However, these experiments are often time consuming and expensive, which to a large extent explains the gap between the number of protein structures (~150,000) deposited in the Protein Data Bank (PDB) ( Berman et al., 2002) and that of sequences (~140,000,000) stored in the UniProtKB/TrEMBL database ( The UniProt Consortium, 2017, 2018). Released from the ribosomes, the chains fold spontaneously to produce functional three-dimensional structures or tertiary structures ( Anfinsen et al., 1961), which are usually determined by experiments, including X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy. The syntheses of proteins as long polypeptide chains or primary sequences take place in the ribosomes.
Proteins are biomacromolecules that function in various life processes, many of which have been found as drug targets of human diseases ( Huang et al., 2016 Li et al., 2021). More importantly, taking into account the reductive architecture, MLPRNN could be a baseline for future developments. The prediction accuracy by the MLPRNN on the publicly available benchmark CB513 data set is comparable with those by other state-of-the-art models. In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or 8-state protein secondary structures. Therefore, an efficient protein secondary structure predictor is of importance especially when the structure of an amino acid sequence fragment is not solved by high-resolution experiments, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy, which are usually time consuming and expensive. Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life science.
2Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China.1College of Computer Engineering, Jimei University, Xiamen, China.Zhiliang Lyu 1 †, Zhijin Wang 1 †, Fangfang Luo 1, Jianwei Shuai 2,3 * and Yandong Huang 1 *
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