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Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition
BACKGROUND: Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences c...
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Formato: | Texto |
Lenguaje: | English |
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Public Library of Science
2010
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2864755/ https://www.ncbi.nlm.nih.gov/pubmed/20463972 http://dx.doi.org/10.1371/journal.pone.0010410 |
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author | Schmidt am Busch, Marcel Sedano, Audrey Simonson, Thomas |
author_facet | Schmidt am Busch, Marcel Sedano, Audrey Simonson, Thomas |
author_sort | Schmidt am Busch, Marcel |
collection | PubMed |
description | BACKGROUND: Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases. METHODOLOGY/PRINCIPAL FINDINGS: We explore this strategy for four SCOP families: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000–300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed. CONCLUSIONS/SIGNIFICANCE: For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use. |
format | Text |
id | pubmed-2864755 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-28647552010-05-12 Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition Schmidt am Busch, Marcel Sedano, Audrey Simonson, Thomas PLoS One Research Article BACKGROUND: Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases. METHODOLOGY/PRINCIPAL FINDINGS: We explore this strategy for four SCOP families: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000–300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed. CONCLUSIONS/SIGNIFICANCE: For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use. Public Library of Science 2010-05-05 /pmc/articles/PMC2864755/ /pubmed/20463972 http://dx.doi.org/10.1371/journal.pone.0010410 Text en Schmidt am Busch et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Schmidt am Busch, Marcel Sedano, Audrey Simonson, Thomas Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition |
title | Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition |
title_full | Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition |
title_fullStr | Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition |
title_full_unstemmed | Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition |
title_short | Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition |
title_sort | computational protein design: validation and possible relevance as a tool for homology searching and fold recognition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2864755/ https://www.ncbi.nlm.nih.gov/pubmed/20463972 http://dx.doi.org/10.1371/journal.pone.0010410 |
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