Literature DB >> 9579654

Recognition of analogous and homologous protein folds--assessment of prediction success and associated alignment accuracy using empirical substitution matrices.

R B Russell1, M A Saqi, P A Bates, R A Sayle, M J Sternberg.   

Abstract

Fold recognition methods aim to use the information in the known protein structures (the targets) to identify that the sequence of a protein of unknown structure (the probe) will adopt a known fold. This paper highlights that the structural similarities sought by these methods can be divided into two types: remote homologues and analogues. Homologues are the result of divergent evolution and often share a common function. We define remote homologues as those that are not easily detectable by sequence comparison methods alone. Analogues do not have a common ancestor and generally do not have a common function. Several sets of empirical matrices for residue substitution, secondary structure conservation and residue accessibility conservation have previously been derived from aligned pairs of remote homologues and analogues (Russell et al., J. Mol. Biol., 1997, 269, 423-439). Here a method for fold recognition, FOLDFIT, is introduced that uses these matrices to match the sequences, secondary structures and residue accessibilities of the probe and target. The approach is evaluated on distinct datasets of analogous and remotely homologous folds. The accuracy of FOLDFIT with the different matrices on the two datasets is contrasted to results from another fold recognition method (THREADER) and to searches using mutation matrices in the absence of any structural information. FOLDFIT identifies at top rank 12 out of 18 remotely homologous folds and five out of nine analogous folds. The average alignment accuracies for residue and secondary structure equivalencing are much higher for homologous folds (residue approximately 42%, secondary structure approximately 78%) than for analogues folds (approximately 12%, approximately 47%). Sequence searches alone can be successful for several homologues in the testing sets but nearly always fail for the analogues. These results suggest that the recognition of analogous and remotely homologous folds should be assessed separately. This study has implications for the development and comparative evaluation of fold recognition algorithms.

Mesh:

Year:  1998        PMID: 9579654     DOI: 10.1093/protein/11.1.1

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  16 in total

1.  Detection of protein fold similarity based on correlation of amino acid properties.

Authors:  I V Grigoriev; S H Kim
Journal:  Proc Natl Acad Sci U S A       Date:  1999-12-07       Impact factor: 11.205

2.  Evaluation of PSI-BLAST alignment accuracy in comparison to structural alignments.

Authors:  I Friedberg; T Kaplan; H Margalit
Journal:  Protein Sci       Date:  2000-11       Impact factor: 6.725

3.  A fully automatic evolutionary classification of protein folds: Dali Domain Dictionary version 3.

Authors:  S Dietmann; J Park; C Notredame; A Heger; M Lappe; L Holm
Journal:  Nucleic Acids Res       Date:  2001-01-01       Impact factor: 16.971

4.  Factors limiting the performance of prediction-based fold recognition methods.

Authors:  X de la Cruz; J M Thornton
Journal:  Protein Sci       Date:  1999-04       Impact factor: 6.725

5.  Two tricks in one bundle: helix-turn-helix gains enzymatic activity.

Authors:  N V Grishin
Journal:  Nucleic Acids Res       Date:  2000-06-01       Impact factor: 16.971

6.  Use of residue pairs in protein sequence-sequence and sequence-structure alignments.

Authors:  J Jung; B Lee
Journal:  Protein Sci       Date:  2000-08       Impact factor: 6.725

7.  Enhanced protein fold recognition using secondary structure information from NMR.

Authors:  D J Ayers; P R Gooley; A Widmer-Cooper; A E Torda
Journal:  Protein Sci       Date:  1999-05       Impact factor: 6.725

8.  Analysis of protein homology by assessing the (dis)similarity in protein loop regions.

Authors:  Anna R Panchenko; Thomas Madej
Journal:  Proteins       Date:  2004-11-15

9.  BCL::contact-low confidence fold recognition hits boost protein contact prediction and de novo structure determination.

Authors:  Mert Karakaş; Nils Woetzel; Jens Meiler
Journal:  J Comput Biol       Date:  2010-02       Impact factor: 1.479

10.  Identification of protein functions using a machine-learning approach based on sequence-derived properties.

Authors:  Bum Ju Lee; Moon Sun Shin; Young Joon Oh; Hae Seok Oh; Keun Ho Ryu
Journal:  Proteome Sci       Date:  2009-08-09       Impact factor: 2.480

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.