Selection of representative protein data sets

By Uwe Hobohm, Michael Scharf, Reinhard Schneider, Christopher Sander. 1992

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A series of PDB related databases for everyday needs (2010, methodology): “From the Sander–Schneider plot, (Figure 2) we see that 25% identity is a safe cut-off.”
UniRef: comprehensive and non-redundant UniProt reference clusters (2007, neutral): “A number of algorithms, tools and databases have been developed for clustering protein sequences (Enright and Ouzounis, 2000; Hobohm et al., 1992; Li et al., 2001; Mika and Rost, 2003; Paccanaro et al., 2006; Petryszak et al., 2005; Pipenbacher et al., 2002).”
Tolerating some redundancy significantly speeds up clustering of large protein databases (2002, methodology): “The basic algorithm for database clustering in this study is the greedy incremental algorithm (Hobohm et al., 1992), which selects representative protein sequence sets, and is also used in NRDB90 and CD-HI.”
STITCH 4: integration of protein–chemical interactions with user data (2013, methodology): “To avoid biases, we ﬁrst excluded highly similar chemicals, enforcing a maximum Tanimoto similarity of 0.9 (using Algorithm 2 described by Hobohm) (31) using 2D chemical ﬁngerprints calculated with the chemistry development kit (32,33).”
Assessing protein structures with a non-local atomic interaction energy. (1998, background): “, 1977) representative list (Hobohm et al., 1992; Hobohm & Sander, 1994), after excluding all the”
NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence (2007, background): “95 distance level, and only a single allele from each cluster selected for display [51].”
CPHmodels-3.0—remote homology modeling using structure-guided sequence profiles (2010, methodology): “A Hobohm 1 algorithm (10) was used to identify clusters of structural similar proteins, and a maximum of 10 structures per cluster were included.”
Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. (1996, background): “Usually, protein representatives are chosen by specifying an upper limit for sequence identity (Hobohm et al., 1992) or structural similarity (Orengo et al.”
Nonlinear Models Using Dirichlet Process Mixtures (2007, methodology): “The proteins in this dataset are obtained from the PDB select database (Hobohm et al. , 1992; Hobohm and Sander, 1994) such that two proteins have no more than 35% of the sequence identity for aligned subsequences larger than 80 residues.”
PDBselect 1992–2009 and PDBfilter-select (2009, background): “which turns out to be faster than the prior greedy algorithm (8,9).”
Exploiting the past and the future in protein secondary structure prediction (1999, methodology): “From the remaining chains, a representative subset with low pairwise sequence similarities was selected by running the algorithm no. 1 of Hobohm et al. (1992), using the local alignment procedure search (rigorous Smith– Waterman algorithm) (Myers and Miller, 1988; Pearson, 1990) using the pam120…”
Removing near-neighbour redundancy from large protein sequence collections (1998, methodology): “Here, we generate a representative set based on a greedy incremental algorithm (Hobohm et al., 1992).”
PISCES: a protein sequence culling server (2003, methodology): “We use the method of Hobohm and Sander (Hobohm et al., 1992; Hobohm and Sander, 1994) to cull the sequences that pass the criteria described above by sequence identity.”