Predicting protein function from sequence and structure

By David A. Lee, Oliver Redfern, Christine A. Orengo. 2007

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In Molecular Biology

Citations in the corpus

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields (2015, neutral): “ the parameters � and � is given by, 𝛻� �,� =[∑Å𝑖=1𝛿(�𝑖=�)𝛿(�𝑖+1=�)]−𝐸�𝑃𝑃(�|�,�,�,�)[∑ 𝛿(�𝑖=�)𝛿(�𝑖+1=�) Å 𝑖=1 ] (6) 𝛻� �,� =[∑Å𝛿(�𝑖=�)𝐻�(�,𝑖,�) 𝑖=1 ]−𝐸�𝑃𝑃(�|�,�,�,�)[∑ 𝛿(�𝑖=�)𝐻�(�,𝑖,�) Å 𝑖=1 ] (7) Where 𝐸�𝑃 is the expectation function and can be calculated efficiently u”
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models (2021, background): “Knowledge of the three-dimensional (3D) arrangement of the atoms of a protein can provide essential clues to understanding the roles and mechanisms under-pinning protein functions (3,4).”
Annotation Error in Public Databases: Misannotation of Molecular Function in Enzyme Superfamilies (2009, background): “The expected continued use of simple annotation transfer for functional annotation for sequences submitted to the NR and TrEMBL databases from large scale sequencing projects suggests that this trend is likely to continue [39–41].”
LocTree3 prediction of localization (2014, unknown): “Bridging the sequenceannotation gap (4) for localization, therefore, calls for cheaper and faster in silico approaches (5,6).”
Comprehensive structural classification of ligand-binding motifs in proteins. (2008, methodology): “There are already a number of methods to compare the atomic structures or other structural features of functional sites of proteins (see reviews, Jones & Thornton, 2004; Lee et al., 2007).”
PDBe-KB: collaboratively defining the biological context of structural data (2021, background): “The structure of biological macromolecules and their complexes is invaluable for understanding their functions (1,2).”