NestedMICA as an ab initio protein motif discovery tool

BackgroundDiscovering overrepresented patterns in amino acid sequences is an important step in protein functional element identification. We adapted and extended NestedMICA, an ab initio motif finder originally developed for finding transcription binding site motifs, to find short protein signals, and compared its performance with another popular protein motif finder, MEME. NestedMICA, an open source protein motif discovery tool written in Java, is driven by a Monte Carlo technique called Nested Sampling. It uses multi-class sequence background models to represent different "uninteresting" parts of sequences that do not contain motifs of interest. In order to assess NestedMICA as a protein motif finder, we have tested it on synthetic datasets produced by spiking instances of known motifs into a randomly selected set of protein sequences. NestedMICA was also tested using a biologically-authentic test set, where we evaluated its performance with respect to varying sequence length.ResultsGenerally NestedMICA recovered most of the short (3–9 amino acid long) test protein motifs spiked into a test set of sequences at different frequencies. We showed that it can be used to find multiple motifs at the same time, too. In all the assessment experiments we carried out, its overall motif discovery performance was better than that of MEME.ConclusionNestedMICA proved itself to be a robust and sensitive ab initio protein motif finder, even for relatively short motifs that exist in only a small fraction of sequences.AvailabilityNestedMICA is available under the Lesser GPL open-source license from: http://www.sanger.ac.uk/Software/analysis/nmica/

[1]  Mona Singh,et al.  A combinatorial optimization approach for diverse motif finding applications , 2006, Algorithms for Molecular Biology.

[2]  Robert B. Russell,et al.  DILIMOT: discovery of linear motifs in proteins , 2006, Nucleic Acids Res..

[3]  Aris Floratos,et al.  Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm [published erratum appears in Bioinformatics 1998;14(2): 229] , 1998, Bioinform..

[4]  Leszek Rychlewski,et al.  ELM server: a new resource for investigating short functional sites in modular eukaryotic proteins , 2003, Nucleic Acids Res..

[5]  Tim J. P. Hubbard,et al.  Large-Scale Discovery of Promoter Motifs in Drosophila melanogaster , 2006, PLoS Comput. Biol..

[6]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[7]  Stuart German,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1988 .

[8]  Shandar Ahmad,et al.  PSSM-based prediction of DNA binding sites in proteins , 2005, BMC Bioinformatics.

[9]  Oliver Kohlbacher,et al.  MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition , 2006, Bioinform..

[10]  Rolf Apweiler,et al.  InterProScan: protein domains identifier , 2005, Nucleic Acids Res..

[11]  Rolf Apweiler,et al.  The SWISS-PROT protein sequence data bank and its new supplement TREMBL , 1996, Nucleic Acids Res..

[12]  T. Hubbard,et al.  NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence , 2005, Nucleic acids research.

[13]  S. Brunak,et al.  Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. , 2000, Journal of molecular biology.

[14]  Oscar Firschein,et al.  Readings in computer vision: issues, problems, principles, and paradigms , 1987 .

[15]  Gary D. Stormo,et al.  Identifying DNA and protein patterns with statistically significant alignments of multiple sequences , 1999, Bioinform..

[16]  Grahame B. Smith.  Stuart Geman and Donald Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images”; , 1987 .

[17]  Charles Elkan,et al.  The Value of Prior Knowledge in Discovering Motifs with MEME , 1995, ISMB.

[18]  A P Burgard,et al.  Review of the TEIRESIAS-based tools of the IBM Bioinformatics and Pattern Discovery Group. , 2001, Metabolic engineering.

[19]  Chittibabu Guda,et al.  Erratum: pTARGET: A new method for predicting protein subcellular localization in eukaryotes (Bioinformatics) vol. 21(21) (3963-3969)) , 2005 .

[20]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Chittibabu Guda,et al.  TARGET: a new method for predicting protein subcellular localization in eukaryotes , 2005, Bioinform..

[22]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[23]  Nikolaj Blom,et al.  BIOINFORMATICS APPLICATIONS NOTE Sequence analysis NetAcet: prediction of N-terminal acetylation sites , 2004 .

[24]  Igor B. Kuznetsov,et al.  DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins , 2007, Bioinform..

[25]  S. Subramaniam,et al.  pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes. , 2005, Bioinformatics.

[26]  Amos Bairoch,et al.  The PROSITE database , 2005, Nucleic Acids Res..

[27]  Seungwoo Hwang,et al.  Using evolutionary and structural information to predict DNA‐binding sites on DNA‐binding proteins , 2006, Proteins.

[28]  Michael B. Yaffe,et al.  Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs , 2003, Nucleic Acids Res..

[29]  Adam Godzik,et al.  Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences , 2006, Bioinform..

[30]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[31]  Patrick Ng,et al.  Apples to apples: improving the performance of motif finders and their significance analysis in the Twilight Zone , 2006, ISMB.