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.
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 and A/Aichi/2/68). Using docking and molecular dynamics, we have modeled 235 enzyme-ligand complexes for 185 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 of the considered strains by a particular inhibitor and to predict the range of its action on neuraminidases from various influenza virus strains.
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).
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.
A set of models for preliminary estimation of the inhibition constant values of potential ligands for the 4 acetylcholine muscarinic receptors M1-M4 was developed. The study uses an information about three-dimensional structure of human M1, M2 and M4 receptors, as well as the M3 receptor model, constructed by homology based on the structure of the rat M3 receptor. The Ki values for 42 compounds were obtained from the sources. Modeling of “protein-ligand” complexes was performed using molecular docking and molecular dynamics procedures. The component energy characteristics of the complexes were calculated from data obtained from simulation of molecular dynamics by the MM-PBSA/MM-GBSA methods. These characteristics were used as independent variables to construct the linear regression equations for pKi value predicting. The equations obtained for each receptors allow us to predict pKi with an average accuracy of 0.65 logarithmic units.
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