PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

By David T. Jones, Daniel W. A. Buchan, Domenico Cozzetto, Massimiliano Pontil. 2012

763 citations recorded by the source corpus.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

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

In Molecular Biology

In Computational Biology & Bioinformatics

Citations in the corpus

High-resolution de novo structure prediction from primary sequence (2022, background): “Unsupervised pre-trained models have achieved tremendous success in research areas such as natural language processing (1–4), computer vision (5–8) and single cell analysis (9, 10).”
CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, methodology): “Following the convention [14], we calculate the weight F: as the inverse of the number of similar homologue proteins that share at least 80% sequence identity with the :-th homologue, and thus "eff represents the number of effective homologue proteins in the MSA.”
CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations (2014, methodology): “, 2013) and PSICOV (Jones et al., 2012), we apply the Average Product Correction (Dunn et al.”
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, methodology): “PSICOV assumes that P(X ) is a Gaussian distribution function and calculates the correlation between two columns by inverse covariance matrix.”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, methodology): “Previous approaches ( Morcos et al., 2011 ; Jones et al., 2012 ) estimated v, w using an approximate moment matching approach ( Kamisetty et al., 2013 ) by inverting a generalized covariance matrix.”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, background): “…residue-residue contacts by learning an inherent graphical model structure that incorporates pairwise terms to capture evolutionary constraints among residues (Ekeberg et al., 2013; Morcos et al., 2011; Kamisetty et al., 2013; Jones et al., 2012, 2015; Marks et al., 2011; Kaján et al., 2014).”
3D structures of membrane proteins from genomic sequencing (2012, neutral): “Themostaccurateofthesestrategiesuseglobalstatistical methods, such as maximum entropy (Marks et al., 2011; Morcos et al., 2011), Bayesian networks (Burger and van Nimwegen, 2010), or covariance estimation (Jones et al., 2012; Meinshausen andBuhlmann,2006).”