Prediction of the Inhibition of Influenza Virus Neuraminidase Various Strains by Means of a Generalized Model Constructed Using the Data on the Position of Known Ligands.

Several variants of models for predicting the IC50 values of inhibitors of influenza virus neuraminidase are presented for both individual strains and also for combinations of data for neuraminidases of several strains. They are based on the use of calculated energy contributions to the amount of change in the free energy of enzyme-inhibitor complexes. In contrast to previous works, aimed at the complex modeling, we added a procedure of comparison of the docking variants with one of the neuraminidase inhibitors, for which the structure of the complexes was determined experimentally. Selection of reference molecules for the comparison of structure similarity was made using the Tanimoto metrics and the limit of the RMSD value for a similar part of the structure was no more than 2 Å. Using this limitation and filtering datasets for a particular strain by the Q2 value obtained in the leave-one-out control procedure it was possible to construct equations for predicting the IC50 value with a Q2 value close to the minimum confidence threshold (0.57 in this work). Taking into consideration that in this version of the prediction models, a minimum set of energy contributions is used, which does not employ expensive calculations of entropy contributions, the result obtained supports the correctness of using a generalized model based on the data on the position of known ligands to predict the inhibition of neuraminidase of the influenza virus of various strains.

[Prediction of inhibition of influenza virus neuraminidase of various strains by using a generalized model constructed using the data on the position of known ligands]. / Predskazanie ingibirovaniia neiraminidazy virusa grippa razlichnykh shtammov s ispol'zovaniem obobshchennoi modeli, postroennoi c uchetom dannykh o polozhenii izvestnykh ligandov.

Several variants of models for predicting the IC50 values of inhibitors of influenza virus neuraminidase are presented for both individual strains and for combinations of data for neuraminidases of several strains. They are based on the use of calculated energy contributions to the amount of change in the free energy of enzyme-inhibitor complexes. In contrast to previous works, aimed at the complex modeling, we added a procedure of comparison of the docking variants with one of the neuraminidase inhibitors, for which the structure of the complexes was determined experimentally. The choice of the comparison structure was made according to the similarity of structures evaluated using the Tanimoto metrics and the limit of the RMSD value for a similar part of the structure was no more than 2 Å. Using this limitation and filtering datasets for a particular strain by the Q2 value obtained in the leave-one-out control procedure it is possible to construct equations for predicting the IC50 value with a Q2 value close to the minimum confidence threshold (0.57 in this work). Taking into consideration that in this version of the prediction, a minimum set of energy contributions is used, which does not provide for expensive calculations of entropy contributions, the result obtained supports the correctness of using a generalized model based on the data on the position of known ligands to predict the inhibition of neuraminidase of the influenza virus of various strains.

[The model of prediction of the inhibition of neuraminidases of influenza A and B based on a reduced set of energy terms]. / Model' predskazaniia ingibirovaniia neiraminidaz virusa grippa A i B na baze ogranichennogo nabora énergeticheskikh vkladov.

The overall model for prediction of IC50 values for inhibitors of neuraminidase influenza virus A and B has been created. It combines data about IC50 values of complexes of 40 variants of neuraminidases of influenza A (7 serotypes) and B and three known inhibitors (oseltamivir, zanamivir, peramivir). The model also uses only data of enthalpy contributions to the potential energy of inhibitor/protein and substrate (MUNANA)/protein complexes. The calculation procedures are ported to use software with support of GPU accelerators, that significant decrease the computation time. The corresponding correlation coefficient (R²) for pIC50 prediction was within 0.45-0.58, the SEM values of around 0.7 (the range of used pIC50 data set is from 4.55 to 10.22).

[Creation of a generalized model prediction of inhibition of neuraminidase of influenza virus of various strains]. / Sozdanie obobshchennoi modeli predskazaniia ingibirovaniia neiraminidazy virusa grippa razlichnykh shtammov.

Preliminary results of construction of overall model for prediction of IC50 value of ligands of influenza virus neuraminidase of any strain are presented. We used MM-PBSA (MM-GBSA) energy terms calculated for the complexes obtained after modeling of 30 variants of neuraminidase structures, subsequent docking and simulation of molecular dynamics as independent variables in prediction equations. The structures of known neuraminidase-inhibiting drugs (oseltamivir, zanamivir and peramivir) and a neuraminidase substrate (MUNANA) were used as ligands. The correlation equation based on calculated energetic parameters of inhibitor complexes with neuraminidase did not result in the prediction of IC50 with acceptable parameters (R2£0.3). However, if information about binding energy of the substrate used for neuraminidase assay (and IC50 detection) is included the resulting IC50 prediction equations become significant (R2³0.55). It is concluded that models based on IC50 values as a predictable variable and combining information about binding of different ligands to different variants of the target proteins must take into account the binding properties of the substrate (used for IC50 determination). The predictive power of such models depends critically on the quality of the modeling of the ligand-protein complexes.

[The sequence coverage in different methods of mass spectrometry data analysis obtained on model proteins]. / Stepen' pokrytiia aminokislotnoi posledovatel'nosti pri ispol'zovanii razlichnykh metodov analiza mass-spektrometricheskikh dannykh, poluchennykh na model'nykh belkakh.

The aim of this study was to evaluate sequence coverage of five model proteins (CYB5A, SMAD4, RAB27B, FECH, and CXXC1) by means of shotgun proteomic data analysis employing different methods of data treatment including database-dependent search engines (MASCOT and X!Tandem) and de novo sequencing software ((PEAKS, Novor, and PepNovo+). In order to achieve maximal results, multiprotease hydrolysis including enzymes trypsin, LYS-C, ASPN and GluC was performed in solution and using the FASP method. High resolution mass spectrometry was carried out with a Q EXACTIVE HF hybrid mass spectrometer in the positive ionization mode; parent ions with the highest intensity and a charge range from +2 to +6 were fragmented in the HCD mode. 27 experiments were carried out (hydrolysis with each of 5 enzymes in solution, 4 for the FASP protocol, three technical repeats). Using parameters limiting false identification of peptides, the search engines and de novo sequencing software gave similar results. The degree of sequence coverage was not at least 40%, and in the best cases it reached 80-90%. The use of de novo sequencing software resulted in identification of the Y12H amino acid substitution in one model protein (CYB5A).

[Use of de novo sequencing for proteins identification]. / Primenenie metodov de novo sekvenirovaniia dlia identifikatsii belkov.

Three de novo sequencing programs (Novor, PEAKS and PepNovo+) have been used for identification of 48 individual human proteins constituting the Universal Proteomics Standard Set 2 (UPS2) ("Sigma-Aldrich", USA). Experimental data have been obtained by tandem mass spectrometry. The MS/MS was performed using pure UPS2 and UPS2 mixtures with E. coli extract and human plasma samples. Protein detection was based on identification of at least two peptides of 9 residues in length or one peptide containing at least 13 residues. Using these criteria 13 (Novor), 20 (PEAKS) and 11 (PepNovo+) proteins were detected in pure UPS2 sample. Protein identifications in mixed samples were comparable or worse. Better results (by ~20%) were obtained using prediction included high quality identified fragment (TAG) containing at least 7 residues and unidentified additional masses at N- and C-termini (PepNovo+). The latter approach confidently recognized mass-spectrometric artefacts (and probably PTM). Atypical mass changes missed in UNIMOD DB were found (PepNovo+) to be statistically significant at the C-terminus (+23.02, +26.04 and +27.03). Using peptides containing these modifications and milder detection threshold 41 of 48 UPS2 proteins were identified.

[Prediction of selective inhibition of neuraminidase from various influenza virus strains by potential inhibitors]. / Predskazanie selektivnogo tormozheniia neiraminidazy virusa grippa razlichnykh shtammov potentsial'nymi ingibitorami.

A universal model of inhibition of neuraminidases from various influenza virus strains by a particular has been developed. It is based on known 3D data for neuraminidases from three influenza virus strains (A/Tokyo/3/67, A/tern/Australia/G70C/75, B/Lee/40) and modeling of 3D structure of neuraminidases from other strains (A/PR/8/34 è A/Aichi/2/68). Using docking and molecular dynamics, we have modeled 235 enzyme-ligand complexes for 89 compounds with known IC50 values. Selection of final variants among three results obtained for each enzyme-ligand pair and calculation of independent variables for generation of linear regression equations was performed using MM-PBSA/MM-GBSA. This resulted in the set of equations individual strains and the equations pooling all the data. Thus using this approach it is possible to predict inhibition for neuraminidase from each the considered strains by a particular inhibitor and to predict the range of its action on neuraminidases from various influenza virus strains.

[ProteoСat: a tool for planning of proteomic experiments].

ProteoCat is a computer program has been designed to help researchers in the planning of large-scale proteomic experiments. The central part of this program is the subprogram of hydrolysis simulation that supports 4 proteases (trypsin, lysine C, endoproteinases AspN and GluC). For the peptides obtained after virtual hydrolysis or loaded from data file a number of properties important in mass-spectrometric experiments can be calculated or predicted. The data can be analyzed or filtered to reduce a set of peptides. The program is using new and improved modification of our methods developed to predict pI and probability of peptide detection; pI can also be predicted for a number of popular pKa's scales, proposed by other investigators. The algorithm for prediction of peptide retention time was realized similar to the algorithm used in the program SSRCalc. ProteoCat can estimate the coverage of amino acid sequences of proteins under defined limitation on peptides detection, as well as the possibility of assembly of peptide fragments with user-defined size of "sticky" ends. The program has a graphical user interface, written on JAVA and available at http://www.ibmc.msk.ru/LPCIT/ProteoCat.