Application of the Docking Program SOL for CSAR Benchmark

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.

Design, Synthesis, and Evaluation of New Hybrid Derivatives of 5,6-Dihydro-4H-pyrrolo[3,2,1-ij]quinolin-2(1H)-one as Potential Dual Inhibitors of Blood Coagulation Factors Xa and XIa

Cardiovascular diseases caused by blood coagulation system disorders are one of the leading causes of morbidity and mortality in the world. Research shows that blood clotting factors are involved in these thrombotic processes. Among them, factor Xa occupies a key position in the blood coagulation cascade. Another coagulation factor, XIa, is also a promising target because its inhibition can suppress thrombosis with a limited contribution to normal hemostasis. In this regard, the development of dual inhibitors as new generation anticoagulants is an urgent problem. Here we report the synthesis and evaluation of novel potential dual inhibitors of coagulation factors Xa and XIa. Based on the principles of molecular design, we selected a series of compounds that combine in their structure fragments of pyrrolo[3,2,1-ij]quinolin-2-one and thiazole, connected through a hydrazine linker. The production of new hybrid molecules was carried out using a two-stage method. The reaction of 5,6-dihydropyrrolo[3,2,1-ij]quinoline-1,2-diones with thiosemicarbazide gave the corresponding hydrazinocarbothioamides. The reaction of the latter with DMAD led to the target methyl 2-(4-oxo-2-(2-(2-oxo-5,6-dihydro-4H-pyrrolo[3,2,1-ij]quinolin-1(2H)-ylidene)hydrazineyl)thiazol-5(4H)-ylidene)acetates in high yields. In vitro testing of the synthesized molecules revealed that ten of them showed high inhibition values for both the coagulation factors Xa and XIa, and the IC50 value for some compounds was also assessed. The resulting structures were also tested for their ability to inhibit thrombin.

New Hybrid Tetrahydropyrrolo[3,2,1-ij]quinolin-1-ylidene-2-thioxothiazolidin-4-ones as New Inhibitors of Factor Xa and Factor XIa: Design, Synthesis, and In Silico and Experimental Evaluation

Despite extensive research in the field of thrombotic diseases, the prevention of blood clots remains an important area of study. Therefore, the development of new anticoagulant drugs with better therapeutic profiles and fewer side effects to combat thrombus formation is still needed. Herein, we report the synthesis and evaluation of novel pyrroloquinolinedione-based rhodanine derivatives, which were chosen from 24 developed derivatives by docking as potential molecules to inhibit the clotting factors Xa and XIa. For the synthesis of new hybrid derivatives of pyrrolo[3,2,1-ij]quinoline-2-one, we used a convenient structural modification of the tetrahydroquinoline fragment by varying the substituents in positions 2, 4, and 6. In addition, the design of target molecules was achieved by alkylating the amino group of the rhodanine fragment with propargyl bromide or by replacing the rhodanine fragment with 2-thioxoimidazolidin-4-one. The in vitro testing showed that eight derivatives are capable of inhibiting both coagulation factors, two compounds are selective inhibitors of factor Xa, and two compounds are selective inhibitors of factor XIa. Overall, these data indicate the potential anticoagulant activity of these molecules through the inhibition of the coagulation factors Xa and XIa.

Experimentally Validated Novel Factor XIIa Inhibitors Identified by Docking and Quantum Chemical Post-processing

Antithrombotic agents based on factor XIIa inhibitors can become a new class of drugs to manage conditions associated with thrombosis. Herein, we report identification of two novel classes of factor XIIa inhibitors. The first one is triazolopyrimidine derivatives designed on the basis of the literature aminotriazole hit and identified using virtual screening of the focused library. The second class is a spirocyclic furo[3,4-c]pyrrole derivatives identified by virtual screening of a large chemical library of drug-like compounds performed in a previous study but confirmed in vitro here. In both cases, the prediction of inhibitory activity is based on the score of the SOL docking program, which uses the MMFF94 force field to calculate the binding energy. For the best ligands selected in virtual screening of the large chemical library, postprocessing with the PM7 semiempirical quantum-chemical method was used to calculate the enthalpy of protein-ligand binding to prioritize 16 compounds for testing in enzymatic assay, and one of them demonstrated micromolar activity. For triazolopyrimidine library, 21 compounds were prioritized for the testing based on docking scores, and visual inspection of docking poses. Of these, 4 compounds showed inhibition of factor XIIa at 30 μM.

New Chemicals Suppressing SARS-CoV-2 Replication in Cell Culture

Candidates to being inhibitors of the main protease (Mpro) of SARS-CoV-2 were selected from the database of Voronezh State University using molecular modeling. The database contained approximately 19,000 compounds represented by more than 41,000 ligand conformers. These ligands were docked into Mpro using the SOL docking program. For one thousand ligands with best values of the SOL score, the protein–ligand binding enthalpy was calculated by the PM7 quantum-chemical method with the COSMO solvent model. Using the SOL score and the calculated protein–ligand binding enthalpies, eighteen compounds were selected for the experiments. Several of these inhibitors suppressed the replication of the coronavirus in cell culture, and we used the best three among them in the search for chemical analogs. Selection among analogs using the same procedure followed by experiments led to identification of seven inhibitors of the SARS-CoV-2 replication in cell culture with EC50 values at the micromolar level. The identified inhibitors belong to three chemical classes. The three inhibitors, 4,4-dimethyldithioquinoline derivatives, inhibit SARS-CoV-2 replication in Vero E6 cell culture just as effectively as the best published non-covalent inhibitors, and show low cytotoxicity. These results open up a possibility to develop antiviral drugs against the SARS-CoV-2 coronavirus.

Novel Inhibitors of 2′-O-Methyltransferase of the SARS-CoV-2 Coronavirus

The COVID-19 pandemic is still affecting many people worldwide and causing a heavy burden to global health. To eliminate the disease, SARS-CoV-2, the virus responsible for the pandemic, can be targeted in several ways. One of them is to inhibit the 2′-O-methyltransferase (nsp16) enzyme that is crucial for effective translation of viral RNA and virus replication. For methylation of substrates, nsp16 utilizes S-adenosyl methionine (SAM). Binding of a small molecule in the protein site where SAM binds can disrupt the synthesis of viral proteins and, as a result, the replication of the virus. Here, we performed high-throughput docking into the SAM-binding site of nsp16 for almost 40 thousand structures, prepared for compounds from three libraries: Enamine Coronavirus Library, Enamine Nucleoside Mimetics Library, and Chemdiv Nucleoside Analogue Library. For the top scoring ligands, semi-empirical quantum-chemical calculations were performed, to better estimate protein–ligand binding enthalpy. Relying upon the calculated binding energies and predicted docking poses, we selected 21 compounds for experimental testing.

New Blood Coagulation Factor XIIa Inhibitors: Molecular Modeling, Synthesis, and Experimental Confirmation

In the modern world, complications caused by disorders in the blood coagulation system are found in almost all areas of medicine. Thus, the development of new, more advanced drugs that can prevent pathological conditions without disrupting normal hemostasis is an urgent task. The blood coagulation factor XIIa is one of the most promising therapeutic targets for the development of anticoagulants based on its inhibitors. The initial stage of drug development is directly related to computational methods of searching for a lead compound. In this study, docking followed by quantum chemical calculations was used to search for noncovalent low-molecular-weight factor XIIa inhibitors in a focused library of druglike compounds. As a result of the study, four low-molecular-weight compounds were experimentally confirmed as factor XIIa inhibitors. Selectivity testing revealed that two of the identified factor XIIa inhibitors were selective over the coagulation factors Xa and XIa.

[Development of antiviral drugs based on inhibitors of the SARS-COV-2 main protease]

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.

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.

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.

Synthesis, Docking, and In Vitro Anticoagulant Activity Assay of Hybrid Derivatives of Pyrrolo[3,2,1-ij]Quinolin-2(1H)-one as New Inhibitors of Factor Xa and Factor XIa

Coagulation factor Xa and factor XIa are proven to be convenient and crucial protein targets for treatment for thrombotic disorders and thereby their inhibitors can serve as effective anticoagulant drugs. In the present work, we focused on the structure–activity relationships of derivatives of pyrrolo[3,2,1-ij]quinolin-2(1H)-one and an evaluation of their activity against factor Xa and factor XIa. For this, docking-guided synthesis of nine compounds based on pyrrolo[3,2,1-ij]quinolin-2(1H)-one was carried out. For the synthesis of new hybrid hydropyrrolo[3,2,1-ij]quinolin-2(1H)-one derivatives, we used convenient structural modification of both the tetrahydro- and dihydroquinoline moiety by varying the substituents at the C^6,8,9 positions. In vitro testing revealed that four derivatives were able to inhibit both coagulation factors and three compounds were selective factor XIa inhibitors. An IC₅₀ value of 3.68 μM for was found for the best factor Xa inhibitor and 2 μM for the best factor XIa inhibitor.

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.

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.

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.

Evaluation of the novel algorithm of flexible ligand docking with moveable target-protein atoms

We present the novel docking algorithm based on the Tensor Train decomposition and the TT-Cross global optimization. The algorithm is applied to the docking problem with flexible ligand and moveable protein atoms. The energy of the protein-ligand complex is calculated in the frame of the MMFF94 force field in vacuum. The grid of precalculated energy potentials of probe ligand atoms in the field of the target protein atoms is not used. The energy of the protein-ligand complex for any given configuration is computed directly with the MMFF94 force field without any fitting parameters. The conformation space of the system coordinates is formed by translations and rotations of the ligand as a whole, by the ligand torsions and also by Cartesian coordinates of the selected target protein atoms. Mobility of protein and ligand atoms is taken into account in the docking process simultaneously and equally. The algorithm is realized in the novel parallel docking SOL-P program and results of its performance for a set of 30 protein-ligand complexes are presented. Dependence of the docking positioning accuracy is investigated as a function of parameters of the docking algorithm and the number of protein moveable atoms. It is shown that mobility of the protein atoms improves docking positioning accuracy. The SOL-P program is able to perform docking of a flexible ligand into the active site of the target protein with several dozens of protein moveable atoms: the native crystallized ligand pose is correctly found as the global energy minimum in the search space with 157 dimensions using 4700 CPU ∗ h at the Lomonosov supercomputer.

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.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima

The adequate choice of the docking target function impacts the accuracy of the ligand positioning as well as the accuracy of the protein-ligand binding energy calculation. To evaluate a docking target function we compared positions of its minima with the experimentally known pose of the ligand in the protein active site. We evaluated five docking target functions based on either the MMFF94 force field or the PM7 quantum-chemical method with or without implicit solvent models: PCM, COSMO, and SGB. Each function was tested on the same set of 16 protein-ligand complexes. For exhaustive low-energy minima search the novel MPI parallelized docking program FLM and large supercomputer resources were used. Protein-ligand binding energies calculated using low-energy minima were compared with experimental values. It was demonstrated that the docking target function on the base of the MMFF94 force field in vacuo can be used for discovery of native or near native ligand positions by finding the low-energy local minima spectrum of the target function. The importance of solute-solvent interaction for the correct ligand positioning is demonstrated. It is shown that docking accuracy can be improved by replacement of the MMFF94 force field by the new semiempirical quantum-chemical PM7 method.

Application of Molecular Modeling to Development of New Factor Xa Inhibitors

In consequence of the key role of factor Xa in the clotting cascade and absence of its activity in the processes that do not affect coagulation, this protein is an attractive target for development of new blood coagulation inhibitors. Factor Xa is more effective and convenient target for creation of anticoagulants than thrombin, inhibition of which may cause some side effects. This study is aimed at finding new inhibitors of factor Xa by molecular computer modeling including docking SOL and postdocking optimization DISCORE programs. After validation of molecular modeling methods on well-known factor Xa inhibitors the virtual screening of NCI Diversity and Voronezh State University databases of ready-made low molecular weight species has been carried out. Seventeen compounds selected on the basis of modeling results have been tested experimentally in vitro. It has been found that 12 of them showed activity against factor Xa (IC50 = 1.8-40 μM). Based on analysis of the results, the new original compound was synthesized and experimentally verified. It shows activity against factor Xa with IC50 value of 0.7 μM.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

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.

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.