{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T09:51:20Z","timestamp":1744192280064},"reference-count":22,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:p> The secondary and tertiary structure of a protein has a primary role in determining its function. Even though many folding prediction algorithms have been developed in the past decades \u2014 mainly based on the assumption that folding instructions are encoded within the protein sequence \u2014 experimental techniques remain the most reliable to establish protein structures. In this paper, we searched for signals related to the formation of [Formula: see text]-helices. We carried out a statistical analysis on a large dataset of experimentally characterized secondary structure elements to find over- or under-occurrences of specific amino acids defining the boundaries of helical moieties. To validate our hypothesis, we trained various Machine Learning models, each equipped with an attention mechanism, to predict the occurrence of [Formula: see text]-helices. The attention mechanism allows to interpret the model\u2019s decision, weighing the importance the predictor gives to each part of the input. The experimental results show that different models focus on the same subsequences, which can be seen as codes driving the secondary structure formation. <\/jats:p>","DOI":"10.1142\/s0219720020500286","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T15:49:20Z","timestamp":1592495360000},"page":"2050028","source":"Crossref","is-referenced-by-count":2,"title":["A deep attention network for predicting amino acid signals in the formation of \u03b1-helices"],"prefix":"10.1142","volume":"18","author":[{"given":"A.","family":"Visibelli","sequence":"first","affiliation":[{"name":"Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100, Siena, Italy"}]},{"given":"P.","family":"Bongini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Mathematics, University of Siena, 53100, Siena, Italy"},{"name":"Department of Information Engineering, University of Florence, 50139, Florence, 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