Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Sobolev, Boris N.; Филимонов, Д. А.; Lagunin, Alexey; Zakharov, Alexey; Koborova, Olga; Kel, Alexander; Poroikov, Vladimir

doi:10.1186/1471-2105-11-313

Cited by 15 publications

(9 citation statements)

References 29 publications

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“…The SPrOS algorithm is a modification of an earlier published method (Sobolev et al, ). Sequences of the preliminary classified training set are compared with the test sequence (“projected” on it).…”

Section: Methodsmentioning

confidence: 99%

Prediction of amino acid positions specific for functional groups in a protein family based on local sequence similarity

Karasev

Veselovsky

Oparina

et al. 2015

J of Molecular Recognition

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The exchange of single amino acid residue in protein can substantially affect the specificity of molecular recognition. Many protein families can be divided into the groups based on specificity to recognized ligands. Prediction of group-discriminating residues within the certain family is extremely necessary for theoretical studies, enzyme engineering, drug design, and so on. The most existing methods use the multiple sequence alignment. They have the limitations in prediction accuracy due to the family sequence divergence and ligand-based grouping. We developed a new method SPrOS (Specificity Projection On Sequence) for estimating the specificity of residues to user-defined groups. SPrOS compares the sequence segments from the test protein and training proteins. Contrary to other segment-comparison approaches extracting the string motifs, SPrOS calculates the scores for single positions by the similarity of their surroundings. The method was evaluated on the simulated sequences and real protein families. The high-prediction accuracy was achieved for simulated sequences, in which SPrOS detected specific positions not predicted with the alignment-based method. For bacterial transcription factors (LacI/GalR) clearly divided into functional groups, the predicted specific residues corresponded to the published experimental data. In a more complicated case of protein kinases classified by inhibitor specificity, the positions predicted with high significance were located in ligand-binding areas. As the ligand specificity is not necessary coincided with phylogeny, evolutionary-coupled mutations could disturb the detection of ligand-specific residues. Excluding proximate homologs of the test protein kinase from the training set, we improved the prediction of the ligand-specific residues. The SPrOS is available at http://www.way2drug.com/spros/

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Section: Methodsmentioning

confidence: 99%

Prediction of amino acid positions specific for functional groups in a protein family based on local sequence similarity

Karasev

Veselovsky

Oparina

et al. 2015

J of Molecular Recognition

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“…Earlier, we have successfully applied the approach described for functional annotation of proteins [37,46] and determination of amino acid residues responsible for ligand binding [34].…”

Section: Prediction Algorithmmentioning

confidence: 99%

Prediction of Protein–Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences

Karasev

Sobolev

Lagunin

et al. 2019

IJMS

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The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein–ligand interactions.

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“…Ca 2+ -dependent calmodulin (CaM) binding in vitro inhibits PP7 catalytic activity [ 88 ]. In plants PP7 function has been implicated in thermotolerance [ 89 ] and light sensing through regulation of phytochrome and cryptochrome [ 90 -92 ]. Together with HRB1, a nuclear ZZ-type zinc fi nger protein, PP7 acts downstream of blue light perception in stomatal aperture control [ 92 ].…”

Section: Phosphatases In Plantsmentioning

confidence: 99%

“…To overcome this shortcoming, several kinase-specifi c phosphorylation sites prediction methods have been developed. Most of the available methods are based on phosphopeptide sequence conservation and different classifi cation methods (e.g., Position-Specifi c Scoring Matrix, Hidden Markov Model, Support Vector Machine), such as ScanSite [ 71 , 72 ], PPSP [ 73 ], PHOSITE [ 74 ], rBBFNN [ 68 ], PredPhospho [ 75 ], NETPHOSK [ 76 , 77 ], GPS [ 78 -80 ], KinasePhos [ 81 , 82 ], AutoMotif [ 83 ], GANNPhos [ 84 ], IEPP [ 85 ], SMALI [ 86 ], SiteSeek [ 87 ], Musite [ 88 ], PAAS [ 89 ], BAE [ 90 ], PostMod [ 91 ], PKIS [ 92 ] , pkaPA [ 93 ], and others [ 94 ].…”

Section: Phosphorylation Network Inferencementioning

confidence: 99%

Plant Phosphoproteomics

2015

Methods in Molecular Biology

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Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates

Cited by 15 publications

References 29 publications

Prediction of amino acid positions specific for functional groups in a protein family based on local sequence similarity

Prediction of amino acid positions specific for functional groups in a protein family based on local sequence similarity

Prediction of Protein–Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences

Plant Phosphoproteomics

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