Fault Tolerance for Large Scale Protein 3D Reconstruction from Contact Maps View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007

AUTHORS

Marco Vassura , Luciano Margara , Pietro Di Lena , Filippo Medri , Piero Fariselli , Rita Casadio

ABSTRACT

In this paper we describe FT-COMAR an algorithm that improves fault tolerance of our heuristic algorithm (COMAR) previously described for protein reconstruction [10]. The algorithm [COMAR-Contact Map Reconstruction] can reconstruct the three-dimensional (3D) structure of the real protein from its contact map with 100% efficiency when tested on 1760 proteins from different structural classes. Here we test the performances of COMAR on native contact maps when a perturbation with random errors is introduced. This is done in order to simulate possible scenarios of reconstruction from predicted (and therefore highly noised) contact maps. From our analysis we obtain that our algorithm performs better reconstructions on blurred contact maps when contacts are under predicted than over predicted. Moreover we modify the algorithm into FT-COMAR [Fault Tolerant-COMAR] in order to use it with incomplete contact maps. FT-COMAR can ignore up to 75% of the contact map and still recover from the remaining 25% entries a 3D structure whose root mean square deviation (RMSD) from the native one is less then 4 Å. Our results indicate that the quality more than the quantity of predicted contacts is relevant to the protein 3D reconstruction and that some hints about “unsafe” areas in the predicted contact maps can be useful to improve reconstruction quality. For this, we implement a very simple filtering procedure to detect unsafe areas in contact maps and we show that by this and in the presences of errors the performance of the algorithm can be significantly improved. Furthermore, we show that both COMAR and FT-COMAR overcome a previous state-of-the-art algorithm for the same task [13]. More... »

PAGES

25-37

References to SciGraph publications

Book

TITLE

Algorithms in Bioinformatics

ISBN

978-3-540-74125-1
978-3-540-74126-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-74126-8_4

DOI

http://dx.doi.org/10.1007/978-3-540-74126-8_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023694245


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/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Bologna", 
          "id": "https://www.grid.ac/institutes/grid.6292.f", 
          "name": [
            "Computer Science Department, University of Bologna, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Vassura", 
        "givenName": "Marco", 
        "id": "sg:person.01130664753.59", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01130664753.59"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Bologna", 
          "id": "https://www.grid.ac/institutes/grid.6292.f", 
          "name": [
            "Computer Science Department, University of Bologna, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Margara", 
        "givenName": "Luciano", 
        "id": "sg:person.013142050277.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013142050277.95"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Bologna", 
          "id": "https://www.grid.ac/institutes/grid.6292.f", 
          "name": [
            "Computer Science Department, University of Bologna, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Di Lena", 
        "givenName": "Pietro", 
        "id": "sg:person.01301214601.87", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01301214601.87"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Bologna", 
          "id": "https://www.grid.ac/institutes/grid.6292.f", 
          "name": [
            "Computer Science Department, University of Bologna, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Medri", 
        "givenName": "Filippo", 
        "id": "sg:person.01313226553.96", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01313226553.96"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Bologna", 
          "id": "https://www.grid.ac/institutes/grid.6292.f", 
          "name": [
            "Biocomputing Group, Department of Biology, University of Bologna, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fariselli", 
        "givenName": "Piero", 
        "id": "sg:person.01347332413.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01347332413.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Bologna", 
          "id": "https://www.grid.ac/institutes/grid.6292.f", 
          "name": [
            "Biocomputing Group, Department of Biology, University of Bologna, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Casadio", 
        "givenName": "Rita", 
        "id": "sg:person.0675702613.15", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0675702613.15"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/s1359-0278(97)00041-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001124166"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0925-7721(97)00014-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019481973"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1006/jmbi.1993.1332", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021358555"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/prot.1173", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025461989"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-59745-574-9_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032370664", 
          "https://doi.org/10.1007/978-1-59745-574-9_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-59745-574-9_8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032370664", 
          "https://doi.org/10.1007/978-1-59745-574-9_8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkh039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033933776"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/gkh039", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033933776"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0083-6729(00)58025-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046010317"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/nar/25.17.3389", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047265454"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1089/cmb.2006.13.631", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059245496"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007", 
    "datePublishedReg": "2007-01-01", 
    "description": "In this paper we describe FT-COMAR an algorithm that improves fault tolerance of our heuristic algorithm (COMAR) previously described for protein reconstruction [10]. The algorithm [COMAR-Contact Map Reconstruction] can reconstruct the three-dimensional (3D) structure of the real protein from its contact map with 100% efficiency when tested on 1760 proteins from different structural classes. Here we test the performances of COMAR on native contact maps when a perturbation with random errors is introduced. This is done in order to simulate possible scenarios of reconstruction from predicted (and therefore highly noised) contact maps. From our analysis we obtain that our algorithm performs better reconstructions on blurred contact maps when contacts are under predicted than over predicted. Moreover we modify the algorithm into FT-COMAR [Fault Tolerant-COMAR] in order to use it with incomplete contact maps. FT-COMAR can ignore up to 75% of the contact map and still recover from the remaining 25% entries a 3D structure whose root mean square deviation (RMSD) from the native one is less then 4 \u00c5. Our results indicate that the quality more than the quantity of predicted contacts is relevant to the protein 3D reconstruction and that some hints about \u201cunsafe\u201d areas in the predicted contact maps can be useful to improve reconstruction quality. For this, we implement a very simple filtering procedure to detect unsafe areas in contact maps and we show that by this and in the presences of errors the performance of the algorithm can be significantly improved. Furthermore, we show that both COMAR and FT-COMAR overcome a previous state-of-the-art algorithm for the same task [13].", 
    "editor": [
      {
        "familyName": "Giancarlo", 
        "givenName": "Raffaele", 
        "type": "Person"
      }, 
      {
        "familyName": "Hannenhalli", 
        "givenName": "Sridhar", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-74126-8_4", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-74125-1", 
        "978-3-540-74126-8"
      ], 
      "name": "Algorithms in Bioinformatics", 
      "type": "Book"
    }, 
    "name": "Fault Tolerance for Large Scale Protein 3D Reconstruction from Contact Maps", 
    "pagination": "25-37", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-74126-8_4"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "70e2a3c99a708efc2f8cb1a74e0a749ccd7c579205710445bea7833e1de5c5d8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1023694245"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-74126-8_4", 
      "https://app.dimensions.ai/details/publication/pub.1023694245"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-16T05:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000345_0000000345/records_64100_00000000.jsonl", 
    "type": "Chapter", 
    "url": "https://link.springer.com/10.1007%2F978-3-540-74126-8_4"
  }
]
 

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.10.1007/978-3-540-74126-8_4'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-74126-8_4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-74126-8_4'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-74126-8_4'


 

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

134 TRIPLES      23 PREDICATES      36 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-74126-8_4 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N7a0d8b9b85164f03a32015819680adca
4 schema:citation sg:pub.10.1007/978-1-59745-574-9_8
5 https://doi.org/10.1002/prot.1173
6 https://doi.org/10.1006/jmbi.1993.1332
7 https://doi.org/10.1016/s0083-6729(00)58025-x
8 https://doi.org/10.1016/s0925-7721(97)00014-x
9 https://doi.org/10.1016/s1359-0278(97)00041-2
10 https://doi.org/10.1089/cmb.2006.13.631
11 https://doi.org/10.1093/nar/25.17.3389
12 https://doi.org/10.1093/nar/gkh039
13 schema:datePublished 2007
14 schema:datePublishedReg 2007-01-01
15 schema:description In this paper we describe FT-COMAR an algorithm that improves fault tolerance of our heuristic algorithm (COMAR) previously described for protein reconstruction [10]. The algorithm [COMAR-Contact Map Reconstruction] can reconstruct the three-dimensional (3D) structure of the real protein from its contact map with 100% efficiency when tested on 1760 proteins from different structural classes. Here we test the performances of COMAR on native contact maps when a perturbation with random errors is introduced. This is done in order to simulate possible scenarios of reconstruction from predicted (and therefore highly noised) contact maps. From our analysis we obtain that our algorithm performs better reconstructions on blurred contact maps when contacts are under predicted than over predicted. Moreover we modify the algorithm into FT-COMAR [Fault Tolerant-COMAR] in order to use it with incomplete contact maps. FT-COMAR can ignore up to 75% of the contact map and still recover from the remaining 25% entries a 3D structure whose root mean square deviation (RMSD) from the native one is less then 4 Å. Our results indicate that the quality more than the quantity of predicted contacts is relevant to the protein 3D reconstruction and that some hints about “unsafe” areas in the predicted contact maps can be useful to improve reconstruction quality. For this, we implement a very simple filtering procedure to detect unsafe areas in contact maps and we show that by this and in the presences of errors the performance of the algorithm can be significantly improved. Furthermore, we show that both COMAR and FT-COMAR overcome a previous state-of-the-art algorithm for the same task [13].
16 schema:editor N899a096881b54454bad5815d29f7f282
17 schema:genre chapter
18 schema:inLanguage en
19 schema:isAccessibleForFree false
20 schema:isPartOf Na129faff13ea47fbb336268acdce4728
21 schema:name Fault Tolerance for Large Scale Protein 3D Reconstruction from Contact Maps
22 schema:pagination 25-37
23 schema:productId N8db587d22123492787fe3d798e0ba228
24 Nc44eb38068614634972b972cf62db3ac
25 Nf0a18b12f3f34c0fbf94d58fb38c0e69
26 schema:publisher Necdb7dfe528b4d979657507bdc748a0f
27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023694245
28 https://doi.org/10.1007/978-3-540-74126-8_4
29 schema:sdDatePublished 2019-04-16T05:25
30 schema:sdLicense https://scigraph.springernature.com/explorer/license/
31 schema:sdPublisher Nc75d24bba989408ba07d228ac8c5a40a
32 schema:url https://link.springer.com/10.1007%2F978-3-540-74126-8_4
33 sgo:license sg:explorer/license/
34 sgo:sdDataset chapters
35 rdf:type schema:Chapter
36 N07cd2e7eaeaa4368911ff50ba3856b9e schema:familyName Hannenhalli
37 schema:givenName Sridhar
38 rdf:type schema:Person
39 N2676afad4e444b9ab2c5ef99c621df27 rdf:first sg:person.0675702613.15
40 rdf:rest rdf:nil
41 N29a1d7804f3545cfafaf948d4e50eebb rdf:first N07cd2e7eaeaa4368911ff50ba3856b9e
42 rdf:rest rdf:nil
43 N2fec27c6a44c4cd19cb7bfbd153f066b schema:familyName Giancarlo
44 schema:givenName Raffaele
45 rdf:type schema:Person
46 N40071b4d1351411a895b66269abbe7df rdf:first sg:person.01313226553.96
47 rdf:rest N95a97e2e855642bea9253e101db1c1a9
48 N439d585c94384478a82dfac78e2716f1 rdf:first sg:person.01301214601.87
49 rdf:rest N40071b4d1351411a895b66269abbe7df
50 N7a0d8b9b85164f03a32015819680adca rdf:first sg:person.01130664753.59
51 rdf:rest N8c7421601a8640e6969df2cc0cdba244
52 N899a096881b54454bad5815d29f7f282 rdf:first N2fec27c6a44c4cd19cb7bfbd153f066b
53 rdf:rest N29a1d7804f3545cfafaf948d4e50eebb
54 N8c7421601a8640e6969df2cc0cdba244 rdf:first sg:person.013142050277.95
55 rdf:rest N439d585c94384478a82dfac78e2716f1
56 N8db587d22123492787fe3d798e0ba228 schema:name readcube_id
57 schema:value 70e2a3c99a708efc2f8cb1a74e0a749ccd7c579205710445bea7833e1de5c5d8
58 rdf:type schema:PropertyValue
59 N95a97e2e855642bea9253e101db1c1a9 rdf:first sg:person.01347332413.06
60 rdf:rest N2676afad4e444b9ab2c5ef99c621df27
61 Na129faff13ea47fbb336268acdce4728 schema:isbn 978-3-540-74125-1
62 978-3-540-74126-8
63 schema:name Algorithms in Bioinformatics
64 rdf:type schema:Book
65 Nc44eb38068614634972b972cf62db3ac schema:name doi
66 schema:value 10.1007/978-3-540-74126-8_4
67 rdf:type schema:PropertyValue
68 Nc75d24bba989408ba07d228ac8c5a40a schema:name Springer Nature - SN SciGraph project
69 rdf:type schema:Organization
70 Necdb7dfe528b4d979657507bdc748a0f schema:location Berlin, Heidelberg
71 schema:name Springer Berlin Heidelberg
72 rdf:type schema:Organisation
73 Nf0a18b12f3f34c0fbf94d58fb38c0e69 schema:name dimensions_id
74 schema:value pub.1023694245
75 rdf:type schema:PropertyValue
76 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
77 schema:name Information and Computing Sciences
78 rdf:type schema:DefinedTerm
79 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
80 schema:name Artificial Intelligence and Image Processing
81 rdf:type schema:DefinedTerm
82 sg:person.01130664753.59 schema:affiliation https://www.grid.ac/institutes/grid.6292.f
83 schema:familyName Vassura
84 schema:givenName Marco
85 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01130664753.59
86 rdf:type schema:Person
87 sg:person.01301214601.87 schema:affiliation https://www.grid.ac/institutes/grid.6292.f
88 schema:familyName Di Lena
89 schema:givenName Pietro
90 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01301214601.87
91 rdf:type schema:Person
92 sg:person.01313226553.96 schema:affiliation https://www.grid.ac/institutes/grid.6292.f
93 schema:familyName Medri
94 schema:givenName Filippo
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01313226553.96
96 rdf:type schema:Person
97 sg:person.013142050277.95 schema:affiliation https://www.grid.ac/institutes/grid.6292.f
98 schema:familyName Margara
99 schema:givenName Luciano
100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013142050277.95
101 rdf:type schema:Person
102 sg:person.01347332413.06 schema:affiliation https://www.grid.ac/institutes/grid.6292.f
103 schema:familyName Fariselli
104 schema:givenName Piero
105 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01347332413.06
106 rdf:type schema:Person
107 sg:person.0675702613.15 schema:affiliation https://www.grid.ac/institutes/grid.6292.f
108 schema:familyName Casadio
109 schema:givenName Rita
110 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0675702613.15
111 rdf:type schema:Person
112 sg:pub.10.1007/978-1-59745-574-9_8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032370664
113 https://doi.org/10.1007/978-1-59745-574-9_8
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1002/prot.1173 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025461989
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1006/jmbi.1993.1332 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021358555
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1016/s0083-6729(00)58025-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1046010317
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1016/s0925-7721(97)00014-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1019481973
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1016/s1359-0278(97)00041-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001124166
124 rdf:type schema:CreativeWork
125 https://doi.org/10.1089/cmb.2006.13.631 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059245496
126 rdf:type schema:CreativeWork
127 https://doi.org/10.1093/nar/25.17.3389 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047265454
128 rdf:type schema:CreativeWork
129 https://doi.org/10.1093/nar/gkh039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033933776
130 rdf:type schema:CreativeWork
131 https://www.grid.ac/institutes/grid.6292.f schema:alternateName University of Bologna
132 schema:name Biocomputing Group, Department of Biology, University of Bologna, Italy
133 Computer Science Department, University of Bologna, Italy
134 rdf:type schema:Organization
 




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


...