Collective Motions in Protein Structures: Application of Elastic Network Models Built from Electron Density Distributions

Research output: Contribution to conferencePoster

41 Downloads (Pure)

Abstract

Computational simulations of protein dynamics play an important role in deciphering protein functions, and usually require the knowledge of atomic coordinates. However, for a number of cases, one can only obtain fuzzy images of the molecules by means of experimental techniques. Therefore, a question is whether one can describe the motion of a protein, at least the principal features, based on such images.
It has recently been shown that it is indeed feasible to efficiently extract the information of protein motions, at a reasonable degree of accuracy, without even knowing the precise amino acid sequence. The structural models that are used, such as the Gaussian Network Model (GNM) and the Anisotropic Network Model (ANM), operate under the fundamental assumption that a folded protein can be viewed as an elastic network [1-2]. Numerous Web servers are now available to easily and rapidly evaluate the slow and large-magnitude dynamics of protein structures [3-9].
The present work consists in studying the dynamics of protein structures using topological and structural informations contained in their low-resolution promolecular electron density distribution functions. Dynamical information are obtained from two approaches. The first one consists in building protein network models from ED maxima calculated at various smoothing levels [10]. The second approach also considers ED protein networks, with edges weighted by ED overlap integral values. Results are compared with those obtained through the classical GNM and ANM approaches applied to networks of Cα atoms.

1 T. Haliloglu, I. Bahar, B. Erman, Phys. Rev. Lett. 79 (1997) 3090.
2 A. R. Atilgan, S. R. Durell, R. L. Jernigan et al., Biophys. J. 80 (2001) 505.
3 K. Suhre, Y.-H. Sanejouand, Nucleic Acids Res. 32 (2004) W610; http://www.igs.cnrs-mrs.fr/elnemo/
4 Z. W. Cao, Y. Xue, L. Y. Han et al., Nucleic Acids Res. 32 (2004) W679; http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl
5 L.-W. Yang, X. Liu, Ch. J. Jursa et al., Bioinformatics 21 (2005) 2978; http://ignm.ccbb.pitt.edu/
6 S. M. Hollup, G. Salensminde, N. Reuter, BMC Bioinformatics 6 (2005) 52 ; http://www.bioinfo.no/tools/normalmodes
7 E. Eyal, L.-W. Yang, I. Bahar, Bioinformatics 22 (2006) 2619; http://ignmtest.ccbb.pitt.edu/cgi-bin/anm/anm1.cgi
8 J. I. Garzó, J. Kovacs, R. Abagyan et al., Bioinformatics 23 (2007) 901; http://sbg.cib.csis.es/Software/DFprot
9 Iowa State University, Plant Science Institute, Laurence H. Baker Center for Bioinformatics and Biological Statistics ; http://gor.bb.iastate.edu/anm/anm.htm
10 L. Leherte, Acta Cryst D 60 (2004) 1254.
Original languageEnglish
Publication statusPublished - 2007
EventConference on Computational Physics 2007 (CCP07) - ULB, Bruxelles, Belgium
Duration: 5 Sep 2007 → …

Symposium

SymposiumConference on Computational Physics 2007 (CCP07)
CountryBelgium
CityULB, Bruxelles
Period5/09/07 → …

Fingerprint Dive into the research topics of 'Collective Motions in Protein Structures: Application of Elastic Network Models Built from Electron Density Distributions'. Together they form a unique fingerprint.

  • Projects

    Cite this

    Leherte, L., & Vercauteren, D. (2007). Collective Motions in Protein Structures: Application of Elastic Network Models Built from Electron Density Distributions. Poster session presented at Conference on Computational Physics 2007 (CCP07), ULB, Bruxelles, Belgium.