Practical limits of function prediction

By Damien P. Devos, Alfonso Valencia. Proteins: Structure, Function, and Bioinformatics, 2000

368 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/02df63306725cf25b2ed2b7e960b9631f17071eb

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Citations in the corpus

Predicting post-synaptic activity in proteins with data mining (2005, background): “This is a very difficult problem, since the relationship between protein sequence and function is very complex (Gerlt and Babbitt, 2000; Devos and Valencia, 2000; Nagl, 2003).”
UniqueProt: creating representative protein sequence sets (2003, background): “…infer that: (i) two proteins dwell in the same sub-cellular compartment (3, K.O.Wrzeszczynski and B.Rost, manuscript submitted); (ii) that they belong to the same groups of cellular function (4), have similar binding sites (5) or belong to similar descriptions according to the GeneOntology (6,7).”
Subsequence-based feature map for protein function classification (2008, background): “All rights reserved. oi:10.1016/j.compbiolchem.2007.11.004 tion ensitivity/specificity (Devos and Valencia, 2000; Gilks et al., 005; Sasson et al., 2006; Friedberg, 2006).”
Metrics for GO based protein semantic similarity: a systematic evaluation (2008, methodology): “On the other hand, the increasingly important role for bioinformatics in annotation [23] has lead to a growing number of annotations extrapolated from sequence similarity, which are prone to errors [24,25].”
Prediction of protein function from protein sequence and structure (2003, result): “(2001) and Devos & Valencia (2000).”
Finding nuclear localization signals (2000, background): “The sequence similarity required to infer function is much higher (Devos and Valencia, 2000).”
Evolution of function in protein superfamilies, from a structural perspective. (2001, methodology): “In their analysis, Devos & Valencia (2000) investigated the relationship between sequence identity and several functional descriptions, including EC assignments, SWISSPROT keywords (Bairoch & Apweiler, 2000) and binding sites, using FSSP (Holm & Sander, 1996) as a source of structural domains and…”
Comparing function and structure between entire proteomes (2001, methodology): “UsingEUCLID (Tamames et al. 1996, 1998), we could classify∼ 45%–65% of all proteins into one of 13 functional classes at a level reported to yield 70% correct classifications with >30% pairwise sequence identity (Devos and Valencia 2000).”
Adaptive computation and machine learning (1998, background): “Many functional aspects of proteins are determined mainly by local sequence characteristics, and do not depend critically on a full 3D structure maintained in part by long-range interactions [149].”
Prediction of human protein function from post-translational modifications and localization features. (2002, neutral): “23”
How well is enzyme function conserved as a function of pairwise sequence identity? (2003, background): “been frequently observed that function may diverge more quickly than sequence, and homologous proteins may evolve to have different functions and possess different functional sites, especially when sequence identity falls below 40%.(17,18,20,39) Thus, the dataset constructed by Rost that contains on”