Nowa wersja platformy jest już dostępna.
Przejdź na https://bibliotekanauki.pl

PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2011 | Vol. 7, no. 2 | 5--12
Tytuł artykułu

Ab initio protein structure prediction – the hydrophobicity distribution analysis

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The three-dimensional structures generated for 20 “never born proteins” (NBP – random amino acid sequence with no significant homology to existing proteins) using two different techniques: ROSETTA (called R in the paper) and “fuzzy oil drop” model (called S in the paper) were compared to estimate the accordance with the assumed model estimating the influence of an external force field on the final structure of the protein. Selected structures are those corresponding to the highest (10 proteins) and lowest (10 proteins) RMS-D values obtained measuring the similarity between the R and S structures. The R structures generated according to an internal force field (the individual inter-molecular interaction) including solvation effects were analyzed using the “fuzzy oil drop” model as target model. The second applied model “fuzzy oil drop” generated structures characterized by an ordered hydrophobic core structure. 13 of the 20 selected S structures appeared to be accordant with the “fuzzy oil drop” model while 6 out of the 20 structures appeared to be accordant with external force field for R structures which suggests a general interpretation of the influence of an external force field on the folding simulation.
Słowa kluczowe
Wydawca

Rocznik
Strony
5--12
Opis fizyczny
Bibliogr. 19 poz., tab., wykr.
Twórcy
  • Department of Biology, University Roma Tre, Viale G. Marconi 446, I-00146 Rome
  • Department of Biology, University Roma Tre, Viale G. Marconi 446, I-00146 Rome
  • Department of Biology, University Roma Tre, Viale G. Marconi 446, I-00146 Rome
autor
  • Institute of Computer Science AGH, Mickiewicza 30, PL-30-059 Krakow
  • Academic Computer Center CYFRONET, Nawojki 11, PL-30-0950 Krakow
  • Academic Computer Center CYFRONET, Nawojki 11, PL-30-0950 Krakow
autor
  • Academic Computer Center CYFRONET, Nawojki 11, PL-30-0950 Krakow
autor
  • Department of Bioinformatics and Telemedicine, Lazarza 16, PL-31-530 Krakow
  • Faculty of Chemistry, Jagiellonian University, Ingardena 3, PL-30-060 Krakow
  • Department of Bioinformatics and Telemedicine, Lazarza 16, PL-31-530 Krakow
  • Faculty of Physics, Astronomy and Applied Computer Science – Jagiellonian University, Reymonta 4, PL30-059 Krakow
  • Department of Bioinformatics and Telemedicine, Lazarza 16, PL-31-530 Krakow
  • Department of Bioinformatics and Telemedicine, Lazarza 16, PL-31-530 Krakow
autor
  • Department of Bioinformatics and Telemedicine, Lazarza 16, PL-31-530 Krakow
autor
  • Department of Bioinformatics and Telemedicine, Lazarza 16, PL-31-530 Krakow
  • Faculty of Physics, Astronomy and Applied Computer Science – Jagiellonian University, Reymonta 4, PL30-059 Krakow
autor
  • Department of Bioinformatics and Telemedicine, Lazarza 16, PL-31-530 Krakow
Bibliografia
  • [1] CASP, http://predictioncenter.org/ (Jan 10. 2011).
  • [2] Chiarabelli C. Vrijbloed J. W. Thomas R. M. Luisi P. L. Investigation of de novo totally random biosequences. Part I: A general method for in vitro selection of folded domains from a random polypeptide library displayed on phage. Chem Biodiversity 2006, 3: 827-839.
  • [3] Chiarabelli C. Vrijbloed J. W. Lucrezia D. D. Thomas R. M. Stano P. Polticelli F. Ottone T. Papa E. Luisi P. L. Investigation of de novo totally random biosequences. Part II: On the folding frequency in a totally random library of de novo proteins obtained by phage display. Chem Biodiversity 2006, 3: 840 -859.
  • [4] Rohl C. A. Strauss C. E. M. Misura K. M. S. Baker D. Protein structure prediction using Rosetta. Methods Enzymol 2004, 383: 66 – 93. http://boinc.bakerlab.org/rosetta/.
  • [5] Evangelista G. Minervini G. Luisi P. L. Polticelli F. RandomBLAST a tool to generate random “never born protein” sequences. Bio-Algorithms and Med-systems 2007, 3: 27-31.
  • [6] Minervini G. Evangelista G. Villanova L. Slanzi D. De Lucrezia D. Poli I. Luisi PL. Polticelli F. Massive non-natural proteins structure prediction using grid technologies. BMC Bioinformatics 2009, 10, Suppl 6: 22.
  • [7] Konieczny L. Brylinski M. Roterman I. Gauss-function-based model of hydrophobicity density in proteins. In Silico Biology 2006, 6: 15-22.
  • [8] Prymula K. Piwowar M. Kochanczyk M. Flis L. Malawski M. Szepieniec T. Evangelista G. Minervini G. Polticelli F. Wisniowski Z. Salapa K. Matczynska E. Roterman I. In silico structural study of random amino acid sequence proteins not present in Nature. Chemistry & Biodiversity 2009, 6: 2311-2336.
  • [9] Levitt M. A simplified representation of protein conformations for rapid simulation of protein folding. J. Mol. Biol. 1976, 104: 59–107.
  • [10] Nalewajski R. Information theory of molecular systems. Amsterdam [etc.]: Elsevier, 2006.
  • [11] Malawski M. Szepieniec T. Kochanczyk M. Piwowar M. Roterman I: An approach to protein folding on the grid - EUChinaGrid experience. Bio-Algorithms Med-Systems 2007, 3, 5: 45-49.
  • [12] Bubak M. et al.. Virtual Laboratory for Collaborative Applications. In: M. Cannataro (Ed.) Handbook of Research on Computational Grid Technologies for Life Sciences. Biomedicine and Healthcare. Chapter 27. pp. 531-551. Information Science Reference, 2009, IGI Global.
  • [13] Bubak M. Malawski M. Rycerz K. Turala M. CrossGrid - Tools and Services for Interactive Grid Applications. TASK QUARTERLY. Scientific Bulletin of Academic Centre in Gdansk. TASK Publishing, 2004, 8: 503-512.
  • [14] Prymula K. Roterman I. Functional characteristics of small proteins (70 aa) forming protein-nucleic acid complexes. J. Biomol. Struct Dynam. 2009, 26: 663-677.
  • [15] Prymula K. Roterman I. Structural entropy to characterize small proteins (70 aa) and their interactions. Entropy 2009, 11: 62-84.
  • [16] Prymula K. Salapa K. Roterman I. “Fuzzy oil drop” model applied to individual small proteins build of 70 amino acids. J. Mol. Model. 2010, 16: 1269-1282.
  • [17] Zobnina V. Roterman I. Application of the fuzzy oil drop model to membrane protein simulation. Proteins 2009, 77: 378-394.
  • [18] Minervini G. Evangelista G. Polticelli F. Piwowar M. Kochanczyk M. Flis L. Szepieniec T. Wisniowski Z. Matczynska E. Prymula K. Roterman I. Never born proteins as a test case for ab initio protein structures prediction. Bioinformation 2008, 3: 177-179.
  • [19] Roterman I. E-infrastructure technologies triggering of bioinformatics development Bioinformation 2007, 2: 126-127.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-e74cbd3c-83a3-4832-9fb7-0bc3444b254f
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.