Within the twilight zone: a sensitive profile-profile comparison tool based on information theory.

By Golan Yona, Michael Levitt. 2002

300 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, methodology): “Quite a few PSSM-based profile comparison methods for homology detection have been developed, including [11, 18-23].”
The HHpred interactive server for protein homology detection and structure prediction (2005, methodology): “In a recent study (28), in which we bench-marked HHsearch, the method for HMM–HMM comparison employed by our server, together with PSI-BLAST, HMMER, PROF_SIM and COMPASS (3,6,7,29), HHsearch was found to possesses the highest sensitivity and alignment accuracy.”
MUSCLE: a multiple sequence alignment method with reduced time and space complexity (2004, methodology): “This is illustrated in Figure 14, in which the discrimination plot for LE on PP2 is compared with several other functions: PSP, LA, Yona and Levitt's [ 46 ], LAMA [47].”
Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison (2010, unknown): “Hidden Markov Models (HMMs) [1-7].”
Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity (2015, background): “where pi and qi are the probability distributions over the 20 amino acids for the natural and designed sequences, respectively, at position i and D(x,y) is the Jensen–Shannon divergence between two distributions x and y, as described in [44].”
The Proteomics Protocols Handbook (2005, background): “015%, digitonin preserves the ultrastructure of ER and mitochrondrial membranes (27), which at higher concentrations (approx 0.”
Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “…number of variants of profile-profile alignment methods that differ in the scoring functions they use (Pietrokovski, 1996; Rychlewski et al., 1998; Yona and Levitt, 2002; Panchenko, 2003; Sadreyev and Grishin, 2003; von Ohsen et al., 2003; Edgar, 2004; Marti-Renom et al., 2004; Zhou and Zhou,…”
Alignment of protein sequences by their profiles (2004, methodology): “A related approach was also used by Yona and Levitt (2000, 2002) to construct the ProtoMap database of protein sequence families (Yona et al. 1999, 2000; Yona and Levitt 2002).”
Finding weak similarities between proteins by sequence profile comparison. (2003, background): “This result is consistent with the previous observation by Yona and Levitt (17) that the position-speci®c weights of different amino acid types correlate very well with their secondary structure propensities for a given position and therefore structural similarities would be more easily recognized b”
A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design (2015, methodology): “These results highlight a caveat with using native sequence recovery as a test of protein design accuracy, which is that protein sequences can be very different from each other but still be consistent with the same protein fold [32], and it is this sequence divergence that can be utilized to evolve ”