Fold recognition by combining sequence profiles derived from evolution and from depth‐dependent structural alignment of fragments

By Hongyi Zhou, Yaoqi Zhou. 2004

251 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

How researchers see this paper

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

In Materials Chemistry

In Molecular Biology

Citations in the corpus

LOMETS: A local meta-threading-server for protein structure prediction (2007, methodology): “LOMETS server takes predictions from nine diﬀerent servers that represent a diverse set of state-of-the-art threading algorithms, i.e. FUGUE (13), HHSEARCH (14), PROSPECT2 (15), SAM-T02 (16), SPARKS2 (17), SP3 (18) programs were copied from other laboratories and the last three developed in our own ”
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, background): “As a result, the parameters Z 0 ð j Þ (and conf ð j Þ ) are 83.0 (0.589), 6.9 (0.587), 33.0 (0.574), 8.7 (0.570), 6.1 (0.569), 10.0 (0.567), 7.0 (0.566), 7.6 (0.562), 3.2 (0.558), 21.0 (0.536), and 5.6 (0.617) for HHpred, SparksX, FFAS3D, Neff-MUSTER, MUSTER, HHsearch, SP3, PPAS, PROSPEC T2, PRC, an”
Structural bioinformatics A machine learning information retrieval approach to protein fold recognition (2006, unknown): “, 2003), SPARKS (Zhou and Zhou, 2004) and SP(3) (Zhou and Zhou, 2005).”
SP5: Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model (2008, methodology): “The first three terms constitute the method SP 3 [11] except that PSIPRED [29] rather than SPINE [31] was used in SP 3 to predict the secondary structure of the query sequence.”
SPARKS 2 and SP3 servers in CASP6 (2005, methodology): “36 and SP 3 (Zhou and Zhou 38 ) have been published elsewhere.”
EigenTHREADER: analogous protein fold recognition by efficient contact map threading (2017, methodology): “Popular methods make use of computational methods as diverse as: dynamic programming, Support Vector Machines, neural networks, Hidden Markov Models, profile-profile comparison and so forth (Rost et al., 1997; Olmea et al., 1999; Zhou and Zhou, 2005; Wu and Zhang, 2008; Lobley et al., 2009; Peng and”
Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “Commonly used threading programs are GenTHREADER (Jones, 1999; McGufﬁn and Jones, 2003), 3D-PSSM (Kelley et al., 2000), FUGUE (Shi et al., 2001), SP3 (Zhou and Zhou, 2005), SAM-T08 multi-track HMM (Karchin et al., 2003; Karplus et al., 2003), and MUSTER (Wu and Zhang, 2008).”
Improving protein fold recognition by random forest (2014, background): “…folds when there is little sequence similarity between a target and a template protein, such as PSI-BLAST [18], HMMER [19], SAM-T98 [20], SSHMM [21],THREADER [22], FUGUE [23], SPARKS [24], SP3 [25], HHpred [26], FOLDpro [5], SP4 [27], SP5 [28], RAPTOR [29], SPARKS-X [30], and BoostThreader [31].”
Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta (2016, methodology): “19 Previously described sequence profile, secondary structure (SS), and Ramachandran-based score terms were used for fragment selection, in addition to depth dependent structure profile,20 phi and psi torsion,21 and solvent accessibil-ity21 score terms.”
Protein structure modeling with MODELLER. (2014, background): “The differences are usually in the form of the gap-penalty function (linear, affine, or variable)(16), the substitution matrix used to score the aligned residues (20x20 matrices derived from alignments with a given sequence identity, those derived from structural alignments, and those incorporating ”
Energy functions in de novo protein design: current challenges and future prospects. (2013, methodology): “This structure-derived sequence profile was successfully employed for protein structure prediction (121) and protein design (112).”