Literature DB >> 8768901

The current state of the art in protein structure prediction.

J Moult1.   

Abstract

The capabilities of current protein structure prediction methods have been assessed from the outcome of a set of blind tests. In comparative modeling, many of the numerical methods did not perform as well as expected, although the resulting structures are still of great practical use. The new methods of fold identification ('threading') were partially successful, and show considerable promise for the future. Except for secondary structure data, results from traditional ab initio methods were poor. A second blind prediction experiment is underway, and progress in all areas is expected.

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Year:  1996        PMID: 8768901     DOI: 10.1016/s0958-1669(96)80118-2

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  9 in total

1.  SWISS-MODEL: An automated protein homology-modeling server.

Authors:  Torsten Schwede; Jürgen Kopp; Nicolas Guex; Manuel C Peitsch
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

2.  Comparative protein structure modeling by iterative alignment, model building and model assessment.

Authors:  Bino John; Andrej Sali
Journal:  Nucleic Acids Res       Date:  2003-07-15       Impact factor: 16.971

3.  Assigning folds to the proteins encoded by the genome of Mycoplasma genitalium.

Authors:  D Fischer; D Eisenberg
Journal:  Proc Natl Acad Sci U S A       Date:  1997-10-28       Impact factor: 11.205

4.  Protein thermostability above 100 degreesC: a key role for ionic interactions.

Authors:  C Vetriani; D L Maeder; N Tolliday; K S Yip; T J Stillman; K L Britton; D W Rice; H H Klump; F T Robb
Journal:  Proc Natl Acad Sci U S A       Date:  1998-10-13       Impact factor: 11.205

Review 5.  Industrial methodology for process verification in research (IMPROVER): toward systems biology verification.

Authors:  Pablo Meyer; Julia Hoeng; J Jeremy Rice; Raquel Norel; Jörg Sprengel; Katrin Stolle; Thomas Bonk; Stephanie Corthesy; Ajay Royyuru; Manuel C Peitsch; Gustavo Stolovitzky
Journal:  Bioinformatics       Date:  2012-03-14       Impact factor: 6.937

6.  Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach.

Authors:  Pablo Meyer; Geoffrey Siwo; Danny Zeevi; Eilon Sharon; Raquel Norel; Eran Segal; Gustavo Stolovitzky
Journal:  Genome Res       Date:  2013-08-15       Impact factor: 9.043

Review 7.  An RNA-centric historical narrative around the Protein Data Bank.

Authors:  Eric Westhof; Neocles B Leontis
Journal:  J Biol Chem       Date:  2021-03-18       Impact factor: 5.157

8.  Current structure predictors are not learning the physics of protein folding.

Authors:  Carlos Outeiral; Daniel A Nissley; Charlotte M Deane
Journal:  Bioinformatics       Date:  2022-01-31       Impact factor: 6.937

9.  Improving breast cancer survival analysis through competition-based multidimensional modeling.

Authors:  Erhan Bilal; Janusz Dutkowski; Justin Guinney; In Sock Jang; Benjamin A Logsdon; Gaurav Pandey; Benjamin A Sauerwine; Yishai Shimoni; Hans Kristian Moen Vollan; Brigham H Mecham; Oscar M Rueda; Jorg Tost; Christina Curtis; Mariano J Alvarez; Vessela N Kristensen; Samuel Aparicio; Anne-Lise Børresen-Dale; Carlos Caldas; Andrea Califano; Stephen H Friend; Trey Ideker; Eric E Schadt; Gustavo A Stolovitzky; Adam A Margolin
Journal:  PLoS Comput Biol       Date:  2013-05-09       Impact factor: 4.475

  9 in total

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