[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3167132.3167136acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

How to compute protein residue contacts more accurately?

Published: 09 April 2018 Publication History

Abstract

Computing contacts in proteins is important to several types of studies from Bioinformatics to Structural Biology. An accurate computation of contacts is essential to the correctness and reliability of applications involving folding prediction, protein structure prediction, quality assessment of protein structures, network contacts analysis, thermodynamic stability prediction, protein-protein and protein-ligand interactions, docking and so forth. In this work, we built an extensive database of contacts using about 45,000 structures from PDB to compare three paradigms for contact prospection at the atomic level: distance-based only, distance-geometry-based and distance-angulation-based.
The main contribution of this paper is a critical evaluation of three different paradigms that can be used to compute contacts between protein atoms. We focused on protein-protein interfaces and analyzed four types of contacts, namely hydrogen bonds, aromatic stackings, hydrophobic and ionic (attractive) interactions. We scanned for possible contacts in the range from 0 to 7 Å. Our database with all computed contacts as well as the source code used to populate this database is freely available at bioinfo.dcc.ufmg.br/capri Our data showed the importance of a geometric approach to filter out spurious occluded contacts after about 3.5 Å for aromatic stackings, hydrophobic and ionic interactions. For hydrogen bonds, to filter out spurious contacts, we need to consider the angles involved in the interactions.

References

[1]
Ali Rana Atilgan, Pelin Akan, and Canan Baysal. 2004. Small-world communication of residues and significance for protein dynamics. Biophysical journal 86, 1 (2004), 85--91.
[2]
Frances C Bernstein, Thomas F Koetzle, Graheme JB Williams, Edgar F Meyer, Michael D Brice, John R Rodgers, Olga Kennard, Takehiko Shimanouchi, and Mitsuo Tasumi. 1977. The protein data bank. The FEBS Journal 80, 2 (1977), 319--324.
[3]
George R Bickerton, Alicia P Higueruelo, and Tom L Blundell. 2011. Comprehensive, atomic-level characterization of structurally characterized protein-protein interactions: the PICCOLO database. BMC bioinformatics 12, 1 (2011), 313.
[4]
Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D3 data-driven documents. IEEE transactions on visualization and computer graphics 17, 12 (2011), 2301--2309.
[5]
Peter JA Cock, Tiago Antao, Jeffrey T Chang, Brad A Chapman, Cymon J Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, et al. 2009. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 11 (2009), 1422--1423.
[6]
RC de Melo, C Ribeiro, CS Murray, CJ Veloso, CH da Silveira, G Neshich, W MeiraJr, RL Carceroni, and MM Santoro. 2007. Finding protein-protein interaction patterns by contact map matching. Genet. Mol. Res 6, 4 (2007), 946--963.
[7]
Adam Godzik, Andrzej Kolinski, and Jeffrey Skolnick. 1992. Topology fingerprint approach to the inverse protein folding problem. Journal of molecular biology 227, 1 (1992), 227--238.
[8]
VM Gonçalves-Almeida, Douglas EV Pires, Raquel Cardoso de Melo-Minardi, Carlos Henrique da Silveira, W Meira, and Marcelo M Santoro. 2012. HydroPaCe: understanding and predicting cross-inhibition in serine proteases through hydrophobic patch centroids. Bioinformatics 28, 3 (2012), 342--349.
[9]
Lydia M Gregoret and Fred E Cohen. 1991. Protein folding: Effect of packing density on chain conformation. Journal of molecular biology 219, 1 (1991), 109--122.
[10]
Thomas Hamelryck and Bernard Manderick. 2003. PDB file parser and structure class implemented in Python. Bioinformatics 19, 17 (2003), 2308--2310. arXiv:http://bioinformatics.oxfordjournals.org/content/19/17/2308.full.pdf+html
[11]
Jaap Heringa and Patrick Argos. 1991. Side-chain clusters in protein structures and their role in protein folding. Journal of molecular biology 220, 1 (1991), 151--171.
[12]
Simon J Hubbard and Janet M Thornton. 1993. Naccess. Computer Program, Department of Biochemistry and Molecular Biology, University College London 2, 1 (1993).
[13]
Eric Jones, Travis Oliphant, Pearu Peterson, et al. 2001. SciPy: Open source scientific tools for Python. (2001). http://www.scipy.org/ {Online; accessed 2015-07-04}.
[14]
Byungkook Lee and Frederic M Richards. 1971. The interpretation of protein structures: estimation of static accessibility. Journal of molecular biology 55, 3 (1971), 379--IN4.
[15]
Arthur M Lesk and Cyrus Chothia. 1980. How different amino acid sequences determine similar protein structures: the structure and evolutionary dynamics of the globins. Journal of molecular biology 136, 3 (1980), 225--270.
[16]
P Manavalan and PK Ponnuswamy. 1977. A study of the preferred environment of amino acid residues in globular proteins. Archives of biochemistry and biophysics 184, 2 (1977), 476--487.
[17]
Adauto L Mancini, Roberto H Higa, A Oliveira, Fabiana Dominiquini, Paula R Kuser, Michel EB Yamagishi, Roberto C Togawa, and Goran Neshich. 2004. STING Contacts: a web-based application for identification and analysis of amino acid contacts within protein structure and across protein interfaces. Bioinformatics 20, 13 (2004), 2145--2147.
[18]
IK McDonald, D Naylor, D Jones, and JM Thornton. 1993. HBPLUS computer program. Department of Biochemistry and Molecular Biology, University College, London, UK (1993).
[19]
Sanzo Miyazawa and Robert L Jernigan. 1985. Estimation of effective interresidue contact energies from protein crystal structures: quasi-chemical approximation. Macromolecules 18, 3 (1985), 534--552.
[20]
Kevin W Plaxco, Kim T Simons, and David Baker. 1998. Contact order, transition state placement and the refolding rates of single domain proteins. Journal of molecular biology 277, 4 (1998), 985--994.
[21]
Carlos Silveira, Douglas Pires, Raquelde Melo-Minardi, Cristina Ribeiro, Caio JM Veloso, Julio CD Lopes, Wagner Meira, Goran Neshich, Carlos HI Ramos, Raul Habesch, et al. 2009. Protein cutoff scanning: A comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins. Proteins: Structure, Function, and Bioinformatics 74, 3 (2009), 727--743.
[22]
Vladimir Sobolev, Anatoli Sorokine, Jaime Prilusky, Enrique E Abola, and Marvin Edelman. 1999. Automated analysis of interatomic contacts in proteins. Bioinformatics 15, 4 (1999), 327--332.
[23]
Frank Wilcoxon. 1945. Individual comparisons by ranking methods. Biometrics bulletin (1945), 80--83.

Cited By

View all
  • (2024)The Role of Structural Bioinformatics in Understanding Tumor Necrosis Factor α-Interacting Protein Mechanisms in Chronic Inflammatory Diseases: A ReviewImmuno10.3390/immuno40100024:1(14-42)Online publication date: 15-Jan-2024
  • (2024)Advances in Structural BioinformaticsAdvances in Bioinformatics10.1007/978-981-99-8401-5_2(35-70)Online publication date: 6-Feb-2024
  • (2020)ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfacesBMC Bioinformatics10.1186/s12859-020-3474-121:1Online publication date: 15-Apr-2020

Index Terms

  1. How to compute protein residue contacts more accurately?

    Recommendations

    Comments

    Please enable JavaScript to view thecomments powered by Disqus.

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing
    April 2018
    2327 pages
    ISBN:9781450351911
    DOI:10.1145/3167132
    © 2018 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 09 April 2018

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. contact computation
    2. database
    3. protein interface
    4. protein residue contact
    5. protein-protein interaction

    Qualifiers

    • Research-article

    Conference

    SAC 2018
    Sponsor:
    SAC 2018: Symposium on Applied Computing
    April 9 - 13, 2018
    Pau, France

    Acceptance Rates

    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

    Upcoming Conference

    SAC '25
    The 40th ACM/SIGAPP Symposium on Applied Computing
    March 31 - April 4, 2025
    Catania , Italy

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 11 Dec 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)The Role of Structural Bioinformatics in Understanding Tumor Necrosis Factor α-Interacting Protein Mechanisms in Chronic Inflammatory Diseases: A ReviewImmuno10.3390/immuno40100024:1(14-42)Online publication date: 15-Jan-2024
    • (2024)Advances in Structural BioinformaticsAdvances in Bioinformatics10.1007/978-981-99-8401-5_2(35-70)Online publication date: 6-Feb-2024
    • (2020)ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfacesBMC Bioinformatics10.1186/s12859-020-3474-121:1Online publication date: 15-Apr-2020

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media