Refining Spin–Spin Distance Distributions in Complex Biological Systems Using Multi-Gaussian Monte Carlo Analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-11-10

AUTHORS

Ivan O. Timofeev, Olesya A. Krumkacheva, Matvey V. Fedin, Galina G. Karpova, Elena G. Bagryanskaya

ABSTRACT

Pulse dipolar electron paramagnetic resonance spectroscopy provides means of distance measurements in the range of ~ 1.5–10 nm between two spin labels tethered to a biological system. However, the extraction of distance distribution between spin labels is an ill-posed mathematical problem. The most common approach for obtaining distance distribution employs Tikhonov regularization method, where a regularization parameter characterizing the smoothness of distribution is introduced. However, in case of multi-modal distance distributions with peaks of different widths, the use of a single regularization parameter might lead to certain distortions of actual distribution shapes. Recently, a multi-Gaussian Monte Carlo approach was proposed for eliminating this drawback and verified for model biradicals [1]. In the present work, we for the first time test this approach on complicated biological systems exhibiting multi-modal distance distributions. We apply multi-Gaussian analysis to pulsed electron–electron double resonance data of supramolecular ribosomal complexes, where the 11-mer oligoribonucleotide (MR) bearing two nitroxide labels at its termini is used as a reporter. Calculated distance distributions reveal the same conformations of MR as those obtained by Tikhonov regularization, but feature the peaks having different widths, which leads to a better resolution in several cases. The advantages, complications, and further perspectives of application of Monte-Carlo-based multi-Gaussian approach to real biological systems are discussed. More... »

PAGES

265-276

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00723-017-0965-y

DOI

http://dx.doi.org/10.1007/s00723-017-0965-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092617963


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0299", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Other Physical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia", 
          "id": "http://www.grid.ac/institutes/grid.4605.7", 
          "name": [
            "International Tomography Center SB RAS, Institutskaya str. 3a, 630090, Novosibirsk, Russia", 
            "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Timofeev", 
        "givenName": "Ivan O.", 
        "id": "sg:person.011260367405.98", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011260367405.98"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia", 
          "id": "http://www.grid.ac/institutes/grid.4605.7", 
          "name": [
            "International Tomography Center SB RAS, Institutskaya str. 3a, 630090, Novosibirsk, Russia", 
            "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Krumkacheva", 
        "givenName": "Olesya A.", 
        "id": "sg:person.01232536605.67", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01232536605.67"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia", 
          "id": "http://www.grid.ac/institutes/grid.4605.7", 
          "name": [
            "International Tomography Center SB RAS, Institutskaya str. 3a, 630090, Novosibirsk, Russia", 
            "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fedin", 
        "givenName": "Matvey V.", 
        "id": "sg:person.01062002050.07", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01062002050.07"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Chemical Biology and Fundamental Medicine SB RAS, pr. Lavrentjeva 8, 630090, Novosibirsk, Russia", 
          "id": "http://www.grid.ac/institutes/grid.418910.5", 
          "name": [
            "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia", 
            "Institute of Chemical Biology and Fundamental Medicine SB RAS, pr. Lavrentjeva 8, 630090, Novosibirsk, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Karpova", 
        "givenName": "Galina G.", 
        "id": "sg:person.01103670644.70", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01103670644.70"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia", 
          "id": "http://www.grid.ac/institutes/grid.4605.7", 
          "name": [
            "N. N. Vorozhtsov, Novosibirsk Institute of Organic Chemistry SB RAS, pr. Lavrentjeva 9, 630090, Novosibirsk, Russia", 
            "Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bagryanskaya", 
        "givenName": "Elena G.", 
        "id": "sg:person.01257440611.27", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01257440611.27"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00723-014-0541-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043292299", 
          "https://doi.org/10.1007/s00723-014-0541-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf03166213", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024213766", 
          "https://doi.org/10.1007/bf03166213"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00723-014-0609-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017146774", 
          "https://doi.org/10.1007/s00723-014-0609-4"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-11-10", 
    "datePublishedReg": "2017-11-10", 
    "description": "Pulse dipolar electron paramagnetic resonance spectroscopy provides means of distance measurements in the range of\u00a0~\u00a01.5\u201310\u00a0nm between two spin labels tethered to a biological system. However, the extraction of distance distribution between spin labels is an ill-posed mathematical problem. The most common approach for obtaining distance distribution employs Tikhonov regularization method, where a regularization parameter characterizing the smoothness of distribution is introduced. However, in case of multi-modal distance distributions with peaks of different widths, the use of a single regularization parameter might lead to certain distortions of actual distribution shapes. Recently, a multi-Gaussian Monte Carlo approach was proposed for eliminating this drawback and verified for model biradicals [1]. In the present work, we for the first time test this approach on complicated biological systems exhibiting multi-modal distance distributions. We apply multi-Gaussian analysis to pulsed electron\u2013electron double resonance data of supramolecular ribosomal complexes, where the 11-mer oligoribonucleotide (MR) bearing two nitroxide labels at its termini is used as a reporter. Calculated distance distributions reveal the same conformations of MR as those obtained by Tikhonov regularization, but feature the peaks having different widths, which leads to a better resolution in several cases. The advantages, complications, and further perspectives of application of Monte-Carlo-based multi-Gaussian approach to real biological systems are discussed.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00723-017-0965-y", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.4896902", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1102112", 
        "issn": [
          "0937-9347", 
          "1613-7507"
        ], 
        "name": "Applied Magnetic Resonance", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "49"
      }
    ], 
    "keywords": [
      "electron\u2013electron double resonance (PELDOR) data", 
      "distance distribution", 
      "double resonance data", 
      "spin labels", 
      "different widths", 
      "regularization parameter", 
      "complicated biological systems", 
      "electron paramagnetic resonance spectroscopy", 
      "first-time tests", 
      "paramagnetic resonance spectroscopy", 
      "Monte Carlo approach", 
      "multi-Gaussian approach", 
      "distance measurements", 
      "resonance data", 
      "single regularization parameter", 
      "biological systems", 
      "nitroxide labels", 
      "Tikhonov regularization method", 
      "Carlo approach", 
      "Monte Carlo", 
      "mathematical problem", 
      "Monte Carlo analysis", 
      "real biological systems", 
      "Tikhonov regularization", 
      "regularization method", 
      "better resolution", 
      "width", 
      "resonance spectroscopy", 
      "Carlo analysis", 
      "pulses", 
      "peak", 
      "distribution shape", 
      "complex biological systems", 
      "spectroscopy", 
      "distribution", 
      "certain distortions", 
      "present work", 
      "further perspectives", 
      "biradicals", 
      "resolution", 
      "measurements", 
      "common approach", 
      "distortion", 
      "regularization", 
      "parameters", 
      "smoothness", 
      "approach", 
      "system", 
      "range", 
      "shape", 
      "problem", 
      "same conformation", 
      "applications", 
      "cases", 
      "conformation", 
      "means", 
      "work", 
      "drawbacks", 
      "method", 
      "advantages", 
      "analysis", 
      "complexes", 
      "data", 
      "labels", 
      "MR", 
      "use", 
      "ribosomal complexes", 
      "extraction", 
      "perspective", 
      "test", 
      "time test", 
      "reporter", 
      "terminus", 
      "oligoribonucleotides", 
      "complications"
    ], 
    "name": "Refining Spin\u2013Spin Distance Distributions in Complex Biological Systems Using Multi-Gaussian Monte Carlo Analysis", 
    "pagination": "265-276", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1092617963"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00723-017-0965-y"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00723-017-0965-y", 
      "https://app.dimensions.ai/details/publication/pub.1092617963"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-20T07:33", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_741.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00723-017-0965-y"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00723-017-0965-y'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00723-017-0965-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00723-017-0965-y'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00723-017-0965-y'


 

This table displays all metadata directly associated to this object as RDF triples.

181 TRIPLES      22 PREDICATES      103 URIs      92 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00723-017-0965-y schema:about anzsrc-for:02
2 anzsrc-for:0299
3 schema:author N4b235eab2a15415f9051827055999822
4 schema:citation sg:pub.10.1007/bf03166213
5 sg:pub.10.1007/s00723-014-0541-7
6 sg:pub.10.1007/s00723-014-0609-4
7 schema:datePublished 2017-11-10
8 schema:datePublishedReg 2017-11-10
9 schema:description Pulse dipolar electron paramagnetic resonance spectroscopy provides means of distance measurements in the range of ~ 1.5–10 nm between two spin labels tethered to a biological system. However, the extraction of distance distribution between spin labels is an ill-posed mathematical problem. The most common approach for obtaining distance distribution employs Tikhonov regularization method, where a regularization parameter characterizing the smoothness of distribution is introduced. However, in case of multi-modal distance distributions with peaks of different widths, the use of a single regularization parameter might lead to certain distortions of actual distribution shapes. Recently, a multi-Gaussian Monte Carlo approach was proposed for eliminating this drawback and verified for model biradicals [1]. In the present work, we for the first time test this approach on complicated biological systems exhibiting multi-modal distance distributions. We apply multi-Gaussian analysis to pulsed electron–electron double resonance data of supramolecular ribosomal complexes, where the 11-mer oligoribonucleotide (MR) bearing two nitroxide labels at its termini is used as a reporter. Calculated distance distributions reveal the same conformations of MR as those obtained by Tikhonov regularization, but feature the peaks having different widths, which leads to a better resolution in several cases. The advantages, complications, and further perspectives of application of Monte-Carlo-based multi-Gaussian approach to real biological systems are discussed.
10 schema:genre article
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf Ne1b7f1539e134737a6115bbb75afd514
14 Neb5ee0f05f56454f82fa74bb28b8f062
15 sg:journal.1102112
16 schema:keywords Carlo analysis
17 Carlo approach
18 MR
19 Monte Carlo
20 Monte Carlo analysis
21 Monte Carlo approach
22 Tikhonov regularization
23 Tikhonov regularization method
24 advantages
25 analysis
26 applications
27 approach
28 better resolution
29 biological systems
30 biradicals
31 cases
32 certain distortions
33 common approach
34 complex biological systems
35 complexes
36 complicated biological systems
37 complications
38 conformation
39 data
40 different widths
41 distance distribution
42 distance measurements
43 distortion
44 distribution
45 distribution shape
46 double resonance data
47 drawbacks
48 electron paramagnetic resonance spectroscopy
49 electron–electron double resonance (PELDOR) data
50 extraction
51 first-time tests
52 further perspectives
53 labels
54 mathematical problem
55 means
56 measurements
57 method
58 multi-Gaussian approach
59 nitroxide labels
60 oligoribonucleotides
61 paramagnetic resonance spectroscopy
62 parameters
63 peak
64 perspective
65 present work
66 problem
67 pulses
68 range
69 real biological systems
70 regularization
71 regularization method
72 regularization parameter
73 reporter
74 resolution
75 resonance data
76 resonance spectroscopy
77 ribosomal complexes
78 same conformation
79 shape
80 single regularization parameter
81 smoothness
82 spectroscopy
83 spin labels
84 system
85 terminus
86 test
87 time test
88 use
89 width
90 work
91 schema:name Refining Spin–Spin Distance Distributions in Complex Biological Systems Using Multi-Gaussian Monte Carlo Analysis
92 schema:pagination 265-276
93 schema:productId N1b1787840923407891227038001d4f51
94 Nc087ea125c254f03b58537acffd7039f
95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092617963
96 https://doi.org/10.1007/s00723-017-0965-y
97 schema:sdDatePublished 2022-05-20T07:33
98 schema:sdLicense https://scigraph.springernature.com/explorer/license/
99 schema:sdPublisher N5d4205931da64ff28bc88422e8ff6a32
100 schema:url https://doi.org/10.1007/s00723-017-0965-y
101 sgo:license sg:explorer/license/
102 sgo:sdDataset articles
103 rdf:type schema:ScholarlyArticle
104 N08ae65f4de55464d8e1d36d7ea2e4c6d rdf:first sg:person.01103670644.70
105 rdf:rest N33e3ef743ad04e2daaccac20f211b115
106 N1b1787840923407891227038001d4f51 schema:name dimensions_id
107 schema:value pub.1092617963
108 rdf:type schema:PropertyValue
109 N33e3ef743ad04e2daaccac20f211b115 rdf:first sg:person.01257440611.27
110 rdf:rest rdf:nil
111 N3ee1a55709f949e9b65c451965b5d069 rdf:first sg:person.01232536605.67
112 rdf:rest Nb7388b3bad764a3c93baa295f4202246
113 N4b235eab2a15415f9051827055999822 rdf:first sg:person.011260367405.98
114 rdf:rest N3ee1a55709f949e9b65c451965b5d069
115 N5d4205931da64ff28bc88422e8ff6a32 schema:name Springer Nature - SN SciGraph project
116 rdf:type schema:Organization
117 Nb7388b3bad764a3c93baa295f4202246 rdf:first sg:person.01062002050.07
118 rdf:rest N08ae65f4de55464d8e1d36d7ea2e4c6d
119 Nc087ea125c254f03b58537acffd7039f schema:name doi
120 schema:value 10.1007/s00723-017-0965-y
121 rdf:type schema:PropertyValue
122 Ne1b7f1539e134737a6115bbb75afd514 schema:volumeNumber 49
123 rdf:type schema:PublicationVolume
124 Neb5ee0f05f56454f82fa74bb28b8f062 schema:issueNumber 3
125 rdf:type schema:PublicationIssue
126 anzsrc-for:02 schema:inDefinedTermSet anzsrc-for:
127 schema:name Physical Sciences
128 rdf:type schema:DefinedTerm
129 anzsrc-for:0299 schema:inDefinedTermSet anzsrc-for:
130 schema:name Other Physical Sciences
131 rdf:type schema:DefinedTerm
132 sg:grant.4896902 http://pending.schema.org/fundedItem sg:pub.10.1007/s00723-017-0965-y
133 rdf:type schema:MonetaryGrant
134 sg:journal.1102112 schema:issn 0937-9347
135 1613-7507
136 schema:name Applied Magnetic Resonance
137 schema:publisher Springer Nature
138 rdf:type schema:Periodical
139 sg:person.01062002050.07 schema:affiliation grid-institutes:grid.4605.7
140 schema:familyName Fedin
141 schema:givenName Matvey V.
142 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01062002050.07
143 rdf:type schema:Person
144 sg:person.01103670644.70 schema:affiliation grid-institutes:grid.418910.5
145 schema:familyName Karpova
146 schema:givenName Galina G.
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01103670644.70
148 rdf:type schema:Person
149 sg:person.011260367405.98 schema:affiliation grid-institutes:grid.4605.7
150 schema:familyName Timofeev
151 schema:givenName Ivan O.
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011260367405.98
153 rdf:type schema:Person
154 sg:person.01232536605.67 schema:affiliation grid-institutes:grid.4605.7
155 schema:familyName Krumkacheva
156 schema:givenName Olesya A.
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01232536605.67
158 rdf:type schema:Person
159 sg:person.01257440611.27 schema:affiliation grid-institutes:grid.4605.7
160 schema:familyName Bagryanskaya
161 schema:givenName Elena G.
162 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01257440611.27
163 rdf:type schema:Person
164 sg:pub.10.1007/bf03166213 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024213766
165 https://doi.org/10.1007/bf03166213
166 rdf:type schema:CreativeWork
167 sg:pub.10.1007/s00723-014-0541-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043292299
168 https://doi.org/10.1007/s00723-014-0541-7
169 rdf:type schema:CreativeWork
170 sg:pub.10.1007/s00723-014-0609-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017146774
171 https://doi.org/10.1007/s00723-014-0609-4
172 rdf:type schema:CreativeWork
173 grid-institutes:grid.418910.5 schema:alternateName Institute of Chemical Biology and Fundamental Medicine SB RAS, pr. Lavrentjeva 8, 630090, Novosibirsk, Russia
174 schema:name Institute of Chemical Biology and Fundamental Medicine SB RAS, pr. Lavrentjeva 8, 630090, Novosibirsk, Russia
175 Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia
176 rdf:type schema:Organization
177 grid-institutes:grid.4605.7 schema:alternateName Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia
178 schema:name International Tomography Center SB RAS, Institutskaya str. 3a, 630090, Novosibirsk, Russia
179 N. N. Vorozhtsov, Novosibirsk Institute of Organic Chemistry SB RAS, pr. Lavrentjeva 9, 630090, Novosibirsk, Russia
180 Novosibirsk State University, Pirogova Str. 2, 630090, Novosibirsk, Russia
181 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...