Drug-drug interaction prediction using PASS.

Simultaneous use of the drugs may lead to undesirable Drug-Drug Interactions (DDIs) in the human body. Many DDIs are associated with changes in drug metabolism that performed by Drug-Metabolizing Enzymes (DMEs). In this case, DDI manifests itself as a result of the effect of one drug on the biotransformation of other drug(s), its slowing down (in the case of inhibiting DME) or acceleration (in case of induction of DME), which leads to a change in the pharmacological effect of the drugs combination. We used OpeRational ClassificAtion (ORCA) system for categorizing DDIs. ORCA divides DDIs into five classes: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). We collected a training set consisting of several thousands of drug pairs. Algorithm of PASS program was used for the first, second and third classes DDI prediction. Chemical descriptors called PoSMNA (Pairs of Substances Multilevel Neighbourhoods of Atoms) were developed and implemented in PASS software to describe in a machine-readable format drug substances pairs instead of the single molecules. The average accuracy of DDI class prediction is about 0.84. A freely available web resource for DDI prediction was developed (http://way2drug.com/ddi/).

[Determination of Amino Acid Residues Responsible for Specific Interaction of Protein Kinases with Small Molecule Inhibitors].

Identifying amino acid positions that determine the specific interaction of proteins with small molecule ligands, is required for search of pharmaceutical targets, drug design, and solution of other biotechnology problems. We studied applicability of an original method SPrOS (specificity projection on sequence) developed to recognize functionally significant positions in amino acid sequences. The method allows residues specific to functional subgroups to be determined within the protein family based on their local surroundings in amino acid sequences. The efficiency of the method has been estimated on the protein kinase family. The residues associated with the protein specificity to inhibitors have been predicted. The results have been verified using 3D structures of protein-ligand complexes. Three small molecule inhibitors have been tested. Residues predicted with SPrOS either in contacted the inhibitor or influenced the conformation of the ligand-binding area. Excluding close homologues from the studied set makes it possible to decrease the number of difficult to interpret positions. The expediency of this procedure was determined by the relationship between an inhibitory spectrum and phylogenic partition. Thus, the method efficiency has been confirmed by matching the prediction results with the protein 3D structures.

[Application of molecular descriptors for recognition of phosphorylation sites in amino acid sequences]. / Ispol'zovanie molekuliarnykh deskriptorov dlia raspoznavaniia saitov fosforilirovaniia v aminokislotnykh posledovatel'nostiakh.

Recognition of the phosphorylation sites in proteins is required for reconstruction of regulatory processes in living systems. This task is complicated because the phosphorylation motifs in amino acid sequences are considerably degenerated. To improve the prediction efficacy researchers often use additional descriptors, which should reflect physicochemical features of site-surrounding regions. We have evaluated the reasonability of this approach by applying molecular descriptors (MNA) for structural presentation of the peptide segments. Comparative testing was performed using the prognostic method PASS and two input data types: sets of the MNA descriptors represented peptides as chemical structures and amino acid sequences written using a one-letter code. Training sets were classified in accordance with the established types of the enzymes (protein kinases), modifying corresponding phosphorylation sites. The accuracy estimates obtained by prognosis validation for various classes of substrates were significantly different with both the letters and molecular descriptors. In case of the letter description, the prognosis accuracy demonstrated less dependence on the length of peptides in the training set, while in the case of structural descriptors the accuracy level was determined by the peptide size and descriptor characteristics (MNA levels). The maximal prognosis accuracy related to various kinase families was achieved at different sizes of molecular fragments covered by the MNA descriptors of corresponding levels. This obviously reflected structural differences in surroundings of phosphorylation sites modified by various protein kinases. The use of molecular descriptors provided the prognostic results comparable with the results obtained using traditional letter representation. The prognosis accuracy demonstrated less dependence on the method describing site-surrounding peptides at higher accuracy rates. Applying the MNA descriptors it is possible to achieve better accuracy in the cases when the letter description cannot provide acceptable accuracy.