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1

Comparison of proteins based on segments structural similarity.

100%

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

Acta Biochimica Polonica

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2004

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vol. 51

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issue 1

161-172

EN

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.

2

Prediction of signal peptides in protein sequences by neural networks

100%

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

Acta Biochimica Polonica

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2008

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vol. 55

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issue 2

261-267

EN

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.

3

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

88%

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

Acta Biochimica Polonica

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2014

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vol. 61

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issue 3

403-419

EN

The aftermath of influenza infection is determined by a complex set of host-pathogen interactions, where genomic variability on both viral and host sides influences the final outcome. Although there exists large body of literature describing influenza virus variability, only a very small fraction covers the issue of host variance. The goal of this review is to explore the variability of host genes responsible for host-pathogen interactions, paying particular attention to genes responsible for the presence of sialylated glycans in the host endothelial membrane, mucus, genes used by viral immune escape mechanisms, and genes particularly expressed after vaccination, since they are more likely to have a direct influence on the infection outcome.

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3 Acta Biochimica Polonica

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3 Plewczynski D.

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1 Pas J.

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Comparison of proteins based on segments structural similarity.

Prediction of signal peptides in protein sequences by neural networks

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