A computational tool for identifying minimotifs in protein–protein interactions and improving the accuracy of minimotif predictions

Rajasekaran, Sanguthevar; Merlin, Jerlin Camilus; Kundeti, Vamsi; Tian, Mi; Oommen, Aaron; Vyas, Jay; Alaniz, Izua J.; Chung, Keith; Chowdhury, Farah; Deverasatty, Sandeep; Irvey, Tenisha M.; Lacambacal, David; Lara, Darlene; Panchangam, Subhasree; Rathnayake, Viraj; Watts, Paula; Schiller, Martin

doi:10.1002/prot.22868

Cited by 9 publications

(17 citation statements)

References 38 publications

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“…To overcome this issue, both databases have developed additional methods to improve prediction accuracy that rely on the use of additional context information, such as accessibility (using structural models 366 and predictions of intrinsic disorder 72 ), evolutionary conservation, 367 , 368 cell compartment (based on annotation), 126 , 369 and protein–protein interactions. 128 , 370 , 371 These efforts will need to be combined in the future with a clearer user interface so researchers can more easily identify the most relevant instances.…”

Section: Discussionmentioning

confidence: 99%

Classification of Intrinsically Disordered Regions and Proteins

et al. 2014

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Section: Discussionmentioning

confidence: 99%

Classification of Intrinsically Disordered Regions and Proteins

et al. 2014

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“…Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure [27] but minimotifs are short functional peptide sequences obtained after analysis of known protein-protein interactions [28].…”

Section: Introductionmentioning

confidence: 99%

“…Recently several computational tools for identifying Linear motifs [27] and minimotifs in protein-protein interactions [28] have been published. Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure [27] but minimotifs are short functional peptide sequences obtained after analysis of known protein-protein interactions [28] .…”

Section: Introductionmentioning

confidence: 99%

Disordered Patterns in Clustered Protein Data Bank and in Eukaryotic and Bacterial Proteomes

Лобанов

Galzitskaya

2011

PLoS ONE

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We have constructed the clustered Protein Data Bank and obtained clusters of chains of different identity inside each cluster, http://bioinfo.protres.ru/st_pdb/. We have compiled the largest database of disordered patterns (141) from the clustered PDB where identity between chains inside of a cluster is larger or equal to 75% (version of 28 June 2010) by using simple rules of selection. The results of these analyses would help to further our understanding of the physicochemical and structural determinants of intrinsically disordered regions that serve as molecular recognition elements. We have analyzed the occurrence of the selected patterns in 97 eukaryotic and in 26 bacterial proteomes. The disordered patterns appear more often in eukaryotic than in bacterial proteomes. The matrix of correlation coefficients between numbers of proteins where a disordered pattern from the library of 141 disordered patterns appears at least once in 9 kingdoms of eukaryota and 5 phyla of bacteria have been calculated. As a rule, the correlation coefficients are higher inside of the considered kingdom than between them. The patterns with the frequent occurrence in proteomes have low complexity (PPPPP, GGGGG, EEEED, HHHH, KKKKK, SSTSS, QQQQQP), and the type of patterns vary across different proteomes, http://bioinfo.protres.ru/fp/search_new_pattern.html.

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“…The Motif Scan ensemble of programs computationally identifies all motifs within a given user-specified protein, while the Database Search ensemble of programs finds all proteins in a protein database, such as Swiss-prot, that match a given motif. One of the most successful tools in this area of research is Minimotif Miner (MnM) that our team has built [8,9,10,11]. All of the known motif search tools suffer from a high false positive rate especially when the motif length is small.…”

Section: Some Preliminariesmentioning

confidence: 99%

“…In our previous work we have proposed a series of algorithms (called filters) (see e.g., [8,9]) to reduce false positives. Examples include protein-protein interaction filter [8], molecular function filter [9], cell function filter [9], etc. These algorithms are all based on sequence information.…”

Section: Introductionmentioning

confidence: 99%

A Structure Based Algorithm for Improving Motifs Prediction

Pathak

Kundeti

Schiller

et al. 2013

Pattern Recognition in Bioinformatics

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Minimotifs are short contiguous peptide sequences in proteins that are known to have functions. There are many repositories for experimentally validated minimotifs. MnM is one of them. Predicting minimotifs (in unknown sequences) is a challenging and interesting problem in biology. Minimotifs stored in the MnM database range in length from 5 to 15. Any algorithm for predicting minimotifs in an unknown query sequence is likely to have many false positives owing to the short lengths of the motifs looked for. Our team has developed a series of algorithms (called filters) in the past to reduce the false positives and improve the prediction accuracy. All of these algorithms are based on sequence information. In a recent paper we have demonstrated the power of structural information in characterizing motifs. In this paper we present an algorithm that exploits structural information for reducing false positives in motifs prediction. We test the validity of our algorithm using the minimotifs stored in the MnM database. MnM is a web system for minimotif search that our team has built. It houses more than 300,000 minimotifs. Our new algorithm is a learning algorithm that will be trained in the first phase and in the second phase its accuracy will be measured. For any input query protein sequence, MnM identifies a list of putative minimotifs in the query sequence. We currently employ a series of sequence based algorithms to reduce the false positives in the predictions of MnM. For every minimotif stored in MnM, we also store a number of attributes pertinent to the motif. One such attribute is the source of the minimotif. The source is nothing but the protein in which the minimotif is present. For the analysis of our new algorithm we only employ those minimtofis that have multiple sources for positive control. Random data is used as negative data. The basic idea of our algorithm is the hypothesis that a putative minimotif is likely to be valid if its structure in the query sequence is very similar to its structure in its source protein. Another important feature of our algorithm is that it is specific to individual minimotifs. In other words, a unique set of parameters is learnt for every minimotif. We feel that this is a better approach than learning a common set of parameters for all the minimotifs together. Our findings reveal that in most of the cases the occurrences of the minimotifs in their source proteins are structurally similar. Also, typically, Corresponding author.

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A computational tool for identifying minimotifs in protein–protein interactions and improving the accuracy of minimotif predictions

Cited by 9 publications

References 38 publications

Classification of Intrinsically Disordered Regions and Proteins

Classification of Intrinsically Disordered Regions and Proteins

Disordered Patterns in Clustered Protein Data Bank and in Eukaryotic and Bacterial Proteomes

A Structure Based Algorithm for Improving Motifs Prediction

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