Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis

By Timothy Nugent, David T. Jones. 2012

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In Molecular Biology

Citations in the corpus

Protein Structure Determination using Metagenome sequence data (2017, neutral): “for sep_x (0,1,2) for sep_y (1,2,4, 8,16,32) ini_mtx = initialize_matrix (sep_x , sep_y) for gap_e (-0.”
CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations (2014, background): “This has resulted in a boost in contact prediction accuracy, thanks to which it is now possible to reliably predict protein structures using only sequence information if enough homologous sequences are available (Hopf et al., 2012; Marks et al., 2011; Nugent and Jones, 2012).”
Protein structure prediction from sequence variation (2012, methodology): “Going all the way from multiply aligned sequence families via predicted residues couplings and contacts to often well-folded predicted three-dimensional structures has now been achieved in several reports ( Table 1 ) 15,22–24 .”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, background): “…model ( Thomas et al., 2008 ) to distinguish between direct and indirect couplings ( Marks et al., 2011 ; Morcos et al., 2011 ; Hopf et al., 2012 ; Nugent and Jones, 2012 ; Jones et al., 2012 ; Lapedes et al., 2012 ; Marks et al., 2012 ; Sułkowska et al., 2012 ; Kamisetty et al., 2013 ).”
MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins (2014, background): “, 2014) and membrane proteins (Hopf et al., 2012; Nugent and Jones, 2012), as well as identifying residues at the interface of protein complexes (Ovchinnikov et al.”
Large-scale determination of previously unsolved protein structures using evolutionary information (2015, background): “…that complicate approaches to membrane protein structure prediction that assume the accuracy of a TM helix prediction were all recovered correctly (for example, the re-entrant helices of aquaporin; the power of fragment-based approaches to model such features was noted in Nugent and Jones, 2012 ).”
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, background): “It has been shown that these global contact prediction methods actually provide sufficient information for predicting the structure of proteins belonging to large protein families [7,19,20].”
Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners (2014, neutral): “Inferred contacts have been shown to be sufficient to guide the assembly of complexes between proteins of known (or homology modeled) monomer structure [24,25], and to predict the fold of single proteins [26–31], including highlights like large transmembrane proteins [28,31].”
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, background): “…sequencing and better statistical and optimization techniques, evolutionary coupling (EC) analysis for contact prediction has made good progress, which makes de novo prediction of some large proteins possible (Hopf et al., 2012; Marks et al., 2011; Nugent and Jones, 2012; Skwark et al., 2013).”
Accurate contact predictions using covariation techniques and machine learning (2015, methodology): “In case of transmembrane (TM) proteins we used FILM3,24 instead of FRAGFOLD.”
Computation and Functional Studies Provide a Model for the Structure of the Zinc Transporter hZIP4* (2015, methodology): “Residue-Residue Contacts Predicted from Co-evolution— Membrane structures have been modeled using contact prediction based on co-evolutionary patterns in large multiple sequence alignments of homologous proteins (32, 33).”
Applications of sequence coevolution in membrane protein biochemistry (2017, unknown): “Alcock et al. (2016) used direct methods (CCMpred, FreeContact, and PSICOV) to detect interactions between TAT subunits [145].”
Protein contact prediction using metagenome sequence data and residual neural networks (2019, methodology): “The information included in a contact map can be used as distance restraints to guide protein structure modeling (Hopf et al., 2012; Kim et al., 2014; Kosciolek and Jones, 2014; Marks et al., 2011, 2012; Nugent and Jones, 2012; Ortiz et al., 1999; Ovchinnikov et al., 2015, 2016; Sadowski et al., 201”