The Protein Mutant Database

By T. Kawabata, M. Ota, Ken Nishikawa. 1999

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Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “We extracted all amino acid changing variants from the Protein Mutant Database [21] (PMD) and mapped these to their corresponding sequences.”
Collective judgment predicts disease-associated single nucleotide variants (2013, background): “This is an expected outcome, as not all functionally deleterious mutations are disease associated [8,28,29].”
Performance of mutation pathogenicity prediction methods on missense variants (2011, methodology): “The training sets for the NN were constructed from Protein Mutant Database (PMD) [Kawabata et al., 1999] data complemented by a set of neutral pseudomutations generated by the authors of the method as described in Bromberg and Rost [2007].”
Predict impact of single amino acid change upon protein structure (2012, background): “The first set comprised 12,461 functionally neutral and 35,585 functional effect mutants from 3,444 proteins [17,18].”
A family of evolution-entropy hybrid methods for ranking protein residues by importance. (2004, methodology): “As an alternative, we construct by literature search a tentative key residue set for several well-investigated proteins, a task in which we are greatly assisted by Protein Mutant Database.21 We then estimate the quality of a method by its capability to rank the members of the key set highly.”
SNAP predicts effect of mutations on protein function (2008, unknown): “Contact: bromberg@rostlab.org Supplementary information: Supplementary data are available at Bioinformatics online.”
Disease-related mutations predicted to impact protein function (2012, methodology): “SNAP was trained on literature-derived [16] mutants that are either functionally similar to the wild-type (neutral) or alter function (effect) in either direction (decrease and increase of function).”
SNAP: predict effect of non-synonymous polymorphisms on function (2007, methodology): “(1) Train the neural network for 200 repetitions on a balanced set of all training data points (2) Determine the accuracy of the network on the cross-training set after each repetition.”
Integrating structural and mutagenesis data to elucidate GPCR ligand binding (2016, background): “The Protein Mutant Database, not limited to GPCRs, featured 218,873 in vitro and natural protein mutations from 45,239 studies [26], but is no longer accessible.”
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations (2014, methodology): “The Protein Mutant Database (PMD) dataset was derived from PMD (version 07Mar26) which contains experimental information about effects of 165,880 single point mutations on protein activity, stability and connections to diseases; extracted from more than 10,000 articles [30].”
SPROUTS: a database for the evaluation of protein stability upon point mutation (2008, background): “One can cite the Protherm database (3) which contains thermodynamic experimental data including free energy changes or the Protein Mutant Database (4) which includes references to mutant proteins from the literature, but no database devoted to the collection of stability changes prediction exists, t”
Neutral and weakly nonneutral sequence variants may define individuality (2013, background): “Because experimental ﬁ ndings are rarely reported to databases in a standardized form, metaresources, such as the Protein Mutant Database (PMD) (4) and Swiss-Prot/UniProt (5), manually curate publications to map variants to diseases.”
Machine learning in protein engineering (2018, background): “The Protein Mutant Database (Kawabata et al., 1999), an early attempt to catalog the effects of mutations from protein engineering studies across different proteins and functions, has not been updated in over a decade.”