Literature DB >> 8710832

Fold recognition and ab initio structure predictions using hidden Markov models and beta-strand pair potentials.

T J Hubbard1, J Park.   

Abstract

Protein structure predictions were submitted for 9 of the target sequences in the competition that ran during 1994. Targets sequences were selected that had no known homology with any sequence of known structure and were members of a reasonably sized family of related but divergent sequences. The objective was either to recognize a compatible fold for the target sequence in the database of known structures or to predict ab initio its rough 3D topology. The main tools used were Hidden Markov models (HMM) for fold recognition, a beta-strand pair potential to predict beta-sheet topology, and the PHD server for secondary structure prediction. Compatible folds were correctly identified in a number of cases and the beta-strand pair potential was shown to be a useful tool for ab initio topology prediction.

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Year:  1995        PMID: 8710832     DOI: 10.1002/prot.340230313

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  7 in total

1.  BETAWRAP: successful prediction of parallel beta -helices from primary sequence reveals an association with many microbial pathogens.

Authors:  P Bradley; L Cowen; M Menke; J King; B Berger
Journal:  Proc Natl Acad Sci U S A       Date:  2001-12-18       Impact factor: 11.205

2.  Improving strand pairing prediction through exploring folding cooperativity.

Authors:  Jieun Jeong; Piotr Berman; Teresa M Przytycka
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2008 Oct-Dec       Impact factor: 3.710

3.  Determinants of strand register in antiparallel beta-sheets of proteins.

Authors:  E G Hutchinson; R B Sessions; J M Thornton; D N Woolfson
Journal:  Protein Sci       Date:  1998-11       Impact factor: 6.725

4.  Recognition of beta-structural motifs using hidden Markov models trained with simulated evolution.

Authors:  Anoop Kumar; Lenore Cowen
Journal:  Bioinformatics       Date:  2010-06-15       Impact factor: 6.937

5.  Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design I: discovery of anticancer compounds.

Authors:  Humberto Gonzáles-Díaz; Ornella Gia; Eugenio Uriarte; Ivan Hernádez; Ronal Ramos; Mayrelis Chaviano; Santiago Seijo; Juan A Castillo; Lázaro Morales; Lourdes Santana; Delali Akpaloo; Enrique Molina; Maikel Cruz; Luis A Torres; Miguel A Cabrera
Journal:  J Mol Model       Date:  2003-09-16       Impact factor: 1.810

6.  A Study of Domain Adaptation Classifiers Derived From Logistic Regression for the Task of Splice Site Prediction.

Authors:  Nic Herndon; Doina Caragea
Journal:  IEEE Trans Nanobioscience       Date:  2016-01-28       Impact factor: 2.935

7.  β-sheet topology prediction with high precision and recall for β and mixed α/β proteins.

Authors:  Ashwin Subramani; Christodoulos A Floudas
Journal:  PLoS One       Date:  2012-03-09       Impact factor: 3.240

  7 in total

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