Proteins structure.

By G. Giacomello. 1957

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Citations in the corpus

Deep transfer learning in the assessment of the quality of protein models (2018, background): “The protein models are defined in a coarse representation by the sine and cosine of the dihedral angles [28] φ and ψ, the secondary structure, relative surface area, and energies of hydrogen bonds in the backbone, as defined by DSSP [29].”
Physical model of the genotype-to-phenotype map of proteins (2016, background): “Past studies have shown that the motion of proteins [37– 40] and their hydrophobicity patterns [41] may often be approximated by a few normal modes, while others have demonstrated that the variation in aligned sequences may be characterized by a few correlation modes [33, 42, 43].”
Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks (2016, methodology): “We use four publicly available datasets, CB6133 produced with PISCES CullPDB [Wang and Dunbrack, 2003], CB513 [Cuff and Barton, 1999] 3, CASP10 [Kryshtafovych et al.”
Inverse statistical physics of protein sequences: a key issues review (2017, unknown): “, to solve the proteinfolding problem [1, 2].”
Protein Structure Prediction: Conventional and Deep Learning Perspectives (2021, methodology): “For structure prediction, CONFOLD2 [118], UniCon3D [119], FUSION [120], GDFuzz3D [121], FT-COMAR [122], MULTICOM [123], and DeepCDpred [124] use ab initio techniques.”
Efficient generative modeling of protein sequences using simple autoregressive models (2021, unknown): “to DCA based on pseudolikelihood maximization [34].”
Integrated protein function prediction by mining function associations, sequences, and protein–protein and gene–gene interaction networks (2015, unknown): “For example, sequence-based function prediction methods PFP [16, 34] and ESG [35] from professor Kihara’s lab use PSI-BLAST one time and recursively against the target sequence to get the hits for protein function prediction [36, 37], the”