Many proteins have an elongated structural domain formed by a stack of alpha helices (alpha-rod), often found to interact with other proteins. The identification of an alpha-rod in a protein can therefore tell something about both the function and the structure of that protein. Though alpha-rods can be readily identified from the structure of proteins, for the vast majority of known proteins this is unavailable, and we have to use their amino acid sequence. Because alpha-rods have highly variable sequences, commonly used methods of domain identification by sequence similarity have difficulty detecting them. However, alpha-rods do have specific patterns of amino acid properties along their sequences, so we used a computational method based on a neural network to learn these patterns. We illustrate how this method finds novel instances of the domain in proteins from a wide range of organisms. We performed detailed analysis of huntingtin, the protein mutated in Huntington's chorea, a neurodegenerative disease. The function of huntingtin remains a mystery partially due to the lack of knowledge about its structure. Therefore, we defined three alpha-rods in this protein and experimentally verified how they interact with each other, a novel result that opens new avenues for huntingtin research. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 5 IS - 3 UR - https://doi.org/10.1371/journal.pcbi.1000304 SP - e1000304 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000304 ER -