Improving protein structure prediction using multiple sequence-based contact predictions.

By Sitao Wu, András Szilágyi, Yang Zhang. 2011

69 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Protein structure prediction from sequence variation (2012, background): “…contacts using three-dimensional information with machine-learning techniques, such as support vector machines, random forests and neural networks, but contact prediction accuracy remained “still quite low” 10 with substantial improvements to models achieved only for some small proteins 11,12 .”
Evaluation of residue-residue contact predictions in CASP9 (2011, background): “16 In some cases, quality of I-TASSER models improved by as much as 30%, resulting in de facto conversion of essentially ‘‘non-foldable’’ targets into ABSTRACT This work presents the results of the assessment of the intra-molecular residue–residue contact predictions submitted to CASP9.”
Ab Initio Protein Structure Assembly Using Continuous Structure Fragments and Optimized Knowledge-based Force Field (2012, background): “This is different from the traditional contact predictions from homologous templates55,56 or machine learning,57,58 but provides comparable accuracy for free-modeling targets.”
Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017, background): “7,8,24 The major advantage for contact based folding simulation methods is that long-range contacts provide a constraint to reduce conformational space to be sampled, and help in folding the proteins with a more complicated topology.”
Predicting protein contact map using evolutionary and physical constraints by integer programming (2013, background): “…sequence information has been an active research topic in recent years partially because contact map is helpful for protein 3D structure prediction (Ortiz et al., 1999; Vassura et al., 2008; Vendruscolo et al., 1997; Wu et al., 2011) and protein model quality assessment (Zhou and Skolnick, 2007).”
Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks (2020, background): “of FM targets (or hard targets in CAMEO) and on long-range contacts for evaluation, since the metric is most relevant for assisting the prediction of the tertiary structure of non-homologous proteins [6,33].”
CONFOLD: Residue-Residue Contact-guided ab initio Protein Folding (2015, background): “First strand is from residues 3 – 8, and second strand is from residues 12 – 17 for antiparallel pairs and 23 – 28 for parallel pairs.”
Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement (2011, methodology): “The I-TASSER force field includes four components: (1) general knowledge-based statistical terms from the PDB (C a /side-chain correlations, 37 hydrogen-bond, 38 and hydrophobicity39); (2) spatial restraints collected from threading templates7; (3) sequence-based C a /C b /side-chain center contact ”
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, background): “Cell This is an open access article under the CC BY-N cess (Kinch et al., 2011;Wu et al., 2011),mainly due to the lowaccuracy of contact-map predictions, which had typically less than 30% of the top L/5 long-range contacts, where L is the protein length and long range represents a sequence separatio”
Knowledge-based machine learning methods for macromolecular 3D structure prediction (2016, background): “…sequence information has been an active research topic in recent years partially because contact map is helpful for protein 3D structure prediction (Ortiz et al., 1999; Vassura et al., 2008; Vendruscolo et al., 1997; Wu et al., 2011) and protein model quality assessment (Zhou and Skolnick, 2007).”