Literature DB >> 20544969

Improving computational protein design by using structure-derived sequence profile.

Liang Dai1, Yuedong Yang, Hyung Rae Kim, Yaoqi Zhou.   

Abstract

Designing a protein sequence that will fold into a predefined structure is of both practical and fundamental interest. Many successful, computational designs in the last decade resulted from improved understanding of hydrophobic and polar interactions between side chains of amino acid residues in stabilizing protein tertiary structures. However, the coupling between main-chain backbone structure and local sequence has yet to be fully addressed. Here, we attempt to account for such coupling by using a sequence profile derived from the sequences of five residue fragments in a fragment library that are structurally matched to the five-residue segments contained in a target structure. We further introduced a term to reduce low complexity regions of designed sequences. These two terms together with optimized reference states for amino-acid residues were implemented in the RosettaDesign program. The new method, called RosettaDesign-SR, makes a 12% increase (from 34 to 46%) in fraction of proteins whose designed sequences are more than 35% identical to wild-type sequences. Meanwhile, it reduces 8% (from 22% to 14%) to the number of designed sequences that are not homologous to any known protein sequences according to psi-blast. More importantly, the sequences designed by RosettaDesign-SR have 2-3% more polar residues at the surface and core regions of proteins and these surface and core polar residues have about 4% higher sequence identity to wild-type sequences than by RosettaDesign. Thus, the proteins designed by RosettaDesign-SR should be less likely to aggregate and more likely to have unique structures due to more specific polar interactions. (c) 2010 Wiley-Liss, Inc.

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Year:  2010        PMID: 20544969      PMCID: PMC3058783          DOI: 10.1002/prot.22746

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


  83 in total

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3.  PISCES: a protein sequence culling server.

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4.  The dependence of all-atom statistical potentials on structural training database.

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Journal:  Biophys J       Date:  2004-06       Impact factor: 4.033

5.  Development and testing of a general amber force field.

Authors:  Junmei Wang; Romain M Wolf; James W Caldwell; Peter A Kollman; David A Case
Journal:  J Comput Chem       Date:  2004-07-15       Impact factor: 3.376

Review 6.  Potential energy functions for protein design.

Authors:  F Edward Boas; Pehr B Harbury
Journal:  Curr Opin Struct Biol       Date:  2007-03-26       Impact factor: 6.809

Review 7.  Full-sequence computational design and solution structure of a thermostable protein variant.

Authors:  Premal S Shah; Geoffrey K Hom; Scott A Ross; Jonathan Kyle Lassila; Karin A Crowhurst; Stephen L Mayo
Journal:  J Mol Biol       Date:  2007-06-16       Impact factor: 5.469

8.  Using protein design for homology detection and active site searches.

Authors:  Jimin Pei; Nikolay V Dokholyan; Eugene I Shakhnovich; Nick V Grishin
Journal:  Proc Natl Acad Sci U S A       Date:  2003-09-15       Impact factor: 11.205

9.  A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins.

Authors:  Gautam Dantas; Brian Kuhlman; David Callender; Michelle Wong; David Baker
Journal:  J Mol Biol       Date:  2003-09-12       Impact factor: 5.469

10.  Structure-based substitutions for increased solubility of a designed protein.

Authors:  Leila K Mosavi; Zheng-Yu Peng
Journal:  Protein Eng       Date:  2003-10
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  9 in total

1.  Characterizing the existing and potential structural space of proteins by large-scale multiple loop permutations.

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2.  Detecting local residue environment similarity for recognizing near-native structure models.

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Review 3.  Energy functions in de novo protein design: current challenges and future prospects.

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4.  Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles.

Authors:  Zhixiu Li; Yuedong Yang; Eshel Faraggi; Jian Zhan; Yaoqi Zhou
Journal:  Proteins       Date:  2014-06-19

5.  ProDCoNN: Protein design using a convolutional neural network.

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Journal:  Proteins       Date:  2020-01-06

6.  PyIgClassify: a database of antibody CDR structural classifications.

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Journal:  Nucleic Acids Res       Date:  2014-11-11       Impact factor: 19.160

7.  Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes.

Authors:  Hege Beard; Anuradha Cholleti; David Pearlman; Woody Sherman; Kathryn A Loving
Journal:  PLoS One       Date:  2013-12-10       Impact factor: 3.240

8.  DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indels.

Authors:  Huiying Zhao; Yuedong Yang; Hai Lin; Xinjun Zhang; Matthew Mort; David N Cooper; Yunlong Liu; Yaoqi Zhou
Journal:  Genome Biol       Date:  2013-03-13       Impact factor: 13.583

9.  Use of designed sequences in protein structure recognition.

Authors:  Gayatri Kumar; Richa Mudgal; Narayanaswamy Srinivasan; Sankaran Sandhya
Journal:  Biol Direct       Date:  2018-05-09       Impact factor: 4.540

  9 in total

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