Prediction of features of the course of chronic hepatitis C using Bayesian networks.

MATERIALS AND METHODS: 253 patients with chronic hepatitis C (CHC) and liver cirrhosis were included in the study. Assessment of gene polymorphisms of genes involved in inflammatory reactions and antiviral immunity (IL-1ß-511C/T, IL-10 -1082G/A, IL28B C/T, IL28B T/G, TNF-α -238G/A, TGF-ß -915G/C, IL-6 -174G/C), activators of local hepatic fibrosis (AGT G-6A, AGT 235 M/T, ATR1 1166 A/C), hemochromatosis (HFE C282Y, HFE H63D), platelet receptors (ITGA2 807 C/T, ITGB3 1565 T/C), coagulation proteins and endothelial dysfunction (FII 20210 G/A, FV 1691G/A, FVII 10976 G/A, FXIII 103 G/T, eNOS 894 G/T, CYBA 242 C/T, FBG -455 G/A, PAI-675 5G/4G, MTHFR 677 C/T) was carried. Using Bayesian networks we studied the predictor value of clinical and laboratory factors for the following conditions - end points (EP): development of cirrhosis (EP1), fibrosis rate (EP2), presence of portal hypertension (EP3) and cryoglobulins (EP4). RESULTS AND DISCUSSION: In addition to traditional factors we have shown the contribution of the following mutations. Predicting EP1- liver cirrhosis - HFE H63D, C282Y, CYBA 242 C/T, AGT G-6G, ITGB31565 T/C gene mutations were significant. We also found a link between the rate of progression of liver fibrosis and gene polymorphisms of AGT G-6G, AGT M235T, FV 1691G/A, ITGB31565 T/C. Among the genetic factors associated with portal hypertension there are gene polymorphisms of PAI-I-675 5G/4G, FII 20210 G/A, CYBA 242 C/T, HFE H63D and Il-6 174GC. Cryoglobulins and cryoglobuliemic vasculitis (EP4) are associated with gene mutations MTHFR C677T, ATR A1166C and HFE H63D. CONCLUSION: The results obtained allow to detect the major pathophysiological and genetic factors which determine the status of the patient and the outcome of the disease, to clarify their contribution, and to reveal the significance of point mutations of genes that control the main routes of HCV course and progression.

Molecular modeling as a new approach to the development of urokinase inhibitors.

Proteolytic activity of urokinase plays an important role in negative remodeling of blood vessels, restenosis, tumor angiogenesis, and metastasizing, which necessitates the development of selective urokinase inhibitors. Using methods of computer modeling (docking, post processing, and direct docking) and quantum chemistry, we selected substances from the large compound database, analyzed their structures, and experimentally verified their inhibitor activity. New urokinase inhibitor candidates were proposed based on the theoretical predictions and experimental verification of compound activities. The process of modifying urokinase inhibitors based on (benzothiazol-3-yl)guanidine was developed. A new urokinase inhibitor (5-brom-benzothiazol-3-yl)guanidine, that can be effective for regulation of vascular remodeling and tumor angiogenesis, was created.

Docking and other computing tools in drug design against SARS-CoV-2.

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Atomistic Simulation of the Ion-Assisted Deposition of Silicon Dioxide Thin Films.

A systematic study of the most significant parameters of the ion-assisted deposited silicon dioxide films is carried out using the classical molecular dynamics method. The energy of the deposited silicon and oxygen atoms corresponds to the thermal evaporation of the target; the energy of the assisting oxygen ions is 100 eV. It is found that an increase in the flow of assisting ions to approximately 10% of the flow of deposited atoms leads to an increase in density and refractive index by 0.5 g/cm3 and 0.1, respectively. A further increase in the flux of assisting ions slightly affects the film density and density profile. The concentration of point defects, which affect the optical properties of the films, and stressed structural rings with two or three silicon atoms noticeably decrease with an increase in the flux of assisting ions. The film growth rate somewhat decreases with an increase in the assisting ions flux. The dependence of the surface roughness on the assisting ions flux is investigated. The anisotropy of the deposited films, due to the difference in the directions of motion of the deposited atoms and assisting ions, is estimated using the effective medium approach.

[Development of antiviral drugs based on inhibitors of the SARS-COV-2 main protease]. / Razrabotka protivovirusnykh lekarstv na osnove ingibitorov glavnoi proteazy SARS-CoV-2.

Docking and quantum-chemical methods have been used for screening of drug-like compounds from the own database of the Voronezh State University to find inhibitors the SARS-CoV-2 main protease, an important enzyme of the coronavirus responsible for the COVID-19 pandemic. Using the SOL program more than 42000 3D molecular structures were docked into the active site of the main protease, and more than 1000 ligands with most negative values of the SOL score were selected for further processing. For all these top ligands, the protein-ligand binding enthalpy has been calculated using the PM7 semiempirical quantum-chemical method with the COSMO implicit solvent model. 20 ligands with the most negative SOL scores and the most negative binding enthalpies have been selected for further experimental testing. The latter has been made by measurements of the inhibitory activity against the main protease and suppression of SARS-CoV-2 replication in a cell culture. The inhibitory activity \of the compounds was determined using a synthetic fluorescently labeled peptide substrate including the proteolysis site of the main protease. The antiviral activity was tested against SARS-CoV-2 virus in the Vero cell culture. Eight compounds showed inhibitory activity against the main protease of SARS-CoV-2 in the submicromolar and micromolar ranges of the IC50 values. Three compounds suppressed coronavirus replication in the cell culture at the micromolar range of EC50 values and had low cytotoxicity. The found chemically diverse inhibitors can be used for optimization in order to obtain a leader compound, the basis of new direct-acting antiviral drugs against the SARS-CoV-2 coronavirus.

Short-range combined water model.

The short-range combined water model (SRCW model) for the calculation of the hydration free energy of the non-polar solutes is presented. A mixed explicit/implicit representation of the solvent is used in the model. A thermodynamic basis for the boundary potential between explicit and implicit parts of the simulation area is derived. A simple functional form for the boundary potential minimizing the water density fluctuations in the explicit part is found. Hydration free energies of the model solutes are calculated in the frame of the developed model. Obtained values are in the good agreement with results of the Monte Carlo simulation using the periodic boundary conditions.

[Docking with combined use of a force field and a quantum-chemical method]. / Doking s kombinirovannym primeneniem silovogo polia i kvantovo-khimicheskogo metoda.

The paper presents the results concerning the application of docking programs FLM to combined use of the MMFF94 force field and the semiempirical quantum-chemical method PM7 in the docking procedure. At the first step of this procedure a fairly wide range of low-energy minima of the protein-ligand complex is found in the frame of the MMFF94 force field using the FLM program. The energies of all these minima are recalculated using the PM7 method and the COSMO solvent continuum model at the second step. On the basis of these calculations the deepest minimum of the protein-ligand energy, calculated by the PM7 method with COSMO solvent, is determined, which gives the position of the ligand closest to its position in the crystal of the protein-ligand complex. It is shown that the first step of the combined procedure is performed more quickly and more efficiently in vacuum, rather than with a solvent model.

Computation of the contribution from the cavity effect to protein-ligand binding free energy.

We present results of the investigation of the cavity creation/annihilation effect in view of formation of the protein-ligand (PL) complexes. The protein and ligand were considered as rigid structures. The change of the cavity creation/annihilation free energy DeltaG(cav) was calculated for three PL complexes using the thermodynamic integration procedure with the original algorithm for growing the interaction potential between the cavity and the water molecules. The thermodynamic cycle consists of two stages, annihilation of the cavity of the ligand for the unbound state and its creation at the active site of the protein (bound state). It was revealed that for all complexes under investigation, the values of DeltaG(cav) are negative and favorable for binding. The main contribution to DeltaG(cav) appears due to the annihilation of the cavity of the ligand. All computations were made using the parallel version of CAVE code, elaborated in our preceding work.

Cavitation free energy for organic molecules having various sizes and shapes.

Cavitation free energy DeltaG(cav), corresponding to the formation of an excluded volume cavity in water, is calculated for a large set of organic molecules employing the thermodynamic integration procedure, which is realized as the original two-step algorithm for growing the interaction potential between the hard cavity wall and the water molecules. A large variety of solute systems is considered. Their characteristic radii change in the range 3-7 A; spherical cavities with radii 3-6 A are also studied. The interaction between water molecules is described by the four-site nonpolarizable TIP4P model. The diversity of the trial molecular set is provided by using a specially formulated nonspherical criterion classifying the cavity shapes according to their deviation from a sphere. Molecular objects were partly taken from the data base NCI Diversity with the aid of this criterion. The so-computed free energies are approximated by the linear volume dependence DeltaG(cav)V = XiV, where V is the cavity volume. This relation works fairly well until the cavity size becomes very large (the effective radius larger than 7 A). The volume dependence valid for solutes of arbitrary shapes can be included in a calculation of the nonpolar free energy component as required in the implicit water model.

Combined Docking with Classical Force Field and Quantum Chemical Semiempirical Method PM7.

Results of the combined use of the classical force field and the recent quantum chemical PM7 method for docking are presented. Initially the gridless docking of a flexible low molecular weight ligand into the rigid target protein is performed with the energy function calculated in the MMFF94 force field with implicit water solvent in the PCM model. Among several hundred thousand local minima, which are found in the docking procedure, about eight thousand lowest energy minima are chosen and then energies of these minima are recalculated with the recent quantum chemical semiempirical PM7 method. This procedure is applied to 16 test complexes with different proteins and ligands. For almost all test complexes such energy recalculation results in the global energy minimum configuration corresponding to the ligand pose near the native ligand position in the crystalized protein-ligand complex. A significant improvement of the ligand positioning accuracy comparing with MMFF94 energy calculations is demonstrated.

Accuracy comparison of several common implicit solvent models and their implementations in the context of protein-ligand binding.

In this study several commonly used implicit solvent models are compared with respect to their accuracy of estimating solvation energies of small molecules and proteins, as well as desolvation penalty in protein-ligand binding. The test set consists of 19 small proteins, 104 small molecules, and 15 protein-ligand complexes. We compared predicted hydration energies of small molecules with their experimental values; the results of the solvation and desolvation energy calculations for small molecules, proteins and protein-ligand complexes in water were also compared with Thermodynamic Integration calculations based on TIP3P water model and Amber12 force field. The following implicit solvent (water) models considered here are: PCM (Polarized Continuum Model implemented in DISOLV and MCBHSOLV programs), GB (Generalized Born method implemented in DISOLV program, S-GB, and GBNSR6 stand-alone version), COSMO (COnductor-like Screening Model implemented in the DISOLV program and the MOPAC package) and the Poisson-Boltzmann model (implemented in the APBS program). Different parameterizations of the molecules were examined: we compared MMFF94 force field, Amber12 force field and the quantum-chemical semi-empirical PM7 method implemented in the MOPAC package. For small molecules, all of the implicit solvent models tested here yield high correlation coefficients (0.87-0.93) between the calculated solvation energies and the experimental values of hydration energies. For small molecules high correlation (0.82-0.97) with the explicit solvent energies is seen as well. On the other hand, estimated protein solvation energies and protein-ligand binding desolvation energies show substantial discrepancy (up to 10kcal/mol) with the explicit solvent reference. The correlation of polar protein solvation energies and protein-ligand desolvation energies with the corresponding explicit solvent results is 0.65-0.99 and 0.76-0.96 respectively, though this difference in correlations is caused more by different parameterization and less by methods and indicates the need for further improvement of implicit solvent models parameterization. Within the same parameterization, various implicit methods give practically the same correlation with results obtained in explicit solvent model for ligands and proteins: e.g. correlation values of polar ligand solvation energies and the corresponding energies in the frame of explicit solvent were 0.953-0.966 for the APBS program, the GBNSR6 program and all models used in the DISOLV program. The DISOLV program proved to be on a par with the other used programs in the case of proteins and ligands solvation energy calculation. However, the solution of the Poisson-Boltzmann equation (APBS program) and Generalized Born method (implemented in the GBNSR6 program) proved to be the most accurate in calculating the desolvation energies of complexes.

[Supercomputer investigation of the protein-ligand system low-energy minima].

The accuracy of the protein-ligand binding energy calculations and ligand positioning is strongly influenced by the choice of the docking target function. This work demonstrates the evaluation of the five different target functions used in docking: functions based on MMFF94 force field and functions based on PM7 quantum-chemical method accounting or without accounting the implicit solvent model (PCM, COSMO or SGB). For these purposes the ligand positions corresponding to the minima of the target function and the experimentally known ligand positions in the protein active site (crystal ligand positions) were compared. Each function was examined on the same test-set of 16 protein-ligand complexes. The new parallelized docking program FLM based on Monte Carlo search algorithm was developed to perform the comprehensive low-energy minima search and to calculate the protein-ligand binding energy. This study demonstrates that the docking target function based on the MMFF94 force field can be used to detect the crystal or near crystal positions of the ligand by the finding the low-energy local minima spectrum of the target function. The importance of solvent accounting in the docking process for the accurate ligand positioning is also shown. The accuracy of the ligand positioning as well as the correlation between the calculated and experimentally determined protein-ligand binding energies are improved when the MMFF94 force field is substituted by the new PM7 method with implicit solvent accounting.

Application of molecular modeling to urokinase inhibitors development.

Urokinase-type plasminogen activator (uPA) plays an important role in the regulation of diverse physiologic and pathologic processes. Experimental research has shown that elevated uPA expression is associated with cancer progression, metastasis, and shortened survival in patients, whereas suppression of proteolytic activity of uPA leads to evident decrease of metastasis. Therefore, uPA has been considered as a promising molecular target for development of anticancer drugs. The present study sets out to develop the new selective uPA inhibitors using computer-aided structural based drug design methods. Investigation involves the following stages: computer modeling of the protein active site, development and validation of computer molecular modeling methods: docking (SOL program), postprocessing (DISCORE program), direct generalized docking (FLM program), and the application of the quantum chemical calculations (MOPAC package), search of uPA inhibitors among molecules from databases of ready-made compounds to find new uPA inhibitors, and design of new chemical structures and their optimization and experimental examination. On the basis of known uPA inhibitors and modeling results, 18 new compounds have been designed, calculated using programs mentioned above, synthesized, and tested in vitro. Eight of them display inhibitory activity and two of them display activity about 10 µM.

Computation of entropy contribution to protein-ligand binding free energy.

The entropy contribution DeltaS to protein-ligand binding free energy is studied for nine protein-lipid complexes. The entropy effect from the loss of the translational/rotational degrees of freedom (DeltaStr) is calculated using the ideal gas approach. The change in the vibrational entropy (DeltaSvib) is calculated using the effective quantum oscillator approach with frequencies derived from the coordinate covariance matrix, so the inharmonic effects are taken into account. The change in the entropy of solvation (DeltaSsolv) is considered using the binomial cell model (developed by the authors) for the hydrophobic effect. The entropy contribution from loss of conformations that are available for the free ligand (DeltaSconf) is also estimated. It is revealed that the negative in view of binding term DeltaStr is only partly compensated by increasing of DeltaSvib, so T(DeltaStr+DeltaSvib+DeltaSconf)<0 for all complexes under investigation, but taking into account DeltaSsolv leads to significantly increased DeltaS. For all complexes except biotin-streptavidin, the results are found to be in reasonable agreement with experimental data.

Excluded volume effect for large and small solutes in water.

The cavitation effect, i.e., the process of the creation of a void of excluded volume in bulk solvent (a cavity), is considered. The cavitation free energy is treated in terms of the information theory (IT) approach [Hummer, G.; Garde, S.; Garcia, A. E.; Paulaitis, M. E.; Pratt, L. R. J. Phys. Chem. B 1998, 102, 10469]. The binomial cell model suggested earlier is applied as the IT default distribution p(m) for the number m of solute (water) particles occupying a cavity of given size and shape. In the present work, this model is extended to cover the entire range of cavity size between small ordinary molecular solutes and bulky biomolecular structures. The resulting distribution consists of two binomial peaks responsible for producing the free energy contributions, which are proportional respectively to the volume and to the surface area of a cavity. The surface peak dominates in the large cavity limit, when the two peaks are well separated. The volume effects become decisive in the opposite limit of small cavities, when the two peaks reduce to a single-peak distribution as considered in our earlier work. With a proper interpolation procedure connecting these two regimes, the MC simulation results for model spherical solutes with radii increasing up to R = 10 A [Huang, D. H.; Geissler, P. L.; Chandler, D. J. Phys. Chem. B 2001, 105, 6704] are well reproduced. The large cavity limit conforms to macroscopic properties of bulk water solvent, such as surface tension, isothermal compressibility and Tolman length. The computations are extended to include nonspherical solutes (hydrocarbons C1-C6).

UV-irradiation-induced structural transformation in phosphosilicate glass fiber.

The Raman spectra of phosphosilicate core (P(2)O(5)-SiO(2)) fibers were investigated. Significant changes in the spectra were observed after UV irradiation of the fibers. An interpretation of the photostructural changes confirmed by computer simulation of phosphorus-related centers is proposed.