Combining prediction of secondary structure and solvent accessibility in proteins

By Rafal Adamczak, Aleksey A. Porollo, Jaroslaw Meller. Proteins: Structure, Function, and Bioinformatics, 2005

326 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/73208be709a8c66be2ff1f16755d1fca8b3911f1

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In Molecular Biology

Citations in the corpus

Prediction‐based fingerprints of protein–protein interactions (2006, background): “35,36,66 In this work, we demonstrate how these real valued RSA predictions can be used to improve the recognition of protein interaction sites in the case when a structure of an individual protein chain is known (without knowing interacting partners).”
Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, methodology): “Its most recent release monitored 13 SS predictors, including PHD [23], PORTER [24], PSIPRED [25], SABLE [26], SSpro [27] and YASPIN [28] that are considered in this study.”
Computational prediction of secondary and supersecondary structures. (2013, methodology): “Two recent comparative studies (16, 39) and prior evaluations within the framework of the EVA initiative show that SABLE achieves accuracy of about 78%.”
A Unified Multitask Architecture for Predicting Local Protein Properties (2012, background): “Many previous authors have combined tasks: transmembrane topology and signal peptide prediction [58], secondary structure and solvent accessibility [8], secondary structure, solvent accessibility and DNA-binding sites [7].”
SP5: Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model (2008, methodology): “…[29] rather than SPINE [31] was employed to predict the secondary structure of the query sequence, and the real values of solvent accessibility from Real-SPINE [25] rather than two-state classifications by SABLE [34] are used to predict the residue solvent accessibility of the query sequence.”