Wyniki wyszukiwania

1

GIDMP: Good protein-protein interaction data metamining practice

100%

Plewczynski D., Klingstrom T.

Cellular and Molecular Biology Letters

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2011

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tom 16

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nr 2

Studying the interactome is one of the exciting frontiers of proteomics, as shown lately at the recent bioinformatics conferences (for example ISMB 2010, or ECCB 2010). Distribution of data is facilitated by a large number of databases. Metamining databases have been created in order to allow researchers access to several databases in one search, but there are serious difficulties for end users to evaluate the metamining effort. Therefore we suggest a new standard, “Good Interaction Data Metamining Practice” (GIDMP), which could be easily automated and requires only very minor inclusion of statistical data on each database homepage. Widespread adoption of the GIDMP standard would provide users with: - a standardized way to evaluate the statistics provided by each metamining database, thus enhancing the end-user experience; - a stable contact point for each database, allowing the smooth transition of statistics; - a fully automated system, enhancing time- and cost-effectiveness The proposed information can be presented as a few hidden lines of text on the source database www page, and a constantly updated table for a metamining database included in the source/credits web page.

2

The interactome: predicting the protein-protein interactions in cells

100%

Plewczynski D., Ginalski K.

Cellular and Molecular Biology Letters

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2009

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tom 14

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nr 1

1-22

The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.

3

Prediction of signal peptides in protein sequences by neural networks

81%

Plewczynski D., Slabinski L., Ginalski K., Rychlewski L.

Acta Biochimica Polonica

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2008

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tom 55

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nr 2

261-267

We present here a neural network-based method for detection of signal peptides (abbreviation used: SP) in proteins. The method is trained on sequences of known signal peptides extracted from the Swiss-Prot protein database and is able to work separately on prokaryotic and eukaryotic proteins. A query protein is dissected into overlapping short sequence fragments, and then each fragment is analyzed with respect to the probability of it being a signal peptide and containing a cleavage site. While the accuracy of the method is comparable to that of other existing prediction tools, it provides a significantly higher speed and portability. The accuracy of cleavage site prediction reaches 73% on heterogeneous source data that contains both prokaryotic and eukaryotic sequences while the accuracy of discrimination between signal peptides and non-signal peptides is above 93% for any source dataset. As a consequence, the method can be easily applied to genome-wide datasets. The software can be downloaded freely from http://rpsp.bioinfo. pl/RPSP.tar.gz.

4

Comparison of proteins based on segments structural similarity

80%

Plewczynski D., Pas J., Von Grotthuss M., Rychlewski L.

Acta Biochimica Polonica

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2004

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tom 51

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nr 1

We present here a simple method for fast and accurate comparison of proteins using their structures. The algorithm is based on structural alignment of segments of Ca chains (with size of 99 or 199 residues). The method is optimized in terms of speed and accuracy. We test it on 97 representative proteins with the similarity measure based on the SCOP classification. We compare our algorithm with the LGscore2 automatic method. Our method has the same accuracy as the LGscore2 algorithm with much faster processing of the whole test set, which is promising. A second test is done using the ToolShop structure prediction evaluation program and shows that our tool is on average slightly less sensitive than the DALI server. Both algorithms give a similar number of correct models, however, the final alignment quality is better in the case of DALI. Our method was implemented under the name 3D-Hit as a web server at http://3dhit.bioinfo.pl/ free for academic use, with a weekly updated database containing a set of 5000 structures from the Protein Data Bank with non-homologous sequences.

5

A support vector machine approach to the identification of phosphorylation sites

80%

Plewczynski D., Tkacz A., Godzik A., Rychlewski L.

Cellular and Molecular Biology Letters

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2005

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tom 10

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nr 1

6

Role of the host genetic variability in the influenza A virus susceptibility

71%

Arcanjo A. C., Mazzocco G., de Oliveira S. F., Plewczynski D., Radomski J. P.

Acta Biochimica Polonica

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2014

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tom 61

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nr 3

7

PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

71%

Chatterjee P., Basu S., Kundu M., Nasipuri M., Plewczynski D.

Cellular and Molecular Biology Letters

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2011

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tom 16

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nr 2

Protein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

8

MLK4 drives aggressiveness of triple-negative breast cancer through NF-κB dependent mechanism

42%

Marusiak A. A., Prelowska M. K., Mehlich D., Lazniewski M., Kaminska K., Gorczynski A., Korwat A., Sokolowska O., Golab J., Biernat W., Plewczynski D., Brognard J., Nowis D.

Acta Biochimica Polonica

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2018

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tom 65

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nr S2

9

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In vitro immune evaluation of adenoviral vector-based platform for infectious diseases

37%

Baran J., Kuryk L., Szczepinska T., Lazniewski M., Garofalo M., Mazurkiewicz-Pisarek A., Mikiewicz D., Mazurkiewicz A., Trzaskowski M., Wieczorek M., Pancer K., Hallmann E., Brydak L., Plewczynski D., Ciach T., Mierzejewska J., Staniszewska M.

BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology

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2023

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tom 104

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nr 4

Ograniczanie wyników

GIDMP: Good protein-protein interaction data metamining practice

The interactome: predicting the protein-protein interactions in cells

Prediction of signal peptides in protein sequences by neural networks

Comparison of proteins based on segments structural similarity

A support vector machine approach to the identification of phosphorylation sites

Role of the host genetic variability in the influenza A virus susceptibility

PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

MLK4 drives aggressiveness of triple-negative breast cancer through NF-κB dependent mechanism

In vitro immune evaluation of adenoviral vector-based platform for infectious diseases