Genomics-aided structure prediction

By Joanna I. Sulkowska, Faruck Morcos, Martin Weigt, Terence Hwa, José N. Onuchic. 2012

222 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “[41] Joanna I Sułkowska, Faruck Morcos, Martin Weigt, Terence Hwa, and José N Onuchic.”
Protein structure prediction from sequence variation (2012, methodology): “The Onuchic group 23 , using a protocol called DCAfold, predicted three-dimensional structures of 15 bacterial protein domains up to 133 residues (in their test set) using the information content in evolutionary P E R S P E C T I V E n p g the next few years and will benefit from the continuing…”
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 ).”
Large-scale determination of previously unsolved protein structures using evolutionary information (2015, background): “…that residue–residue contacts can be accurately inferred from co-evolution patterns in sequences of related proteins ( Marks et al., 2011 ; Morcos et al., 2011 ; Hopf et al., 2012 ; Jones et al., 2012 ; Marks et al., 2012 ; Nugent and Jones, 2012 ; Sułkowska et al., 2012 ; Kamisetty et al., 2013 ).”
Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners (2014, neutral): “t the in uence of the sequence by the factor wm = 1=nm. This leads to a redenition of the empirical means and covariances (see eqs. 1 and 2 in the Main Text), for 1 i;j;N: x i = 1 M e XM m=1 wmxm (29) C ij = 1 M e XM m=1 wm (xm i x i) xm j x j ; (30) where M e = P M m=1 w m is a normalization factor”
Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning (2013, methodology): “Results: This article presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning to improve accuracy on proteins without many sequence homologs.”
Transformer protein language models are unsupervised structure learners (2020, methodology): “For example, EVFold (Marks et al., 2011) and DCAFold (Sulkowska et al., 2012) predict co-evolving couplings using a Potts Model and then generate 3D conformations by directly folding an initial conformation with simulated annealing, using the predicted residue-residue contacts as constraints.”
De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts (2014, background): “Recent advancements in intra-protein contact predictions [6,7,20,21], and important breakthroughs in the prediction of transmembrane protein structures [22,23] prove that the approach of utilizing genomic-scale sequence information to infer residue coupling information can substantially aid research”
Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes (2016, methodology): “DCA has been used to identify highly coevolving pairs of residues to predict the native state conformation of a protein (Marks et al. 2012; Sulkowska et al. 2012; Sutto et al. 2015), including repeat proteins (Espada et al. 2015), as well as identify additional functionally relevant conformational…”
Inferring interaction partners from protein sequences (2016, background): “[10] Su lkowska JI, Morcos F, Weigt M, Hwa T, Onuchic JN (2012) Genomics-aided structure prediction.”