A method to identify protein sequences that fold into a known three-dimensional structure.

By J. Bowie, R. Lüthy, D. Eisenberg. 1991

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yang Zhang
2. Ben M. Webb
3. Sheng Wang
4. Jinbo Xu
5. Jianlin Cheng
6. Robin Pearce
7. Andrej Sali
8. Björn Wallner

Citations in the corpus

• Template-based protein structure modeling using the RaptorX web server (2012, background): “It is thus often found that evolutionarily distant or unrelated protein sequences share common structural elements, which is used by threading methods 8,9 such as MUSTER 10 , SPARKS 11,12 and RAPTOR 13–15 .”
• An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction. (1998, background): “…knowledge-based discriminatory functions have gained popularity in their application to the ``fold recognition'' problem, i.e. recognising the fold an amino acid belongs to in the absence of detectable sequence homology (Sippl, 1990; Bowie et al., 1991; Jones et al., 1992; Bryant & Lawrence, 1993).”
• Statistical potential for assessment and prediction of protein structures (2006, unknown): “2006), ab initio protein structure prediction (Bowie et al. 1991; Sun 1993; O’Donoghue and Nilges 1997; Chiu and Goldstein 2000; Tobi and Elber 2000; Tobi et al. 2000), fold recognition or threading (Maiorov and Crippen 1992; Sippl and Weitckus 1992; Bryant and Lawrence 1993; Ouzounis et al.”
• Visualizing and quantifying molecular goodness-of-fit: small-probe contact dots with explicit hydrogen atoms. (1999, background): “Since such designed models often look good by most criteria (such as sequence-structurè`threading''; e.g. Bowie et al., 1991) but are much less well ordered than natural proteins when they are actually produced (e.g. Richardson et al., 1992; Betz et al., 1993), it is important to develop more…”
• Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks (2000, background): “2 AEFIKLRV I ILPV IP GP G m [24; 16] 1 AEL ADEQ AELMQ AELMQR AEKLMQR AEKLMQR AEKLMQR AKLMQR AKLMR 2 GP GPV GNPST GNPST DGPST DGPST DGPSTV DGPSTV DPSTV n [0; 12] 1 E L AL KR AEK L GN P 2 GP GPV AEFILPSTVY o [21; 11] 1 Y AL K AEK GN K P V 2 P GPV AEFILPSTVY p [21; 12] 1 A D DKN GN I DP PV 2 G GIPV AFI”
• SPARKS 2 and SP3 servers in CASP6 (2005, background): “18–23 More recent work attempts to optimally combine the sequence and structure information for a more accurate/sensitive fold recognition.”
• Template-based structure modeling of protein-protein interactions (2013, methodology): “Dimeric threading The first and probably the earliest strategy is the dimeric threading method [24], which is an extension of the single-chain threading (or fold-recognition) approach widely used in tertiary protein structure prediction [14,16].”
• Statistical potentials extracted from protein structures: how accurate are they? (1996, background): “, 1992; Bryant & Lawrence, 1993), to identify possible folds for a sequence of unknown structure (Bowie et al., 1991; Sippl & Weitckus, 1992), to predict docking of protein structures (Pellegrini & Doniach, 1993), to find amino acid sequences compatible with a desired structure (Godzik & Skolnick, 1”
• MUFOLD: A new solution for protein 3D structure prediction (2010, methodology): “Computational protein structure prediction methods can be classified into three categories: (1) comparative modeling (CM),3–7 (2) threading,8–16 and (3) ab initio prediction.”
• Progress and challenges in protein structure prediction. (2008, background): “Template structure identification Since its invention in the early 1990s [6,7], threading has become one of the most active areas in proteins structure prediction.”