Real value prediction of solvent accessibility from amino acid sequence

By Shandar Ahmad, M. Michael Gromiha, Akinori Sarai. 2003

202 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, methodology): “ASA output is not directly predicted, but calculated by multiplying RSA and ASAmax (25).”
iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content (2010, methodology): “Similarly, the predictors of the residue-level descriptors include methods for prediction of the secondary structure (Jones 1999; McGuffin et al. 2000; Rost 2001, 2005, 2008; Rost et al. 2004; Pollastri and McLysaght 2005; Ofer and Yaoqi 2007; Montgomerie et al. 2008; Kurgan 2008; Ding et al. 2009),”
Ab initio modeling of small proteins by iterative TASSER simulations (2007, unknown): “[34].”
Predicting absolute contact numbers of native protein structure from amino acid sequence (2004, background): “4–14 In spite of the continuous nature of accessibility, almost all the prediction methods predict it in terms of a few (2, 3, or 10) discrete states (an exception is that of Ahmad et al., 13 which does predict continuous values).”
Prediction‐based fingerprints of protein–protein interactions (2006, methodology): “Furthermore, we show that among real valued RSA prediction methods assessed in this work, including PHDacc, 37 RVPNet, 38 PROF, 39 and SABLE, 35 the latter provides predictions that are most consistent with the RSA observed in protein complexes, yielding the best discrimination between interacting a”
Prediction of protein B‐factor profiles (2005, unknown): “25,44,45”
A generic method for assignment of reliability scores applied to solvent accessibility predictions (2009, unknown): “Two sequences were not accepted by the server leaving us with a set of 511 sequences (CB511) used when comparing the performance of NetSurfP and several other methods [5,20,22,24].”
Accurate prediction of solvent accessibility using neural networks–based regression (2004, methodology): “A novel method that provides real-value RSA prediction, based on simple binary encoding of amino acid sequences and feed forward NN with two output nodes and subsequent transformation of their excitations into a real-value prediction, was published recently.(17) While this new method goes beyond the”