Computational Methods for Domain Partitioning of Protein Structures

  • Stella Veretnik
  • Ilya Shindyalov


Analysis of protein structures typically begins with decomposition of structure into more basic units, called “structural domains”. The underlying goal is to reduce a complex protein structure to a set of simpler yet structurally meaningful units, each of which can be analyzed independently. Structural semi-independence of domains is their hallmark: domains often have compact structure and can fold or function independently. Domains can undergo so-called “domain shuffling”when they reappear in different combinations in different proteins thus implementing different biological functions (Doolittle, 1995). Proteins can then be conceived as being built of such basic blocks: some, especially small proteins, consist usually of just one domain, while other proteins possess a more complex architecture containing multiple domains. Therefore, the methods for partitioning a structure into domains are of critical importance: their outcome defines the set of basic units upon which structural classifications are built and evolutionary analysis is performed. This is especially true nowadays in the era of structural genomics. Today there are many methods that decompose the structure into domains: some of them are manual (i.e., based on human judgment), others are semiautomatic, and still others are completely automatic (based on algorithms implemented as software). Overall there is a high level of consistency and robustness in the process of partitioning a structure into domains (for ∼80% of proteins); at least for structures where domain location is obvious. The picture is less bright when we consider proteins with more complex architectures—neither human experts nor computational methods can reach consistent partitioning in many such cases. This is a rather accurate reflection of biological phenomena in general since domains are formed by different mechanisms, hence it is nearly impossible to come up with a set of well-defined rules that captures all of the observed cases.


Structural Domain Domain Decomposition Hydrophobic Core Human Expert Domain Decomposition Method 
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© Springer 2007

Authors and Affiliations

  • Stella Veretnik
  • Ilya Shindyalov

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