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Glyceraldehyde-3-phosphate dehydrogenase from human sperm (GAPDHS) provides energy to the sperm flagellum, and is therefore essential for sperm motility and male fertility. This isoform is distinct from somatic GAPDH, not only in being specific for the testis but also because it contains an additional amino-terminal region that encodes a proline-rich motif that is known to bind to the fibrous sheath of the sperm tail. By conducting a large-scale sequence comparison on low-complexity sequences available in databases, we identified a strong similarity between the proline-rich motif from GAPDHS and the proline-rich sequence from Ena/vasodilator-stimulated phosphoprotein-like (EVL), which is known to bind an SH3 domain of dynamin-binding protein (DNMBP). The putative binding partners of the proline-rich GAPDHS motif include SH3 domain-binding protein 4 (SH3BP4) and the IL2-inducible T-cell kinase/tyrosine-protein kinase ITK/TSK (ITK). This result implies that GAPDHS participates in specific signal-transduction pathways. Gene Ontology category-enrichment analysis showed several functional classes shared by both proteins, of which the most interesting ones are related to signal transduction and regulation of hydrolysis. Furthermore, a mutation of one EVL proline to leucine is known to cause colorectal cancer, suggesting that mutation of homologous amino acid residue in the GAPDHS motif may be functionally deleterious.

Here, we present two perspectives on the task of predicting post translational modifications (PTMs) from local sequence fragments using machine learning algorithms. The first is the description of the fundamental steps required to construct a PTM predictor from the very beginning. These steps include data gathering, feature extraction, or machine-learning classifier selection. The second part of our work contains the detailed discussion of more advanced problems which are encountered in PTM prediction task. Probably the most challenging issues which we have covered here are: (1) how to address the training data class imbalance problem (we also present statistics describing the problem); (2) how to properly set up cross-validation folds with an approach which takes into account the homology of protein data records, to address this problem we present our folds-over-clusters algorithm; and (3) how to efficiently reach for new sources of learning features. Presented techniques and notes resulted from intense studies in the field, performed by our and other groups, and can be useful both for researchers beginning in the field of PTM prediction and for those who want to extend the repertoire of their research techniques.

Here, we present two perspectives on the task of predicting post translational modifications (PTMs) from local sequence fragments using machine learning algorithms. The first is the description of the fundamental steps required to construct a PTM predictor from the very beginning. These steps include data gathering, feature extraction, or machine-learning classifier selection. The second part of our work contains the detailed discussion of more advanced problems which are encountered in PTM prediction task. Probably the most challenging issues which we have covered here are: (1) how to address the training data class imbalance problem (we also present statistics describing the problem); (2) how to properly set up cross-validation folds with an approach which takes into account the homology of protein data records, to address this problem we present our folds-over-clusters algorithm; and (3) how to efficiently reach for new sources of learning features. Presented techniques and notes resulted from intense studies in the field, performed by our and other groups, and can be useful both for researchers beginning in the field of PTM prediction and for those who want to extend the repertoire of their research techniques.

Glyceraldehyde-3-phosphate dehydrogenase from human sperm (GAPDHS) provides energy to the sperm flagellum, and is therefore essential for sperm motility and male fertility. This isoform is distinct from somatic GAPDH, not only in being specific for the testis but also because it contains an additional amino-terminal region that encodes a proline-rich motif that is known to bind to the fibrous sheath of the sperm tail. By conducting a large-scale sequence comparison on low-complexity sequences available in databases, we identified a strong similarity between the proline-rich motif from GAPDHS and the proline-rich sequence from Ena/vasodilator-stimulated phosphoprotein-like (EVL), which is known to bind an SH3 domain of dynamin-binding protein (DNMBP). The putative binding partners of the proline-rich GAPDHS motif include SH3 domain-binding protein 4 (SH3BP4) and the IL2-inducible T-cell kinase/tyrosine-protein kinase ITK/TSK (ITK). This result implies that GAPDHS participates in specific signal-transduction pathways. Gene Ontology category-enrichment analysis showed several functional classes shared by both proteins, of which the most interesting ones are related to signal transduction and regulation of hydrolysis. Furthermore, a mutation of one EVL proline to leucine is known to cause colorectal cancer, suggesting that mutation of homologous amino acid residue in the GAPDHS motif may be functionally deleterious.

In this paper we investigate a special case of classification problem, called multi-label learning, where each instance (or object) is associated with a set of target labels (or simple decisions). Multi-label classification is one of the most important issues in semantic indexing and text categorization systems. Most of multi-label classification methods are based on combination of binary classifiers, which are trained separately for each label. In this paper we concentrate on the application of ensemble technique to multi-label classification problem. We present the most recent ensemble methods for both the binary classifier training phase as well as the combination learning phase. The proposed methods have been implemented within the SONCA system which is a part of SYNAT project. We present some experiment results performed on PubMed Central biomedical articles database.