Literature DB >> 12581652

Functional sites in protein families uncovered via an objective and automated graph theoretic approach.

Pramod P Wangikar1, Ashish V Tendulkar, S Ramya, Deepali N Mali, Sunita Sarawagi.   

Abstract

We report a method for detection of recurring side-chain patterns (DRESPAT) using an unbiased and automated graph theoretic approach. We first list all structural patterns as sub-graphs where the protein is represented as a graph. The patterns from proteins are compared pair-wise to detect patterns common to a protein pair based on content and geometry criteria. The recurring pattern is then detected using an automated search algorithm from the all-against-all pair-wise comparison data of proteins. Intra-protein pattern comparison data are used to enable detection of patterns recurring within a protein. A method has been proposed for empirical calculation of statistical significance of recurring pattern. The method was tested on 17 protein sets of varying size, composed of non-redundant representatives from SCOP superfamilies. Recurring patterns in serine proteases, cysteine proteases, lipases, cupredoxin, ferredoxin, ferritin, cytochrome c, aspartoyl proteases, peroxidases, phospholipase A2, endonuclease, SH3 domain, EF-hand and lectins show additional residues conserved in the vicinity of the known functional sites. On the basis of the recurring patterns in ferritin, EF-hand and lectins, we could separate proteins or domains that are structurally similar yet different in metal ion-binding characteristics. In addition, novel recurring patterns were observed in glutathione-S-transferase, phospholipase A2 and ferredoxin with potential structural/functional roles. The results are discussed in relation to the known functional sites in each family. Between 2000 and 50,000 patterns were enumerated from each protein with between ten and 500 patterns detected as common to an evolutionarily related protein pair. Our results show that unbiased extraction of functional site pattern is not feasible from an evolutionarily related protein pair but is feasible from protein sets comprising five or more proteins. The DRESPAT method does not require a user-defined pattern, size or location of the pattern and therefore, has the potential to uncover new functional sites in protein families.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 12581652     DOI: 10.1016/s0022-2836(02)01384-0

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  34 in total

1.  Automated prediction of protein function and detection of functional sites from structure.

Authors:  Florencio Pazos; Michael J E Sternberg
Journal:  Proc Natl Acad Sci U S A       Date:  2004-09-29       Impact factor: 11.205

2.  Comparison of substructural epitopes in enzyme active sites using self-organizing maps.

Authors:  Katrin Kupas; Alfred Ultsch; Gerhard Klebe
Journal:  J Comput Aided Mol Des       Date:  2004-11       Impact factor: 3.686

3.  Structure-based function inference using protein family-specific fingerprints.

Authors:  Deepak Bandyopadhyay; Jun Huan; Jinze Liu; Jan Prins; Jack Snoeyink; Wei Wang; Alexander Tropsha
Journal:  Protein Sci       Date:  2006-06       Impact factor: 6.725

4.  Association of putative concave protein-binding sites with the fluctuation behavior of residues.

Authors:  Asli Ertekin; Ruth Nussinov; Turkan Haliloglu
Journal:  Protein Sci       Date:  2006-10       Impact factor: 6.725

5.  Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development.

Authors:  Deepak Bandyopadhyay; Jun Huan; Jan Prins; Jack Snoeyink; Wei Wang; Alexander Tropsha
Journal:  J Comput Aided Mol Des       Date:  2009-06-20       Impact factor: 3.686

6.  Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications.

Authors:  Deepak Bandyopadhyay; Jun Huan; Jan Prins; Jack Snoeyink; Wei Wang; Alexander Tropsha
Journal:  J Comput Aided Mol Des       Date:  2009-06-23       Impact factor: 3.686

Review 7.  Exploring the structure and function paradigm.

Authors:  Oliver C Redfern; Benoit Dessailly; Christine A Orengo
Journal:  Curr Opin Struct Biol       Date:  2008-06       Impact factor: 6.809

8.  Graphlet kernels for prediction of functional residues in protein structures.

Authors:  Vladimir Vacic; Lilia M Iakoucheva; Stefano Lonardi; Predrag Radivojac
Journal:  J Comput Biol       Date:  2010-01       Impact factor: 1.479

Review 9.  Protein function annotation by homology-based inference.

Authors:  Yaniv Loewenstein; Domenico Raimondo; Oliver C Redfern; James Watson; Dmitrij Frishman; Michal Linial; Christine Orengo; Janet Thornton; Anna Tramontano
Journal:  Genome Biol       Date:  2009-02-02       Impact factor: 13.583

10.  FLORA: a novel method to predict protein function from structure in diverse superfamilies.

Authors:  Oliver C Redfern; Benoît H Dessailly; Timothy J Dallman; Ian Sillitoe; Christine A Orengo
Journal:  PLoS Comput Biol       Date:  2009-08-28       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.