A novel approach to the recognition of protein architecture from sequence using fourier analysis and neural networks

Shepherd, Adrian J.; Gorse, Denise; Thornton, Janet M.

doi:10.1002/prot.10290

Cited by 29 publications

(17 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach has been used successfully several times as a starting point to identify protein interactions by ourselves and others groups (several examples are reported in Parbhane et al, 2000;Shepherd et al, 2003;Krishnan et al, 2004;Wen et al, 2005;Sinkala, 2006). By applying the ISM to the analysis of the whole array of human proteins present in the GeneBank database we identified EMILIN1 (Elastin Microfibril Interface Located ProteIN) (Doliana et al, 1999) as well as other members of this family of extracellular matrix (ECM) proteins as novel and additional PA-binding sites.…”

Section: Introductionmentioning

confidence: 99%

EMILINs interact with anthrax protective antigen and inhibit toxin action in vitro

Doliana

Veljković²,

Prljić³

et al. 2008

Matrix Biology

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

EMILINs interact with anthrax protective antigen and inhibit toxin action in vitro

Doliana

Veljković²,

Prljić³

et al. 2008

Matrix Biology

View full text Add to dashboard Cite

“…The classification and assignment of nuclear receptors to various subfamilies was done on the basis of amino acid composition and dipeptide composition. Amino acid and dipeptide compositions are simplistic approaches for producing patterns of fixed length from the protein sequences of varying length (8). In the past, amino acid composition has been used to predict the structural class of domains and the subcellular localization of proteins (9 -11).…”

mentioning

confidence: 99%

Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition

Bhasin

Raghava

2004

Journal of Biological Chemistry

272

201

View full text Add to dashboard Cite

Nuclear receptors are key transcription factors that regulate crucial gene networks responsible for cell growth, differentiation, and homeostasis. Nuclear receptors form a superfamily of phylogenetically related proteins and control functions associated with major diseases (e.g. diabetes, osteoporosis, and cancer). In this study, a novel method has been developed for classifying the subfamilies of nuclear receptors. The classification was achieved on the basis of amino acid and dipeptide composition from a sequence of receptors using support vector machines. The training and testing was done on a non-redundant data set of 282 proteins obtained from the NucleaRDB data base (1). The performance of all classifiers was evaluated using a 5-fold cross validation test. In the 5-fold cross-validation, the data set was randomly partitioned into five equal sets and evaluated five times on each distinct set while keeping the remaining four sets for training. It was found that different subfamilies of nuclear receptors were quite closely correlated in terms of amino acid composition as well as dipeptide composition. The overall accuracy of amino acid composition-based and dipeptide compositionbased classifiers were 82.6 and 97.5%, respectively. Therefore, our results prove that different subfamilies of nuclear receptors are predictable with considerable accuracy using amino acid or dipeptide composition. Furthermore, based on above approach, an online web service, NRpred, was developed, which is available at www.imtech.res.in/raghava/nrpred.The availability of sequence data for different genomes in recent years has increased the demand for computational tools that can recognize new proteins from this data. The recognition of nuclear receptors is crucial, because many of them are potential drug targets for developing therapeutic strategies for diseases like breast cancer and diabetes (2). Nuclear receptors are one of the most abundant classes of transcriptional regulators, which regulate diverse functions during reproduction, metabolism, and development. Nuclear receptors function as ligand-activated transcriptional factors, providing a direct link between the signaling molecules that control these processes and transcriptional responses (3). The nuclear receptors share a common structural organization. All nuclear receptors consist of six distinct regions or domains as follows: highly variable N-terminal and C-terminal regions (A/B and F domains) that contain one or more transactivation regions; a central, well conserved DNA binding domain (C); a non-conserved hinge region (D) that contains a nuclear localization signal (NLS), and a moderately conserved ligand binding domain (E) (4). The DNA binding domain (C region) of nuclear receptors consists of two zinc fingers, which act as a signature for this superfamily (5). The presence of these zinc fingers facilitates the recognition of nuclear receptors from a genome sequence using simple similarity-based search tools like BLAST and FASTA (6 -7). On the other hand, the major li...

show abstract

“…This database included the sequence of all alternative splice isoforms listed in version 43 of SwissProt [46]. However, the method is independent from the origin of AS data, and these can come from other databases like ENSEMBL [47], ASAP [18], etc.…”

Section: Methodsmentioning

confidence: 99%

A procedure for identifying homologous alternative splicing events

2007

View full text Add to dashboard Cite

Background: The study of the functional role of alternative splice isoforms of a gene is a very active area of research in biology. The difficulty of the experimental approach (in particular, in its high-throughput version) leaves ample room for the development of bioinformatics tools that can provide a useful first picture of the problem. Among the possible approaches, one of the simplest is to follow classical protein function annotation protocols and annotate target alternative splice events with the information available from conserved events in other species. However, the application of this protocol requires a procedure capable of recognising such events. Here we present a simple but accurate method developed for this purpose.

show abstract

A novel approach to the recognition of protein architecture from sequence using fourier analysis and neural networks

Cited by 29 publications

References 38 publications

EMILINs interact with anthrax protective antigen and inhibit toxin action in vitro

EMILINs interact with anthrax protective antigen and inhibit toxin action in vitro

Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition

A procedure for identifying homologous alternative splicing events

Contact Info

Product

Resources

About