Literature DB >> 10611398

The bottom line for prediction of residue solvent accessibility.

C J Richardson1, D J Barlow.   

Abstract

A simple method of predicting residue solvent accessibilities in proteins is described, with the intention that it should be used as a baseline by which more sophisticated approaches to prediction can be judged. Comparison with existing methods of predicting residue burial reveals that their performance is often little better than that of the baseline method. The problem of comparing different prediction methods is shown to be complicated by the proliferation of different schemes for classifying residue burial.

Mesh:

Substances:

Year:  1999        PMID: 10611398     DOI: 10.1093/protein/12.12.1051

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  5 in total

1.  Real value prediction of protein solvent accessibility using enhanced PSSM features.

Authors:  Darby Tien-Hao Chang; Hsuan-Yu Huang; Yu-Tang Syu; Chih-Peng Wu
Journal:  BMC Bioinformatics       Date:  2008-12-12       Impact factor: 3.169

2.  A unified multitask architecture for predicting local protein properties.

Authors:  Yanjun Qi; Merja Oja; Jason Weston; William Stafford Noble
Journal:  PLoS One       Date:  2012-03-26       Impact factor: 3.240

3.  A two-stage approach for improved prediction of residue contact maps.

Authors:  Alessandro Vullo; Ian Walsh; Gianluca Pollastri
Journal:  BMC Bioinformatics       Date:  2006-03-30       Impact factor: 3.169

4.  Context dependent reference states of solvent accessibility derived from native protein structures and assessed by predictability analysis.

Authors:  Hemajit Singh; Shandar Ahmad
Journal:  BMC Struct Biol       Date:  2009-04-27

5.  Logistic regression models to predict solvent accessible residues using sequence- and homology-based qualitative and quantitative descriptors applied to a domain-complete X-ray structure learning set.

Authors:  Reecha Nepal; Joanna Spencer; Guneet Bhogal; Amulya Nedunuri; Thomas Poelman; Thejas Kamath; Edwin Chung; Katherine Kantardjieff; Andrea Gottlieb; Brooke Lustig
Journal:  J Appl Crystallogr       Date:  2015-11-10       Impact factor: 3.304

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.