2026-04-14T05:45:37Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/488062025-12-20T16:44:18Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Anton, Bernat author Besalú, Mireia author Fornés Crespo, Oriol, 1983- author Bonet Martínez, Jaume, 1982- author Molina, Alexis author Molina Fernández, Rubén author Cuevas, Gemma de las author Fernández Fuentes, Narcís author Oliva Miguel, Baldomero author 2021-10-26T05:58:10Z 2021-10-26T05:58:10Z 2021 Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that groups chemically equivalent residues combined with super-secondary structure motifs to model protein structures. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure and it is in line with the role of hydrophobic forces in protein-folding funneling. As a result of a compressed alphabet, the number of sequences required for the multiple sequence alignment is reduced. The number of long-range contacts predicted is limited; therefore, our approach requires the use of neighboring sequence-positions. We use the prediction of secondary structure and motifs of super-secondary structures to predict local contacts. We use RADI and raDIMod, a fragment-based protein structure modelling, achieving near native conformations when the number of super-secondary motifs covers >30-50% of the sequence. Interestingly, although different contacts are predicted with different alphabets, they produce similar structures. Spanish Ministry of Economy MINECO [BIO2014-57518-R, BIO2017-83591-R (FEDER, UE), BIO2017-85329-R (FEDER, UE)]; Generalitat de Catalunya [SGR17-1020]. On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction