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Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics
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| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , , |
| In: | Bioinformatics, 25, 2009, 5, S. 606-614 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
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| Schlagwörter: |
| author_facet |
Yang, Lee-Wei Eyal, Eran Bahar, Ivet Kitao, Akio Yang, Lee-Wei Eyal, Eran Bahar, Ivet Kitao, Akio |
|---|---|
| author |
Yang, Lee-Wei Eyal, Eran Bahar, Ivet Kitao, Akio |
| spellingShingle |
Yang, Lee-Wei Eyal, Eran Bahar, Ivet Kitao, Akio Bioinformatics Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
yang, lee-wei |
| spelling |
Yang, Lee-Wei Eyal, Eran Bahar, Ivet Kitao, Akio 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btp023 <jats:title>Abstract</jats:title> <jats:p>Motivation: To efficiently analyze the ‘native ensemble of conformations’ accessible to proteins near their folded state and to extract essential information from observed distributions of conformations, reliable mathematical methods and computational tools are needed.</jats:p> <jats:p>Result: Examination of 24 pairs of structures determined by both NMR and X-ray reveals that the differences in the dynamics of the same protein resolved by the two techniques can be tracked to the most robust low frequency modes elucidated by principal component analysis (PCA) of NMR models. The active sites of enzymes are found to be highly constrained in these PCA modes. Furthermore, the residues predicted to be highly immobile are shown to be evolutionarily conserved, lending support to a PCA-based identification of potential functional sites. An online tool, PCA_NEST, is designed to derive the principal modes of conformational changes from structural ensembles resolved by experiments or generated by computations.</jats:p> <jats:p>Availability: http://ignm.ccbb.pitt.edu/oPCA_Online.htm</jats:p> <jats:p>Contact: lwy1@iam.u-tokyo.ac.jp</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics Bioinformatics |
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10.1093/bioinformatics/btp023 |
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| title |
Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_unstemmed |
Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_full |
Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_fullStr |
Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_full_unstemmed |
Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_short |
Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_sort |
principal component analysis of native ensembles of biomolecular structures (pca_nest): insights into functional dynamics |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/btp023 |
| publishDate |
2009 |
| physical |
606-614 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Motivation: To efficiently analyze the ‘native ensemble of conformations’ accessible to proteins near their folded state and to extract essential information from observed distributions of conformations, reliable mathematical methods and computational tools are needed.</jats:p>
<jats:p>Result: Examination of 24 pairs of structures determined by both NMR and X-ray reveals that the differences in the dynamics of the same protein resolved by the two techniques can be tracked to the most robust low frequency modes elucidated by principal component analysis (PCA) of NMR models. The active sites of enzymes are found to be highly constrained in these PCA modes. Furthermore, the residues predicted to be highly immobile are shown to be evolutionarily conserved, lending support to a PCA-based identification of potential functional sites. An online tool, PCA_NEST, is designed to derive the principal modes of conformational changes from structural ensembles resolved by experiments or generated by computations.</jats:p>
<jats:p>Availability: http://ignm.ccbb.pitt.edu/oPCA_Online.htm</jats:p>
<jats:p>Contact: lwy1@iam.u-tokyo.ac.jp</jats:p>
<jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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| author | Yang, Lee-Wei, Eyal, Eran, Bahar, Ivet, Kitao, Akio |
| author_facet | Yang, Lee-Wei, Eyal, Eran, Bahar, Ivet, Kitao, Akio, Yang, Lee-Wei, Eyal, Eran, Bahar, Ivet, Kitao, Akio |
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| description | <jats:title>Abstract</jats:title> <jats:p>Motivation: To efficiently analyze the ‘native ensemble of conformations’ accessible to proteins near their folded state and to extract essential information from observed distributions of conformations, reliable mathematical methods and computational tools are needed.</jats:p> <jats:p>Result: Examination of 24 pairs of structures determined by both NMR and X-ray reveals that the differences in the dynamics of the same protein resolved by the two techniques can be tracked to the most robust low frequency modes elucidated by principal component analysis (PCA) of NMR models. The active sites of enzymes are found to be highly constrained in these PCA modes. Furthermore, the residues predicted to be highly immobile are shown to be evolutionarily conserved, lending support to a PCA-based identification of potential functional sites. An online tool, PCA_NEST, is designed to derive the principal modes of conformational changes from structural ensembles resolved by experiments or generated by computations.</jats:p> <jats:p>Availability: http://ignm.ccbb.pitt.edu/oPCA_Online.htm</jats:p> <jats:p>Contact: lwy1@iam.u-tokyo.ac.jp</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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| imprint | Oxford University Press (OUP), 2009 |
| imprint_str_mv | Oxford University Press (OUP), 2009 |
| institution | DE-15, DE-Pl11, DE-Rs1, DE-14, DE-105, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Zi4, DE-Gla1 |
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| spelling | Yang, Lee-Wei Eyal, Eran Bahar, Ivet Kitao, Akio 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btp023 <jats:title>Abstract</jats:title> <jats:p>Motivation: To efficiently analyze the ‘native ensemble of conformations’ accessible to proteins near their folded state and to extract essential information from observed distributions of conformations, reliable mathematical methods and computational tools are needed.</jats:p> <jats:p>Result: Examination of 24 pairs of structures determined by both NMR and X-ray reveals that the differences in the dynamics of the same protein resolved by the two techniques can be tracked to the most robust low frequency modes elucidated by principal component analysis (PCA) of NMR models. The active sites of enzymes are found to be highly constrained in these PCA modes. Furthermore, the residues predicted to be highly immobile are shown to be evolutionarily conserved, lending support to a PCA-based identification of potential functional sites. An online tool, PCA_NEST, is designed to derive the principal modes of conformational changes from structural ensembles resolved by experiments or generated by computations.</jats:p> <jats:p>Availability: http://ignm.ccbb.pitt.edu/oPCA_Online.htm</jats:p> <jats:p>Contact: lwy1@iam.u-tokyo.ac.jp</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics Bioinformatics |
| spellingShingle | Yang, Lee-Wei, Eyal, Eran, Bahar, Ivet, Kitao, Akio, Bioinformatics, Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_full | Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_fullStr | Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_full_unstemmed | Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_short | Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| title_sort | principal component analysis of native ensembles of biomolecular structures (pca_nest): insights into functional dynamics |
| title_unstemmed | Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/btp023 |