Literature DB >> 19327982

Protein structure prediction: when is it useful?

Yang Zhang1.   

Abstract

Computationally predicted three-dimensional structure of protein molecules has demonstrated the usefulness in many areas of biomedicine, ranging from approximate family assignments to precise drug screening. For nearly 40 years, however, the accuracy of the predicted models has been dictated by the availability of close structural templates. Progress has recently been achieved in refining low-resolution models closer to the native ones; this has been made possible by combining knowledge-based information from multiple sources of structural templates as well as by improving the energy funnel of physics-based force fields. Unfortunately, there has been no essential progress in the development of techniques for detecting remotely homologous templates and for predicting novel protein structures.

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Year:  2009        PMID: 19327982      PMCID: PMC2673339          DOI: 10.1016/j.sbi.2009.02.005

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  53 in total

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2.  Molecular dynamics in the endgame of protein structure prediction.

Authors:  M R Lee; J Tsai; D Baker; P A Kollman
Journal:  J Mol Biol       Date:  2001-10-19       Impact factor: 5.469

3.  Completeness in structural genomics.

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Journal:  Nat Struct Biol       Date:  2001-06

4.  Local energy landscape flattening: parallel hyperbolic Monte Carlo sampling of protein folding.

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Journal:  Proteins       Date:  2002-08-01

5.  Can a physics-based, all-atom potential find a protein's native structure among misfolded structures? I. Large scale AMBER benchmarking.

Authors:  Liliana Wroblewska; Jeffrey Skolnick
Journal:  J Comput Chem       Date:  2007-09       Impact factor: 3.376

6.  Applying undertaker cost functions to model quality assessment.

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Journal:  Proteins       Date:  2009-05-15

7.  Coordinated Up-regulation by hypoxia of adrenomedullin and one of its putative receptors (RDC-1) in cells of the rat blood-brain barrier.

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Journal:  J Biol Chem       Date:  2000-12-22       Impact factor: 5.157

8.  CXCR7 (RDC1) promotes breast and lung tumor growth in vivo and is expressed on tumor-associated vasculature.

Authors:  Zhenhua Miao; Kathryn E Luker; Bretton C Summers; Rob Berahovich; Mahaveer S Bhojani; Alnawaz Rehemtulla; Celina G Kleer; Jeffrey J Essner; Aidas Nasevicius; Gary D Luker; Maureen C Howard; Thomas J Schall
Journal:  Proc Natl Acad Sci U S A       Date:  2007-09-26       Impact factor: 11.205

9.  Benchmarking consensus model quality assessment for protein fold recognition.

Authors:  Liam J McGuffin
Journal:  BMC Bioinformatics       Date:  2007-09-18       Impact factor: 3.169

Review 10.  In silico pharmacology for drug discovery: applications to targets and beyond.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

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  107 in total

1.  Structure prediction and binding sites analysis of curcin protein of Jatropha curcas using computational approaches.

Authors:  Mugdha Srivastava; Shishir K Gupta; P C Abhilash; Nandita Singh
Journal:  J Mol Model       Date:  2011-12-07       Impact factor: 1.810

2.  Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization.

Authors:  Dong Xu; Yang Zhang
Journal:  Biophys J       Date:  2011-11-15       Impact factor: 4.033

3.  How significant is a protein structure similarity with TM-score = 0.5?

Authors:  Jinrui Xu; Yang Zhang
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4.  On the significance of an RNA tertiary structure prediction.

Authors:  Christine E Hajdin; Feng Ding; Nikolay V Dokholyan; Kevin M Weeks
Journal:  RNA       Date:  2010-05-24       Impact factor: 4.942

5.  Functional implications of structural predictions for alternative splice proteins expressed in Her2/neu-induced breast cancers.

Authors:  Rajasree Menon; Ambrish Roy; Srayanta Mukherjee; Saveliy Belkin; Yang Zhang; Gilbert S Omenn
Journal:  J Proteome Res       Date:  2011-10-28       Impact factor: 4.466

6.  Automated protein structure modeling in CASP9 by I-TASSER pipeline combined with QUARK-based ab initio folding and FG-MD-based structure refinement.

Authors:  Dong Xu; Jian Zhang; Ambrish Roy; Yang Zhang
Journal:  Proteins       Date:  2011-08-23

7.  Structure-based barcoding of proteins.

Authors:  Rahul Metri; Gaurav Jerath; Govind Kailas; Nitin Gacche; Adityabarna Pal; Vibin Ramakrishnan
Journal:  Protein Sci       Date:  2014-01       Impact factor: 6.725

8.  Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

Authors:  Chengxin Zhang; S M Mortuza; Baoji He; Yanting Wang; Yang Zhang
Journal:  Proteins       Date:  2017-11-14

9.  Conformation dependence of backbone geometry in proteins.

Authors:  Donald S Berkholz; Maxim V Shapovalov; Roland L Dunbrack; P Andrew Karplus
Journal:  Structure       Date:  2009-10-14       Impact factor: 5.006

10.  Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles.

Authors:  Vahid Mirjalili; Michael Feig
Journal:  J Chem Theory Comput       Date:  2012-12-22       Impact factor: 6.006

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