Abstract
Structural signature is a set of characteristics that unequivocally identifies protein folding and the nature of interactions with other proteins or binding compounds. We investigate the use of the geometric linearity of the main chain as a key feature for structural classification. Using polypeptide main chain atoms as structural signature, we showed that this signature is better to preciselly classify than using C\(\alpha \) only. Our results are equivalent in precision to a structural signature built including artificial points between C\(\alpha \)s and hence we believe this improvement in classification precision occurs due to the strengthening of geometric linearity.
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Funding
This work was supported by the Brazilian agencies Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 51/2013 - 23038.004007/2014-82; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG).
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Gadelha Campelo, J.A.F., Rodrigues Monteiro, C., da Silveira, C.H., de Azevedo Silveira, S., Cardoso de Melo-Minardi, R. (2019). Protein Structural Signatures Revisited: Geometric Linearity of Main Chains are More Relevant to Classification Performance than Packing of Residues. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11465. Springer, Cham. https://doi.org/10.1007/978-3-030-17938-0_35
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DOI: https://doi.org/10.1007/978-3-030-17938-0_35
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