Role of long- and short-range hydrophobic, hydrophilic and charged residues contact network in protein’s structural organization View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-12

AUTHORS

Dhriti Sengupta, Sudip Kundu

ABSTRACT

BACKGROUND: The three-dimensional structure of a protein can be described as a graph where nodes represent residues and the strength of non-covalent interactions between them are edges. These protein contact networks can be separated into long and short-range interactions networks depending on the positions of amino acids in primary structure. Long-range interactions play a distinct role in determining the tertiary structure of a protein while short-range interactions could largely contribute to the secondary structure formations. In addition, physico chemical properties and the linear arrangement of amino acids of the primary structure of a protein determines its three dimensional structure. Here, we present an extensive analysis of protein contact subnetworks based on the London van der Waals interactions of amino acids at different length scales. We further subdivided those networks in hydrophobic, hydrophilic and charged residues networks and have tried to correlate their influence in the overall topology and organization of a protein. RESULTS: The largest connected component (LCC) of long (LRN)-, short (SRN)- and all-range (ARN) networks within proteins exhibit a transition behaviour when plotted against different interaction strengths of edges among amino acid nodes. While short-range networks having chain like structures exhibit highly cooperative transition; long- and all-range networks, which are more similar to each other, have non-chain like structures and show less cooperativity. Further, the hydrophobic residues subnetworks in long- and all-range networks have similar transition behaviours with all residues all-range networks, but the hydrophilic and charged residues networks don't. While the nature of transitions of LCC's sizes is same in SRNs for thermophiles and mesophiles, there exists a clear difference in LRNs. The presence of larger size of interconnected long-range interactions in thermophiles than mesophiles, even at higher interaction strength between amino acids, give extra stability to the tertiary structure of the thermophiles. All the subnetworks at different length scales (ARNs, LRNs and SRNs) show assortativity mixing property of their participating amino acids. While there exists a significant higher percentage of hydrophobic subclusters over others in ARNs and LRNs; we do not find the assortative mixing behaviour of any the subclusters in SRNs. The clustering coefficient of hydrophobic subclusters in long-range network is the highest among types of subnetworks. There exist highly cliquish hydrophobic nodes followed by charged nodes in LRNs and ARNs; on the other hand, we observe the highest dominance of charged residues cliques in short-range networks. Studies on the perimeter of the cliques also show higher occurrences of hydrophobic and charged residues' cliques. CONCLUSIONS: The simple framework of protein contact networks and their subnetworks based on London van der Waals force is able to capture several known properties of protein structure as well as can unravel several new features. The thermophiles do not only have the higher number of long-range interactions; they also have larger cluster of connected residues at higher interaction strengths among amino acids, than their mesophilic counterparts. It can reestablish the significant role of long-range hydrophobic clusters in protein folding and stabilization; at the same time, it shed light on the higher communication ability of hydrophobic subnetworks over the others. The results give an indication of the controlling role of hydrophobic subclusters in determining protein's folding rate. The occurrences of higher perimeters of hydrophobic and charged cliques imply the role of charged residues as well as hydrophobic residues in stabilizing the distant part of primary structure of a protein through London van der Waals interaction. More... »

PAGES

142

References to SciGraph publications

  • 2003-01. Evolutionarily conserved networks of residues mediate allosteric communication in proteins in NATURE STRUCTURAL & MOLECULAR BIOLOGY
  • 2008. PDB (Protein Data Bank) in ENCYCLOPEDIA OF GENETICS, GENOMICS, PROTEOMICS AND INFORMATICS
  • 1982-11. Effect of short- and long-range interactions on protein folding in THE PROTEIN JOURNAL
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/1471-2105-13-142

    DOI

    http://dx.doi.org/10.1186/1471-2105-13-142

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1011542450

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/22720789


    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/2008/0306", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Physical Chemistry (incl. Structural)", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Chemical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Amino Acids", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Cluster Analysis", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Hydrophobic and Hydrophilic Interactions", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Protein Conformation", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Protein Folding", 
            "type": "DefinedTerm"
          }, 
          {
            "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
            "name": "Proteins", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Calcutta", 
              "id": "https://www.grid.ac/institutes/grid.59056.3f", 
              "name": [
                "Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta, 92 APC Road, 700009, Kolkata, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sengupta", 
            "givenName": "Dhriti", 
            "id": "sg:person.0730313605.11", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0730313605.11"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Calcutta", 
              "id": "https://www.grid.ac/institutes/grid.59056.3f", 
              "name": [
                "Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta, 92 APC Road, 700009, Kolkata, India"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kundu", 
            "givenName": "Sudip", 
            "id": "sg:person.0667720624.89", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0667720624.89"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.pbiomolbio.2003.09.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000407665"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.pbiomolbio.2003.09.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000407665"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0501043102", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002183245"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/jp015514e", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004528958"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/jp015514e", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004528958"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0301-4622(01)00154-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004639412"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/ja809947w", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006705100"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1021/ja809947w", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006705100"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.0605504103", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008266007"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.89.208701", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012572068"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physrevlett.89.208701", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012572068"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0014-5793(99)00622-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015755203"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.70.3.830", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016169925"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1039/b903019k", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018368668"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmb.2004.10.055", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019364803"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.75.2.559", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019519181"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.physa.2006.03.056", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020346950"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/10826069908544933", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021508317"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.122076099", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021741329"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/prot.20109", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024856257"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/prot.10419", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025077163"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1107/s002188981000110x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025349594"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1107/s002188981000110x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025349594"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.febslet.2007.05.021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025515124"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nsb881", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025773167", 
              "https://doi.org/10.1038/nsb881"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.physa.2012.03.034", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027786819"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jmbi.2001.4775", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028172780"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1093/bioinformatics/btm257", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030189369"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.jmb.2003.08.061", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036026326"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01039553", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037032124", 
              "https://doi.org/10.1007/bf01039553"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1038/msb4100063", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040362452"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1038/msb4100063", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040362452"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0006-3495(03)75000-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040755744"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.bpj.2009.07.016", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043603447"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1529/biophysj.106.098004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044107791"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1529/biophysj.106.099903", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045616207"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1081/pb-100104905", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045682555"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.physa.2004.08.055", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048435270"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4020-6754-9_12455", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049819550", 
              "https://doi.org/10.1007/978-1-4020-6754-9_12455"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1529/biophysj.105.064485", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050658725"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1006/jmbi.1999.3058", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1054489997"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreve.65.061910", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060728621"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1103/physreve.65.061910", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1060728621"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2012-12", 
        "datePublishedReg": "2012-12-01", 
        "description": "BACKGROUND: The three-dimensional structure of a protein can be described as a graph where nodes represent residues and the strength of non-covalent interactions between them are edges. These protein contact networks can be separated into long and short-range interactions networks depending on the positions of amino acids in primary structure. Long-range interactions play a distinct role in determining the tertiary structure of a protein while short-range interactions could largely contribute to the secondary structure formations. In addition, physico chemical properties and the linear arrangement of amino acids of the primary structure of a protein determines its three dimensional structure. Here, we present an extensive analysis of protein contact subnetworks based on the London van der Waals interactions of amino acids at different length scales. We further subdivided those networks in hydrophobic, hydrophilic and charged residues networks and have tried to correlate their influence in the overall topology and organization of a protein.\nRESULTS: The largest connected component (LCC) of long (LRN)-, short (SRN)- and all-range (ARN) networks within proteins exhibit a transition behaviour when plotted against different interaction strengths of edges among amino acid nodes. While short-range networks having chain like structures exhibit highly cooperative transition; long- and all-range networks, which are more similar to each other, have non-chain like structures and show less cooperativity. Further, the hydrophobic residues subnetworks in long- and all-range networks have similar transition behaviours with all residues all-range networks, but the hydrophilic and charged residues networks don't. While the nature of transitions of LCC's sizes is same in SRNs for thermophiles and mesophiles, there exists a clear difference in LRNs. The presence of larger size of interconnected long-range interactions in thermophiles than mesophiles, even at higher interaction strength between amino acids, give extra stability to the tertiary structure of the thermophiles. All the subnetworks at different length scales (ARNs, LRNs and SRNs) show assortativity mixing property of their participating amino acids. While there exists a significant higher percentage of hydrophobic subclusters over others in ARNs and LRNs; we do not find the assortative mixing behaviour of any the subclusters in SRNs. The clustering coefficient of hydrophobic subclusters in long-range network is the highest among types of subnetworks. There exist highly cliquish hydrophobic nodes followed by charged nodes in LRNs and ARNs; on the other hand, we observe the highest dominance of charged residues cliques in short-range networks. Studies on the perimeter of the cliques also show higher occurrences of hydrophobic and charged residues' cliques.\nCONCLUSIONS: The simple framework of protein contact networks and their subnetworks based on London van der Waals force is able to capture several known properties of protein structure as well as can unravel several new features. The thermophiles do not only have the higher number of long-range interactions; they also have larger cluster of connected residues at higher interaction strengths among amino acids, than their mesophilic counterparts. It can reestablish the significant role of long-range hydrophobic clusters in protein folding and stabilization; at the same time, it shed light on the higher communication ability of hydrophobic subnetworks over the others. The results give an indication of the controlling role of hydrophobic subclusters in determining protein's folding rate. The occurrences of higher perimeters of hydrophobic and charged cliques imply the role of charged residues as well as hydrophobic residues in stabilizing the distant part of primary structure of a protein through London van der Waals interaction.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1186/1471-2105-13-142", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1023786", 
            "issn": [
              "1471-2105"
            ], 
            "name": "BMC Bioinformatics", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "13"
          }
        ], 
        "name": "Role of long- and short-range hydrophobic, hydrophilic and charged residues contact network in protein\u2019s structural organization", 
        "pagination": "142", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "36fe4c5618ee68cdfeb9461949142da5a714eac4732d3b24d20eed368c175c4c"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "22720789"
            ]
          }, 
          {
            "name": "nlm_unique_id", 
            "type": "PropertyValue", 
            "value": [
              "100965194"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1186/1471-2105-13-142"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1011542450"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1186/1471-2105-13-142", 
          "https://app.dimensions.ai/details/publication/pub.1011542450"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T09:58", 
        "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/v90/0000000347_0000000347/records_89812_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1186%2F1471-2105-13-142"
      }
    ]
     

    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.10.1186/1471-2105-13-142'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-142'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-142'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/1471-2105-13-142'


     

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

    210 TRIPLES      21 PREDICATES      71 URIs      27 LITERALS      15 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1186/1471-2105-13-142 schema:about N4d222b9779564846bbf4c145780fcf34
    2 N51eca48942174756aca06d8eb731064d
    3 N6630016ebd514fdfaaaf22b0f67bb6b3
    4 N7d7607c498e64e51bcc573ba5cb6cbbc
    5 Na4a8d3718daf4e7b9d4b2e26e8f63b04
    6 Neabfb111289d4193a788a2b33ad2eea8
    7 anzsrc-for:03
    8 anzsrc-for:0306
    9 schema:author Nef64e74b93d44a538e30806d8a0a0ed4
    10 schema:citation sg:pub.10.1007/978-1-4020-6754-9_12455
    11 sg:pub.10.1007/bf01039553
    12 sg:pub.10.1038/nsb881
    13 https://doi.org/10.1002/prot.10419
    14 https://doi.org/10.1002/prot.20109
    15 https://doi.org/10.1006/jmbi.1999.3058
    16 https://doi.org/10.1006/jmbi.2001.4775
    17 https://doi.org/10.1016/j.bpj.2009.07.016
    18 https://doi.org/10.1016/j.febslet.2007.05.021
    19 https://doi.org/10.1016/j.jmb.2003.08.061
    20 https://doi.org/10.1016/j.jmb.2004.10.055
    21 https://doi.org/10.1016/j.pbiomolbio.2003.09.003
    22 https://doi.org/10.1016/j.physa.2004.08.055
    23 https://doi.org/10.1016/j.physa.2006.03.056
    24 https://doi.org/10.1016/j.physa.2012.03.034
    25 https://doi.org/10.1016/s0006-3495(03)75000-0
    26 https://doi.org/10.1016/s0014-5793(99)00622-5
    27 https://doi.org/10.1016/s0301-4622(01)00154-5
    28 https://doi.org/10.1021/ja809947w
    29 https://doi.org/10.1021/jp015514e
    30 https://doi.org/10.1038/msb4100063
    31 https://doi.org/10.1039/b903019k
    32 https://doi.org/10.1073/pnas.0501043102
    33 https://doi.org/10.1073/pnas.0605504103
    34 https://doi.org/10.1073/pnas.122076099
    35 https://doi.org/10.1073/pnas.70.3.830
    36 https://doi.org/10.1073/pnas.75.2.559
    37 https://doi.org/10.1080/10826069908544933
    38 https://doi.org/10.1081/pb-100104905
    39 https://doi.org/10.1093/bioinformatics/btm257
    40 https://doi.org/10.1103/physreve.65.061910
    41 https://doi.org/10.1103/physrevlett.89.208701
    42 https://doi.org/10.1107/s002188981000110x
    43 https://doi.org/10.1529/biophysj.105.064485
    44 https://doi.org/10.1529/biophysj.106.098004
    45 https://doi.org/10.1529/biophysj.106.099903
    46 schema:datePublished 2012-12
    47 schema:datePublishedReg 2012-12-01
    48 schema:description BACKGROUND: The three-dimensional structure of a protein can be described as a graph where nodes represent residues and the strength of non-covalent interactions between them are edges. These protein contact networks can be separated into long and short-range interactions networks depending on the positions of amino acids in primary structure. Long-range interactions play a distinct role in determining the tertiary structure of a protein while short-range interactions could largely contribute to the secondary structure formations. In addition, physico chemical properties and the linear arrangement of amino acids of the primary structure of a protein determines its three dimensional structure. Here, we present an extensive analysis of protein contact subnetworks based on the London van der Waals interactions of amino acids at different length scales. We further subdivided those networks in hydrophobic, hydrophilic and charged residues networks and have tried to correlate their influence in the overall topology and organization of a protein. RESULTS: The largest connected component (LCC) of long (LRN)-, short (SRN)- and all-range (ARN) networks within proteins exhibit a transition behaviour when plotted against different interaction strengths of edges among amino acid nodes. While short-range networks having chain like structures exhibit highly cooperative transition; long- and all-range networks, which are more similar to each other, have non-chain like structures and show less cooperativity. Further, the hydrophobic residues subnetworks in long- and all-range networks have similar transition behaviours with all residues all-range networks, but the hydrophilic and charged residues networks don't. While the nature of transitions of LCC's sizes is same in SRNs for thermophiles and mesophiles, there exists a clear difference in LRNs. The presence of larger size of interconnected long-range interactions in thermophiles than mesophiles, even at higher interaction strength between amino acids, give extra stability to the tertiary structure of the thermophiles. All the subnetworks at different length scales (ARNs, LRNs and SRNs) show assortativity mixing property of their participating amino acids. While there exists a significant higher percentage of hydrophobic subclusters over others in ARNs and LRNs; we do not find the assortative mixing behaviour of any the subclusters in SRNs. The clustering coefficient of hydrophobic subclusters in long-range network is the highest among types of subnetworks. There exist highly cliquish hydrophobic nodes followed by charged nodes in LRNs and ARNs; on the other hand, we observe the highest dominance of charged residues cliques in short-range networks. Studies on the perimeter of the cliques also show higher occurrences of hydrophobic and charged residues' cliques. CONCLUSIONS: The simple framework of protein contact networks and their subnetworks based on London van der Waals force is able to capture several known properties of protein structure as well as can unravel several new features. The thermophiles do not only have the higher number of long-range interactions; they also have larger cluster of connected residues at higher interaction strengths among amino acids, than their mesophilic counterparts. It can reestablish the significant role of long-range hydrophobic clusters in protein folding and stabilization; at the same time, it shed light on the higher communication ability of hydrophobic subnetworks over the others. The results give an indication of the controlling role of hydrophobic subclusters in determining protein's folding rate. The occurrences of higher perimeters of hydrophobic and charged cliques imply the role of charged residues as well as hydrophobic residues in stabilizing the distant part of primary structure of a protein through London van der Waals interaction.
    49 schema:genre research_article
    50 schema:inLanguage en
    51 schema:isAccessibleForFree true
    52 schema:isPartOf N061073d4f1694d08aa3f0b45a5485155
    53 N3683a809a7ee444f9c21aef514bc9351
    54 sg:journal.1023786
    55 schema:name Role of long- and short-range hydrophobic, hydrophilic and charged residues contact network in protein’s structural organization
    56 schema:pagination 142
    57 schema:productId N0f4dc3b54dad401abaecc268d26519c8
    58 N426971a3b7024f98aeaac82a2bc1ceb5
    59 N5c067ae13f7c4a6392705cf7ad7765b7
    60 N7612cba848204786af21e6a906ce9c65
    61 Ne8f15545cd7e4e83838118de3d69869b
    62 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011542450
    63 https://doi.org/10.1186/1471-2105-13-142
    64 schema:sdDatePublished 2019-04-11T09:58
    65 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    66 schema:sdPublisher Na4b7c1b265694242b597a2f3ccb479fb
    67 schema:url https://link.springer.com/10.1186%2F1471-2105-13-142
    68 sgo:license sg:explorer/license/
    69 sgo:sdDataset articles
    70 rdf:type schema:ScholarlyArticle
    71 N061073d4f1694d08aa3f0b45a5485155 schema:volumeNumber 13
    72 rdf:type schema:PublicationVolume
    73 N0f4dc3b54dad401abaecc268d26519c8 schema:name dimensions_id
    74 schema:value pub.1011542450
    75 rdf:type schema:PropertyValue
    76 N3683a809a7ee444f9c21aef514bc9351 schema:issueNumber 1
    77 rdf:type schema:PublicationIssue
    78 N426971a3b7024f98aeaac82a2bc1ceb5 schema:name pubmed_id
    79 schema:value 22720789
    80 rdf:type schema:PropertyValue
    81 N4d222b9779564846bbf4c145780fcf34 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    82 schema:name Proteins
    83 rdf:type schema:DefinedTerm
    84 N51eca48942174756aca06d8eb731064d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    85 schema:name Protein Folding
    86 rdf:type schema:DefinedTerm
    87 N5c067ae13f7c4a6392705cf7ad7765b7 schema:name nlm_unique_id
    88 schema:value 100965194
    89 rdf:type schema:PropertyValue
    90 N5c11c5eed0a74195ae842241b966d254 rdf:first sg:person.0667720624.89
    91 rdf:rest rdf:nil
    92 N6630016ebd514fdfaaaf22b0f67bb6b3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    93 schema:name Protein Conformation
    94 rdf:type schema:DefinedTerm
    95 N7612cba848204786af21e6a906ce9c65 schema:name readcube_id
    96 schema:value 36fe4c5618ee68cdfeb9461949142da5a714eac4732d3b24d20eed368c175c4c
    97 rdf:type schema:PropertyValue
    98 N7d7607c498e64e51bcc573ba5cb6cbbc schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    99 schema:name Amino Acids
    100 rdf:type schema:DefinedTerm
    101 Na4a8d3718daf4e7b9d4b2e26e8f63b04 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    102 schema:name Cluster Analysis
    103 rdf:type schema:DefinedTerm
    104 Na4b7c1b265694242b597a2f3ccb479fb schema:name Springer Nature - SN SciGraph project
    105 rdf:type schema:Organization
    106 Ne8f15545cd7e4e83838118de3d69869b schema:name doi
    107 schema:value 10.1186/1471-2105-13-142
    108 rdf:type schema:PropertyValue
    109 Neabfb111289d4193a788a2b33ad2eea8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
    110 schema:name Hydrophobic and Hydrophilic Interactions
    111 rdf:type schema:DefinedTerm
    112 Nef64e74b93d44a538e30806d8a0a0ed4 rdf:first sg:person.0730313605.11
    113 rdf:rest N5c11c5eed0a74195ae842241b966d254
    114 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
    115 schema:name Chemical Sciences
    116 rdf:type schema:DefinedTerm
    117 anzsrc-for:0306 schema:inDefinedTermSet anzsrc-for:
    118 schema:name Physical Chemistry (incl. Structural)
    119 rdf:type schema:DefinedTerm
    120 sg:journal.1023786 schema:issn 1471-2105
    121 schema:name BMC Bioinformatics
    122 rdf:type schema:Periodical
    123 sg:person.0667720624.89 schema:affiliation https://www.grid.ac/institutes/grid.59056.3f
    124 schema:familyName Kundu
    125 schema:givenName Sudip
    126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0667720624.89
    127 rdf:type schema:Person
    128 sg:person.0730313605.11 schema:affiliation https://www.grid.ac/institutes/grid.59056.3f
    129 schema:familyName Sengupta
    130 schema:givenName Dhriti
    131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0730313605.11
    132 rdf:type schema:Person
    133 sg:pub.10.1007/978-1-4020-6754-9_12455 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049819550
    134 https://doi.org/10.1007/978-1-4020-6754-9_12455
    135 rdf:type schema:CreativeWork
    136 sg:pub.10.1007/bf01039553 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037032124
    137 https://doi.org/10.1007/bf01039553
    138 rdf:type schema:CreativeWork
    139 sg:pub.10.1038/nsb881 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025773167
    140 https://doi.org/10.1038/nsb881
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1002/prot.10419 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025077163
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1002/prot.20109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024856257
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1006/jmbi.1999.3058 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054489997
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1006/jmbi.2001.4775 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028172780
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1016/j.bpj.2009.07.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043603447
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1016/j.febslet.2007.05.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025515124
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1016/j.jmb.2003.08.061 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036026326
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1016/j.jmb.2004.10.055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019364803
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1016/j.pbiomolbio.2003.09.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000407665
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1016/j.physa.2004.08.055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048435270
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1016/j.physa.2006.03.056 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020346950
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1016/j.physa.2012.03.034 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027786819
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1016/s0006-3495(03)75000-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040755744
    167 rdf:type schema:CreativeWork
    168 https://doi.org/10.1016/s0014-5793(99)00622-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015755203
    169 rdf:type schema:CreativeWork
    170 https://doi.org/10.1016/s0301-4622(01)00154-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004639412
    171 rdf:type schema:CreativeWork
    172 https://doi.org/10.1021/ja809947w schema:sameAs https://app.dimensions.ai/details/publication/pub.1006705100
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1021/jp015514e schema:sameAs https://app.dimensions.ai/details/publication/pub.1004528958
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1038/msb4100063 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040362452
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1039/b903019k schema:sameAs https://app.dimensions.ai/details/publication/pub.1018368668
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1073/pnas.0501043102 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002183245
    181 rdf:type schema:CreativeWork
    182 https://doi.org/10.1073/pnas.0605504103 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008266007
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1073/pnas.122076099 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021741329
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1073/pnas.70.3.830 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016169925
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1073/pnas.75.2.559 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019519181
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1080/10826069908544933 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021508317
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1081/pb-100104905 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045682555
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1093/bioinformatics/btm257 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030189369
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.1103/physreve.65.061910 schema:sameAs https://app.dimensions.ai/details/publication/pub.1060728621
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.1103/physrevlett.89.208701 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012572068
    199 rdf:type schema:CreativeWork
    200 https://doi.org/10.1107/s002188981000110x schema:sameAs https://app.dimensions.ai/details/publication/pub.1025349594
    201 rdf:type schema:CreativeWork
    202 https://doi.org/10.1529/biophysj.105.064485 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050658725
    203 rdf:type schema:CreativeWork
    204 https://doi.org/10.1529/biophysj.106.098004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044107791
    205 rdf:type schema:CreativeWork
    206 https://doi.org/10.1529/biophysj.106.099903 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045616207
    207 rdf:type schema:CreativeWork
    208 https://www.grid.ac/institutes/grid.59056.3f schema:alternateName University of Calcutta
    209 schema:name Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta, 92 APC Road, 700009, Kolkata, India
    210 rdf:type schema:Organization
     




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


    ...