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Prediction of protein function from protein sequence and structure

  • James C. Whisstock (a1) and Arthur M. Lesk (a1) (a2)
  • DOI: http://dx.doi.org/10.1017/S0033583503003901
  • Published online: 26 January 2004
Abstract

1. Introduction 308

2. Plan of this article 312

3. Natural mechanisms of development of novel protein functions 313

3.1 Divergence 313

3.2 Recruitment 316

3.3 ‘Mixing and matching’ of domains, including duplication/oligomerization, and domain swapping or fusion 316

4. Classification schemes for protein functions 317

4.1 General schemes 317

4.2 The EC classification 318

4.3 Combined classification schemes 319

4.4 The Gene Ontology Consortium 321

5. Methods for assigning protein function 321

5.1 Detection of protein homology from sequence, and its application to function assignment 321

5.2 Detection of structural similarity, protein structure classifications, and structure/function correlations 326

5.3 Function prediction from amino-acid sequence 327

5.3.1 Databases of single motifs 328

5.3.2 Databases of profiles 329

5.3.3 Databases of multiple motifs 330

5.3.4 Precompiled families 331

5.3.5 Function identification from sequence by feature extraction 331

5.4 Methods making use of structural data 332

6. Applications of full-organism information: inferences from genomic context and protein interaction patterns 334

7. Conclusions 335

8. Acknowledgements 335

9. References 335

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein–protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.

Copyright
Corresponding author
A. M. Lesk, Cambridge Institute for Medical Research, University of Cambridge Clinical School, Wellcome Trust/MRC Building, Hills Road, Cambridge, CB2 2XY, UK. (E-mail: aml2@mrc-lmb.cam.ac.uk)
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Quarterly Reviews of Biophysics
  • ISSN: 0033-5835
  • EISSN: 1469-8994
  • URL: /core/journals/quarterly-reviews-of-biophysics
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