ISIS: interaction sites identified from sequence

By Yanay Ofran, Burkhard Rost. 2007

274 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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In Molecular Biology

Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, background): “…aspects of protein function improved by combining evolutionary information and machine learning, including predictions of subcellular localization (aka cellular compartment or CC in GO [21, 22]), protein interaction sites [23 – 25], and the effects of sequence variation upon function [26, 27].”
Better prediction of functional effects for sequence variants (2015, neutral): “Additionally, we introduced new features for SNAP2: amino acid properties as provided by the AAindex database [42], predicted binding residues [43], predicted disordered regions [44], proximity to N- and C-terminus, statistical contact potentials [45], co-evolving positions, residue annotations from”
A Unified Multitask Architecture for Predicting Local Protein Properties (2012, methodology): “The authors plotted a precision-recall curve (though the terms ‘‘accuracy’’ and ‘‘coverage’’ of interaction were used in [50]) for different cut-offs on the predicted score, and found this strongest prediction.”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Later, methods predicting aspects of protein function improved through this combination, including predictions of sub-cellular localization (aka cellular location [21, 22]), protein interaction sites [23-25], and the effects of sequence variation on function [26, 27].”
Applying the Naïve Bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites (2010, methodology): “…servers; ISIS used ≤6 Å distance constraints for interatomic contacts between any atoms of the target chain and any atoms of the interacting chains (Ofran and Rost, 2007), and SPPIDER defined the interface residues based on >4% rSA change and >5 Å2 SA change on complex formation of the exposed…”
Predict impact of single amino acid change upon protein structure (2012, methodology): “In addition, we used the following predicted structural and functional features: secondary structure [32,33] and solvent accessibility [24,25,32], protein flexibility [34], protein disorder [35-38], protein-protein interaction hotspots [39-41] and DNA-binding residues [42].”
Protein–protein interaction and quaternary structure (2008, background): “…that are specifically designed for that purpose (Mintseris et al. 2007), and to complement the statistical approach with machine-learning procedures that can handle parameters not easily converted into potentials (Block et al. 2006 ; Zhu et al. 2006 ; Ofran & Rost, 2007 ; Bernauer et al. 2008).”
Disease-related mutations predicted to impact protein function (2012, methodology): “For many prediction methods developed in our group (protein-protein binding [24-26], protein-DNA binding [27], backbone flexibility [28]), the strength of an effect correlated with prediction strength, e.”
Protein–Protein Interaction Hotspots Carved into Sequences (2007, methodology): “ISIS is a knowledge-based method we developed to identify interface residues from sequence [28].”