IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v288y2000i1p31-48.html
   My bibliography  Save this article

Simple models of the protein folding problem

Author

Listed:
  • Tang, Chao

Abstract

The protein folding problem has attracted an increasing attention from physicists. The problem has a flavor of statistical mechanics, but possesses the most common feature of most biological problems – the profound effects of evolution. I will give an introduction to the problem, and then focus on some recent work concerning the so-called “designability principle”. The designability of a structure is measured by the number of sequences that have that structure as their unique ground state. Structures differ drastically in terms of their designability; highly designable structures emerge with a number of associated sequences much larger than the average. These highly designable structures (1) possess “protein-like” secondary structures and motifs, (2) are thermodynamically more stable, (3) fold faster than other structures. These results suggest that protein structures are selected in nature because they are readily designed and stable against mutations, and that such selection simultaneously leads to thermodynamic stability and foldability. According to this picture, a key to the protein folding problem is to understand the emergence and the properties of the highly desginable structures.

Suggested Citation

  • Tang, Chao, 2000. "Simple models of the protein folding problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 288(1), pages 31-48.
  • Handle: RePEc:eee:phsmap:v:288:y:2000:i:1:p:31-48
    DOI: 10.1016/S0378-4371(00)00413-1
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437100004131
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/S0378-4371(00)00413-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Kaiyuan & Huang, Houjing & Vandans, Olafs & Murali, Adithya & Tian, Fujia & Yap, Roland H.C. & Dai, Liang, 2023. "Applying deep reinforcement learning to the HP model for protein structure prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Kozyrev, S.V. & Volovich, I.V., 2014. "Quinary lattice model of secondary structures of polymers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 86-95.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:288:y:2000:i:1:p:31-48. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.