New generation of docking programs: Supercomputer validation of force fields and quantum-chemical methods for docking

Thwarting resistance: MgrA inhibition with methylophiopogonanone a unveils a new battlefront against S. aureus

Limitations in the clinical treatment of Staphylococcus aureus (S. aureus) infections have arisen due to the advent of antibiotic-resistant strains. Given the immense potential of therapeutic strategies targeting bacterial virulence, the role of MgrA as a pivotal virulence determinant in S. aureus-orchestrating resistance, adherence, and hundreds of virulence targets-becomes indispensable. In this investigation, leveraging advanced virtual screening and fluorescence anisotropy assays, we discerned methylophiopogonanone A (Mo-A), a flavonoid derivative, as a potent disruptor of the MgrA-DNA interaction nexus. Subsequent analysis revealed that Mo-A effectively inhibits the expression of virulence factors such as Hla and Pvl in S. aureus and markedly reduces its adhesion capability to fibrinogen. On a cellular landscape, Mo-A exerts a mitigating influence on the deleterious effects inflicted by S. aureus USA300 on A549 cells. Furthermore, our data indicate that Mo-A downregulates the transcription of genes associated with immune evasion, such as nucleases (nuc), Staphylococcal Chemotaxis Inhibitory Protein (chips), and Staphylococcal Complement Inhibitor (scin), thereby undermining immune escape and amplifying neutrophil chemotaxis. Upon application in an in vivo setting, Mo-A assumes a protective persona in a murine model of S. aureus USA300-induced pneumonia and demonstrates efficacy in the Galleria mellonella infection model. Of note, S. aureus displayed no swift acquisition of resistance to Mo-A, and the effect was synergistically enhanced when used in combination with vancomycin. Our findings add substantive weight to the expanding field of virulence-targeted therapeutic strategies and set the stage for more comprehensive exploration of Mo-A potential in combating antibiotic-resistant S. aureus.

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.

Identification of Novel Protein Targets of Prodigiosin for Breast Cancer Using Inverse Virtual Screening Methods

Prodigiosin (PG) is chemically formulated as 4-methoxy-5-[(5-methyl-4-pentyl-2H-pyrrol-2ylidene)methyl]-2,2'-bi-1H-pyrrole and it is an apoptotic agent. Only a few protein targets for PG have been identified so far for regulating various diseases; nevertheless, finding more PG targets is crucial for novel drug discovery research. A bioinformatics method was applied in this work to find additional potential PG targets. Initially, a text mining analysis was conducted to determine the relationship between PG and a variety of metabolic processes. One hundred sixteen proteins from the KEGG pathway were selected for the docking study. Inverse virtual screening was performed by Discovery Studio software 4.1 using CHARMm-based docking tool. Twelve proteins are screened out of 116 because their CDOCKER interaction energy is larger than - 40.22 kcal/mol. The best docking score with PG was reported to be - 44.25 kcal/mol, - 44.99 kcal/mol, and - 40.91 kcal/mol for three novel proteins, such as human epidermal growth factor-2 (HER-2), mitogen-activated protein kinase (MEK), and S6 kinase protein (S6K) respectively. The interactions in the S6K/PG complex are predominantly hydrophobic; however, hydrogen bond interactions can be identified in the MEK/PG and HER-2/PG complexes. The root-mean-square deviation (RMSD) and key interaction score system (KISS) were further used to validate the docking approach. The docking approach employed in this work has a low RMSD value (2.44 Å) and a high KISS score (0.5), indicating that it is significant.

The Advances and Limitations of the Determination and Applications of Water Structure in Molecular Engineering

Water is a key actor of various processes of nature and, therefore, molecular engineering has to take the structural and energetic consequences of hydration into account. While the present review focuses on the target-ligand interactions in drug design, with a focus on biomolecules, these methods and applications can be easily adapted to other fields of the molecular engineering of molecular complexes, including solid hydrates. The review starts with the problems and solutions of the determination of water structures. The experimental approaches and theoretical calculations are summarized, including conceptual classifications. The implementations and applications of water models are featured for the calculation of the binding thermodynamics and computational ligand docking. It is concluded that theoretical approaches not only reproduce or complete experimental water structures, but also provide key information on the contribution of individual water molecules and are indispensable tools in molecular engineering.

Natural coumarins from Murraya paniculata as mixed-type inhibitors of cholinesterases: In vitro and in silico investigations

Currently, acetylcholinesterase (AChE) inhibiting drugs in clinical use, such as tacrine, donepezil, rivastigmine, and galanthamine, are associated with serious side effects and short half-lives. In recent years, numerous phytochemicals have been identified as inhibitors of cholinesterases with potential applications in the management of Alzheimer’s disease (AD). In this study three natural coumarins, 2′-O-ethylmurrangatin (1), murranganone (2), and paniculatin (3) isolated previously by our group from the leaves of Murraya paniculata, were tested against the two cholinesterases (ChE) enzymes, AChE and butyrylcholinesterase (BChE) using in vitro assay. Molecular docking was performed to highlight the structural properties that contribute to the molecular recognition pattern in the inhibition of ChE and the structural differences resulting in the selectivity of these compounds toward AChE. Classical enzyme inhibition kinetics data suggested that compounds 2 and 3 were potent inhibitors of AChE and BChE, while 1 was found inactive against both enzymes. The findings from molecular docking studies revealed the competitive and non-competitive inhibition mechanisms of compounds 2 and 3 against both enzymes. Molecular docking and simulations have revealed that hydrogen bonding, mediated by ketone and hydroxyl functionalities in various positions, significantly contributes to the binding of the inhibitor to the receptor. According to MD simulation studies, the stability of the ligand-AChE complex for the most active compound (3) is found to be comparable to that of the widely used drug Tacrine. In addition, to evaluate the drug-likeness of compounds, in silico ADME evaluation was performed, and the compounds presented good ADME profiles. Data suggested that the coumarin nucleus having diverse side chains at the C-8 position can serve as a potential inhibitor of cholinesterases and can act as a lead to develop a new semisynthetic drug for the treatment of AD.

Development of Novel Ecto-Nucleotide Pyrophosphatase/Phosphodiesterase 1 (ENPP1) Inhibitors for Tumor Immunotherapy

The cyclic guanosine monophosphate–adenosine monophosphate synthase–stimulator of interferon genes–TANK-binding kinase 1–interferon regulating factor 3 (cGAS-STING-TBK1-IRF3) axis is now acknowledged as the major signaling pathway in innate immune responses. However, 2′,3′-cGAMP as a STING stimulator is easily recognized and degraded by ecto-nucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), which reduces the effect of tumor immunotherapy and promotes metastatic progression. In this investigation, the structure-based virtual screening strategy was adopted to discover eight candidate compounds containing zinc-binding quinazolin-4(3H)-one scaffold as ENPP1 inhibitors. Subsequently, these novel inhibitors targeting ENPP1 were synthesized and characterized by NMR and high-resolution mass spectra (HRMS). In bioassays, 7-fluoro-2-(((5-methoxy-1H-imidazo[4,5-b]pyridin-2-yl)thio)methyl)quina-zolin-4(3H)-one(compound 4e) showed excellent activity against the ENPP1 at the molecular and cellular levels, with IC₅₀ values of 0.188 μM and 0.732 μM, respectively. Additionally, compound 4e had superior selectivity towards metastatic breast cancer cells (4T1) than towards normal cells (LO2 and 293T) in comparison with cisplatin, indicating that compound 4e can potentially be used in metastatic breast cancer therapy. On the other hand, compound 4e upgraded the expression levels of IFN-β in vivo by preventing the ENPP1 from hydrolyzing the cGAMP to stimulate a more potent innate immune response. Therefore, this compound might be applied to boost antitumor immunity for cancer immunotherapy. Overall, our work provides a strategy for the development of a promising drug candidate targeting ENPP1 for tumor immunotherapy.

Quantum-Chemical Quasi-Docking for Molecular Dynamics Calculations

The quantum quasi-docking procedure is used to compare the docking accuracies of two quantum-chemical semiempirical methods, namely, PM6-D3H4X and PM7. Quantum quasi-docking is an approximation to quantum docking. In quantum docking, it is necessary to search directly for the global minimum of the energy of the protein-ligand complex calculated by the quantum-chemical method. In quantum quasi-docking, firstly, we look for a wide spectrum of low-energy minima, calculated using the MMFF94 force field, and secondly, we recalculate the energies of all these minima using the quantum-chemical method, and among these recalculated energies we determine the lowest energy and the corresponding ligand position. Both PM6-D3H4X and PM7 are novel methods that describe well-dispersion interactions, hydrogen and halogen bonds. The PM6-D3H4X and PM7 methods are used with the COSMO implicit solvent model as it is implemented in the MOPAC program. The comparison is made for 25 high quality protein-ligand complexes. Firstly, the docking positioning accuracies have been compared, and we demonstrated that PM7+COSMO provides better positioning accuracy than PM6-D3H4X. Secondly, we found that PM7+COSMO demonstrates a much higher correlation between the calculated and measured protein-ligand binding enthalpies than PM6-D3H4X. For future quantum docking PM7+COSMO is preferable, but the COSMO model must be improved.

Effects of Baicalein and Chrysin on the Structure and Functional Properties of β-Lactoglobulin

Two flavonoids with similar structures, baicalein (Bai) and chrysin (Chr), were selected to investigate the interactions with β-lactoglobulin (BLG) and the influences on the structure and functional properties of BLG by multispectral methods combined with molecular docking and dynamic (MD) simulation techniques. The results of fluorescence quenching suggested that both Bai and Chr interacted with BLG to form complexes with the binding constant of the magnitude of 10⁵ L·mol⁻¹. The binding affinity between BLG and Bai was stronger than that of Chr due to more hydrogen bond formation in Bai–BLG binding. The existence of Bai or Chr induced a looser conformation of BLG, but Chr had a greater effect on the secondary structure of BLG. The surface hydrophobicity and free sulfhydryl group content of BLG lessened due to the presence of the two flavonoids. Molecular docking was performed at the binding site of Bai or Chr located in the surface of BLG, and hydrophobic interaction and hydrogen bond actuated the formation of the Bai/Chr–BLG complex. Molecular dynamics simulation verified that the combination of Chr and BLG decreased the stability of BLG, while Bai had little effect on it. Moreover, the foaming properties of BLG got better in the presence of the two flavonoids compounds and Bai improved its emulsification stability of the protein, but Chr had the opposite effect. This work provides a new idea for the development of novel dietary supplements using functional proteins as flavonoid delivery vectors.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Structure-based virtual screening, molecular dynamics and binding affinity calculations of some potential phytocompounds against SARS-CoV-2

COVID-19 caused by a positive-sense single stranded RNA virus named as severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) triggered the global pandemic. This virus has infected about 10.37 Crores and taken lives of 2.24 Crores people of 213 countries to date. To cope-up this emergency clinical trials are undergoing with some existing drugs like remdesivir, flavipiravir, lopinavir-ritonavir, nafamostat, doxycycline, hydroxy-chloroquine, dexamethasone, etc., despite their severe toxicity and health hazards among diabetics, hypertensive, cardiac patients or normal individuals. The lack of safe and approved treatment for COVID-19 has forced the scientific community to find novel and safe compounds with potential efficacy. This study evaluates a few selective herbal compounds like glucoraphanin, vitexin, niazinin, etc., as a potential inhibitor of the spike protein and 3-chymotrypsin-like protease (3CLpro) or main protease (Mpro) of SARS-COV-2 through in-silico virtual studies such as molecular docking, target analysis, toxicity prediction and ADME prediction and supported by a Molecular-Dynamic simulation. Selective phytocompounds were docked successfully in the binding site of spike glycoprotein and 3CLpro (Mpro) of SARS-CoV-2. In-silico approaches also predict this molecule to have good solubility, pharmacodynamic property and target accuracy through MD simulation and ADME studies. These hit molecules niazinin, vitexin, glucoraphanin also obey Lipinski's rule along with their stable binding towards target protein of the virus, which makes them suitable for further biochemical and cell-based assays followed by clinical investigations to highlight their potential use in COVID-19 treatment.Communicated by Ramaswamy H. Sarma.

Supercomputing, Docking and Quantum Mechanics in Quest for Inhibitors of Papain-like Protease of SARS-CoV-2

Lomonosov-2 supercomputer is used to search for new organic compounds that can suppress the replication of the SARS-CoV-2 coronavirus. The latter is responsible for the COVID-19 pandemic. Docking and a quantum-chemical semiempirical atomistic modeling method are used to find inhibitors of the SARS-CoV-2 papain-like protease, which is one of the key coronavirus enzymes responsible for its replication. The atomistic model of the papain-like protease of this coronavirus is based on the high-resolution structure deposited in the Protein Data Bank. The SOL docking program has been used for virtual screening of more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40000$$\end{document} low molecular weight molecules (ligands). Ligands with the highest protein-ligand binding energy, selected using the docking results, were subjected to quantum-chemical calculations. The latters are performed by the PM7 semiempirical method with the COSMO implicit solvent model using the MOPAC program. The enthalpy of protein-ligand binding is calculated for the best position of the ligand in the protein. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$19$$\end{document} ligands were selected for experimental in vitro testing as candidates for papain-like protease inhibitors base on docking and quantum-chemical results. In case of experimental confirmation, these compounds may become the basis for direct-acting antiviral drugs for the SARS-CoV-2 coronavirus.

Partial Least-Squares Discriminant Analysis and Ensemble-Based Flexible Docking of PD-1/PD-L1 Inhibitors: A Pilot Study

Although mAbs targeting the programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) pathway have achieved remarkable therapeutic potential against multiple types of cancer, it is still of great interest for researchers to develop small-molecule PD-1/PD-L1 inhibitors without the mAb-related disadvantages of no oral bioavailability and poor solid tumor penetration. However, targeting the PD-1/PD-L1 pathway with small molecules is normally considered challenging because of the flat and large interaction surface of the PD-1/PD-L1 complex. In this paper, a total of 2558 PD-1/PD-L1 inhibitors were compiled from recent patents and literatures and then used for exploring the chemical space and structural features of PD-1/PD-L1 inhibitors by partial least-squares discriminant analysis. The results showed that intramolecular H bond, amphotericity indices, radius of gyration, nonbond electrostatic energy, fractional van der Waals surface area of H-bond donors, octanol-water partition coefficient, and molecular weight are the seven key features discriminating the PD-1/PD-L1 inhibitors from noninhibitors, with the prediction accuracy larger than 0.90. Based on the seven crystal structures of the PD-L1 dimer complexed with the patent Bristol Myers Squibb (BMS) inhibitors, the feasibility of molecular docking for this unconventional binding pocket was further investigated. The results showed that the ensemble-based flexible docking protocol can reproduce the near-native binding conformations of the BMS inhibitors with a strong correlation between the IC₅₀ values and ligand-receptor interaction energies (R = 0.81). In general, this paper delineates, for the first time, the characteristic features of the PD-1/PD-L1 inhibitors as well as a high-quality flexible docking strategy for the unconventional binding pocket of the PD-L1 dimer.

In silico Drug Repurposing for COVID-19: Targeting SARS-CoV-2 Proteins through Docking and Consensus Ranking

In December 2019, an infectious disease caused by the coronavirus SARS-CoV-2 appeared in Wuhan, China. This disease (COVID-19) spread rapidly worldwide, and on March 2020 was declared a pandemic by the World Health Organization (WHO). Today, over 21 million people have been infected, with more than 750.000 casualties. Today, no vaccine or antiviral drug is available. While the development of a vaccine might take at least a year, and for a novel drug, even longer; finding a new use to an old drug (drug repurposing) could be the most effective strategy. We present a docking-based screening using a quantum mechanical scoring of a library built from approved drugs and compounds undergoing clinical trials, against three SARS-CoV-2 target proteins: the spike or S-protein, and two proteases, the main protease and the papain-like protease. The S-protein binds directly to the Angiotensin Converting Enzyme 2 receptor of the human host cell surface, while the two proteases process viral polyproteins. Following the analysis of our structure-based compound screening, we propose several structurally diverse compounds (either FDA-approved or in clinical trials) that could display antiviral activity against SARS-CoV-2. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against the disease. We hope that these findings may contribute to the rational drug design against COVID-19.

High-Throughput Docking Using Quantum Mechanical Scoring

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

Advances in Docking

BACKGROUND

Design of small molecules which are able to bind to the protein responsible for a disease is the key step of the entire process of the new medicine discovery. Atomistic computer modeling can significantly improve effectiveness of such design. The accurate calculation of the free energy of binding a small molecule (a ligand) to the target protein is the most important problem of such modeling. Docking is one of the most popular molecular modeling methods for finding ligand binding poses in the target protein and calculating the protein-ligand binding energy. This energy is used for finding the most active compounds for the given target protein. This short review aims to give a concise description of distinctive features of docking programs focusing on computation methods and approximations influencing their accuracy.

METHODS

This review is based on the peer-reviewed research literature including author's own publications. The main features of several representative docking programs are briefly described focusing on their characteristics influencing docking accuracy: force fields, energy calculations, solvent models, algorithms of the best ligand pose search, global and local optimizations, ligand and target protein flexibility, and the simplifications made for the docking accelerating. Apart from other recent reviews focused mainly on the performance of different docking programs, in this work, an attempt is made to extract the most important functional characteristics defining the docking accuracy. Also a roadmap for increasing the docking accuracy is proposed. This is based on the new generation of docking programs which have been realized recently. These programs and respective new global optimization algorithms are described shortly.

RESULTS

Several popular conventional docking programs are considered. Their search of the best ligand pose is based explicitly or implicitly on the global optimization problem. Several algorithms are used to solve this problem, and among them, the heuristic genetic algorithm is distinguished by its popularity and an elaborate design. All conventional docking programs for their acceleration use the preliminary calculated grids of protein-ligand interaction potentials or preferable points of protein and ligand conjugation. These approaches and commonly used fitting parameters restrict strongly the docking accuracy. Solvent is considered in exceedingly simplified approaches in the course of the global optimization and the search for the best ligand poses. More accurate approaches on the base of implicit solvent models are used frequently for more careful binding energy calculations after docking. The new generation of docking programs are developed recently. They find the spectrum of low energy minima of a protein-ligand complex including the global minimum. These programs should be more accurate because they do not use a preliminary calculated grid of protein-ligand interaction potentials and other simplifications, the energy of any conformation of the molecular system is calculated in the frame of a given force field and there are no fitting parameters. A new docking algorithm is developed and fulfilled specially for the new docking programs. This algorithm allows docking a flexible ligand into a flexible protein with several dozen mobile atoms on the base of the global energy minimum search. Such docking results in improving the accuracy of ligand positioning in the docking process. The adequate choice of the method of molecular energy calculations also results in the better docking positioning accuracy. An advancement in the application of quantum chemistry methods to docking and scoring is revealed.

CONCLUSION

The findings of this review confirm the great demand in docking programs for discovery of new medicine substances with the help of molecular modeling. New trends in docking programs design are revealed. These trends are focused on the increase of the docking accuracy at the expense of more accurate molecular energy calculations without any fitting parameters, including quantum-chemical methods and implicit solvent models, and by using new global optimization algorithms which make it possible to treat flexibility of ligands and mobility of protein atoms simultaneously. Finally, it is shown that all the necessary prerequisites for increasing the docking accuracy can be accomplished in practice.

Docking-based inverse virtual screening strategy for identification of novel protein targets for triclosan

Triclosan (TCS) is chemically designated as 5-chloro-2-(2,4-dichlorophenoxy) phenol and is considered as endocrine-disrupting chemical (EDC). The various diseases found due to exposure of TCS, have been linked with modulation of the human enoyl-acyl carrier protein-reductase (hER). However, the new protein targets for TCS other than hER, which are responsible for various diseases, are still unknown. In the present study, a bioinformatics approach was used to identify new possible targets for TCS. A text mining study was initially performed to understand the association of TCS in various biochemical processes. Discovery studio software 4.1 was used to carry out inverse virtual screening for 226 numbers of pathway proteins by docking study using CHARMm based docking tool, and twenty proteins were screened. CDOCKER energy values lower than -12.65 kcal/mol was considered for the screening of selected proteins. Three new proteins; Receptor-interacting protein 1 (RIP1), Apoptosis signal-regulating kinase 1 (ASK1) and B-cell lymphoma 2 (Bcl-2) from Apoptosis Signaling Pathway revealed best CDOCKER energy with triclosan which was -26.88, -23.34 and -22.96 kcal/mol respectively. The interaction of TCS with RIP1 and ASK1 were mostly hydrophobic; however, hydrogen bond type interaction was found in TCS/Bcl2 complex. Therefore, docking-based inverse virtual screening study suggests that TCS has other targets rather than hER, which can modulate various biochemical processes. The docking protocol was validated through evaluation of root-mean-square deviation (RMSD), key interaction score system (KISS) and the relationship between the docking energy and toxicity data available in ToxCast database. Low RMSD value (0.55 ˚A) and high KISS score (0.66) along with higher correlation (R² = 0.9798) between docking affinity and toxicity indicated that docking protocol can be used to optimize the binding energetics.

Supercomputer docking with a large number of degrees of freedom

Docking represents one of the most popular computational approaches in drug design. It has reached popularity owing to capability of identifying correct conformations of a ligand within an active site of the target-protein and of estimating the binding affinity of a ligand that is immensely helpful in prediction of compound activity. Despite many success stories, there are challenges, in particular, handling with a large number of degrees of freedom in solving the docking problem. Here, we show that SOL-P, the docking program based on the new Tensor Train algorithm, is capable to dock successfully oligopeptides having up to 25 torsions. To make the study comparative we have performed docking of the same oligopeptides with the SOL program which uses the same force field as that utilized by SOL-P and has common features of many docking programs: the genetic algorithm of the global optimization and the grid approximation. SOL has managed to dock only one oligopeptide. Moreover, we present the results of docking with SOL-P ligands into proteins with moveable atoms. Relying on visual observations we have determined the common protein atom groups displaced after docking which seem to be crucial for successful prediction of experimental conformations of ligands.

Computer Design of Low-Molecular-Weight Inhibitors of Coagulation Factors

The review discusses main approaches to searching for new low-molecular-weight inhibitors of coagulation factors IIa, Xa, IXa, and XIa and the results of such studies conducted from 2015 to 2018. For each of these factors, several inhibitors with IC₅₀ < 10 nM have been found, some of which are now tested in clinical trials. However, none of the identified inhibitors meets the requirements for an "ideal" anticoagulant, so further studies are required.

[Docking with combined use of a force field and a quantum-chemical method]

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.

New factor Xa inhibitors based on 1,2,3,4-tetrahydroquinoline developed by molecular modelling

Factor Xa is a serine protease representing a crucial element in the coagulation process and an attractive target for anticoagulant therapy. At the present time there are several chemical classes of factor Xa inhibitors with proven activity. Furthermore, three factor Xa inhibitors have been approved for the medical use to date. However, therapy with these medications is accompanied by substantial adverse effects. In this background, the structure-based computational approach combining molecular docking and semiempirical quantum chemical calculations was applied for a search for new effective factor Xa inhibitors. We have undertaken a few virtual screening procedures to select potential candidates for synthesis and subsequent testing. The first screen of the focused library resulted in identifying 20 compounds among which 7 compounds showed the noticeable inhibition of factor Xa at maximal concentrations, allowed by solubility. The subsequent additional screens identified 20 additional candidates. Of these, 5 substances were shown to be capable of inhibiting factor Xa at 5 μM. The best two found 1,2,3,4-tetrahydroquinoline derivatives identified by means of modelling have demonstrated IC50 values in the micromolar range. One of them turned out to be selective factor Xa inhibitor over trypsin, factors IIa, IXa and XIa.

Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

We present two new semiempirical quantum-chemical methods with orthogonalization and dispersion corrections: ODM2 and ODM3 (ODM x). They employ the same electronic structure model as the OM2 and OM3 (OM x) methods, respectively. In addition, they include Grimme's dispersion correction D3 with Becke-Johnson damping and three-body corrections E ABC for Axilrod-Teller-Muto dispersion interactions as integral parts. Heats of formation are determined by adding explicitly computed zero-point vibrational energy and thermal corrections, in contrast to standard MNDO-type and OM x methods. We report ODM x parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine that are optimized with regard to a wide range of carefully chosen state-of-the-art reference data. Extensive benchmarks show that the ODM x methods generally perform better than the available MNDO-type and OM x methods for ground-state and excited-state properties, while they describe noncovalent interactions with similar accuracy as OM x methods with a posteriori dispersion corrections.