Literature DB >> 15504402

A simple physical model for the prediction and design of protein-DNA interactions.

James J Havranek1, Carlos M Duarte, David Baker.   

Abstract

Protein-DNA interactions are crucial for many biological processes. Attempts to model these interactions have generally taken the form of amino acid-base recognition codes or purely sequence-based profile methods, which depend on the availability of extensive sequence and structural information for specific structural families, neglect side-chain conformational variability, and lack generality beyond the structural family used to train the model. Here, we take advantage of recent advances in rotamer-based protein design and the large number of structurally characterized protein-DNA complexes to develop and parameterize a simple physical model for protein-DNA interactions. The model shows considerable promise for redesigning amino acids at protein-DNA interfaces, as design calculations recover the amino acid residue identities and conformations at these interfaces with accuracies comparable to sequence recovery in globular proteins. The model shows promise also for predicting DNA-binding specificity for fixed protein sequences: native DNA sequences are selected correctly from pools of competing DNA substrates; however, incorporation of backbone movement will likely be required to improve performance in homology modeling applications. Interestingly, optimization of zinc finger protein amino acid sequences for high-affinity binding to specific DNA sequences results in proteins with little or no predicted specificity, suggesting that naturally occurring DNA-binding proteins are optimized for specificity rather than affinity. When combined with algorithms that optimize specificity directly, the simple computational model developed here should be useful for the engineering of proteins with novel DNA-binding specificities.

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Year:  2004        PMID: 15504402     DOI: 10.1016/j.jmb.2004.09.029

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  54 in total

1.  A new hydrogen-bonding potential for the design of protein-RNA interactions predicts specific contacts and discriminates decoys.

Authors:  Yu Chen; Tanja Kortemme; Tim Robertson; David Baker; Gabriele Varani
Journal:  Nucleic Acids Res       Date:  2004-09-30       Impact factor: 16.971

2.  Prediction of DNA-binding specificity in zinc finger proteins.

Authors:  Sumedha Roy; Shayoni Dutta; Kanika Khanna; Shruti Singla; Durai Sundar
Journal:  J Biosci       Date:  2012-07       Impact factor: 1.826

3.  Decoding transcriptional regulatory interactions.

Authors:  L Angela Liu; Joel S Bader
Journal:  Physica D       Date:  2006-12       Impact factor: 2.300

Review 4.  Prediction and design of macromolecular structures and interactions.

Authors:  David Baker
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2006-03-29       Impact factor: 6.237

5.  Motif-directed flexible backbone design of functional interactions.

Authors:  James J Havranek; David Baker
Journal:  Protein Sci       Date:  2009-06       Impact factor: 6.725

6.  Recent computational approaches to understand gene regulation: mining gene regulation in silico.

Authors:  I Abnizova; T Subhankulova; Wr Gilks
Journal:  Curr Genomics       Date:  2007-04       Impact factor: 2.236

7.  Experimental determination of the evolvability of a transcription factor.

Authors:  Sebastian J Maerkl; Stephen R Quake
Journal:  Proc Natl Acad Sci U S A       Date:  2009-10-19       Impact factor: 11.205

8.  De novo prediction of DNA-binding specificities for Cys2His2 zinc finger proteins.

Authors:  Anton V Persikov; Mona Singh
Journal:  Nucleic Acids Res       Date:  2013-10-03       Impact factor: 16.971

9.  Assessment of the optimization of affinity and specificity at protein-DNA interfaces.

Authors:  Justin Ashworth; David Baker
Journal:  Nucleic Acids Res       Date:  2009-04-23       Impact factor: 16.971

10.  From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions.

Authors:  Mu Gao; Jeffrey Skolnick
Journal:  PLoS Comput Biol       Date:  2009-04-03       Impact factor: 4.475

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