SLiMSearch 2.0: biological context for short linear motifs in proteins

Davey, Norman E.; Haslam, Niall; Shields, Denis C.; Edwards, Richard J.

doi:10.1093/nar/gkr402

Cited by 68 publications

(73 citation statements)

References 31 publications

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“…A few approaches have been proposed for SLiM discovery, and the corresponding tools are publicly available, such as SLiMFinder [18], SLiMSearch [19], QuasiMotiFinder [20], MnM [21] and MEME. All these tools are capable of discovering SLiMs from new datasets of proteins.…”

Section: B Slim Identificationmentioning

confidence: 99%

“…The length of the SLiMs were set to 3 − 10 and 2 − 7, the minimum number of sites to 8 and the maximum number of sites to 200. Motifs of length greater than 10 were not considered, by following the general consensus of the most recent studies, which claim that SLiMs are patterns of typically less than 10 amino acids [13], [19]. We chose a minimum number of sites equal to 8 to make sure at least a few complexes have occurrences of the motif, and hence avoiding a large number of zeros in the classification dataset.…”

Section: B Slim Identificationmentioning

confidence: 99%

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A model based on minimotifs for classification of stable protein-protein complexes

Rueda

Pandit

2014

2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

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Prediction of protein-protein interactions (PPIs) is an important problem in biology, since interactions play key role in most biological processes and functions in living cells. PPIs have been studied from many perspectives. Of these, an important problem is prediction of different complex types such as obligate vs. non obligate and transient vs. permanent, among others. We focus on prediction of obligate protein complexes, which are more stable and perform a specific function, as opposed to transient and non-obligate complexes which last for a short period of time. We have modeled the prediction problem using minimotifs, aka short-linear motifs, to extract information contained in the protein sequences to distinguish between obligate and non-obligate PPIs. Incorporating different classifiers such as the k-nearest neighbor (k-NN), the support vector machine (SVM) and linear dimensionality reduction (LDR) yields a very powerful scheme for prediction. On two well-known datasets, the model delivers classification accuracies as high as 99%. Analysis and cross-dataset validation show that the information contained in the training sequences is crucial for prediction and determination of stability in PPIs.

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Section: B Slim Identificationmentioning

confidence: 99%

Section: B Slim Identificationmentioning

confidence: 99%

A model based on minimotifs for classification of stable protein-protein complexes

Rueda

Pandit

2014

2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

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“…Clues to function may also be gained by searching the motif against the whole proteome and looking for enriched biological functions associated with evolutionary conserved occurrences. 28 …”

Section: Candidate Novel Motifsmentioning

confidence: 99%

Interactome-wide prediction of short, disordered protein interaction motifs in humans

et al. 2012

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“…M OTIF search is to find short similar sequence segments in a given set of sequences over an alphabet S, which plays an important role in discovering significant segments in biological sequences, such as transcription factor binding sites [1] and linear protein motifs [2], [3]. The planted (l, d) motif search (PMS) [4] is a widely accepted formulation of the problem.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Exact Algorithm for the Motif Stem Search Problem over Large Alphabets

Huo

Vitter

et al. 2015

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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In recent years, there has been an increasing interest in planted (l, d) motif search (PMS) with applications to discovering significant segments in biological sequences. However, there has been little discussion about PMS over large alphabets. This paper focuses on motif stem search (MSS), which is recently introduced to search motifs on large-alphabet inputs. A motif stem is an l-length string with some wildcards. The goal of the MSS problem is to find a set of stems that represents a superset of all (l , d) motifs present in the input sequences, and the superset is expected to be as small as possible. The three main contributions of this paper are as follows: (1) We build motif stem representation more precisely by using regular expressions. (2) We give a method for generating all possible motif stems without redundant wildcards. (3) We propose an efficient exact algorithm, called StemFinder, for solving the MSS problem. Compared with the previous MSS algorithms, StemFinder runs much faster and reports fewer stems which represent a smaller superset of all (l, d) motifs. StemFinder is freely available at http://sites.google.com/site/feqond/stemfinder.

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SLiMSearch 2.0: biological context for short linear motifs in proteins

Cited by 68 publications

References 31 publications

A model based on minimotifs for classification of stable protein-protein complexes

A model based on minimotifs for classification of stable protein-protein complexes

Interactome-wide prediction of short, disordered protein interaction motifs in humans

An Efficient Exact Algorithm for the Motif Stem Search Problem over Large Alphabets

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