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

Immunoinformatic based designing of potential immunogenic novel mRNA and peptide-based prophylactic vaccines against H5N1 and H7N9 avian influenza viruses

Influenza viruses are the most common cause of serious respiratory illnesses worldwide and are responsible for a significant number of annual fatalities. Therefore, it is crucial to look for new immunogenic sites that might trigger an effective immune response. In the present study, bioinformatics tools were used to design mRNA and multiepitope-based vaccines against H5N1 and H7N9 subtypes of avian influenza viruses. Several Immunoinformatic tools were employed to extrapolate T and B lymphocyte epitopes of HA and NA proteins of both subtypes. The molecular docking approach was used to dock the selected HTL and CTL epitopes with the corresponding MHC molecules. Eight (8) CTL, four (4) HTL, and Six (6) linear B cell epitopes were chosen for the structural arrangement of mRNA and of peptide-based prophylactic vaccine designs. Different physicochemical characteristics of the selected epitopes fitted with suitable linkers were analyzed. High antigenic, non-toxic, and non-allergenic features of the designed vaccines were noted at a neutral physiological pH. Codon optimization tool was used to check the GC content and CAI value of constructed MEVC-Flu vaccine, which were recorded to be 50.42% and 0.97 respectively. the GC content and CAI value verify the stable expression of vaccine in pET28a + vector. In-silico immunological simulation the MEVC-Flu vaccine construct revealed a high level of immune responses. The molecular dynamics simulation and docking results confirmed the stable interaction of TLR-8 and MEVC-Flu vaccine. Based on these parameters, vaccine constructs can be regarded as an optimistic choice against H5N1 and H7N9 strains of the influenza virus. Further experimental testing of these prophylactic vaccine designs against pathogenic avian influenza strains may clarify their safety and efficacy.Communicated by Ramaswamy H. Sarma.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

An in-silico pharmacophore-based molecular docking study to evaluate the inhibitory potentials of novel fungal triterpenoid Astrakurkurone analogues against a hypothetical mutated main protease of SARS-CoV-2 virus

BACKGROUND

The main protease is an important structural protein of SARS-CoV-2, essential for its survivability inside a human host. Considering current vaccines' limitations and the absence of approved therapeutic targets, Mpro may be regarded as the potential candidate drug target. Novel fungal phytocompound Astrakurkurone may be studied as the potential Mpro inhibitor, considering its medicinal properties reported elsewhere.

METHODS

In silico molecular docking was performed with Astrakurkurone and its twenty pharmacophore-based analogues against the native Mpro protein. A hypothetical Mpro was also constructed with seven mutations and targeted by Astrakurkurone and its analogues. Furthermore, multiple parameters such as statistical analysis (Principal Component Analysis), pharmacophore alignment, and drug likeness evaluation were performed to understand the mechanism of protein-ligand molecular interaction. Finally, molecular dynamic simulation was done for the top-ranking ligands to validate the result.

RESULT

We identified twenty Astrakurkurone analogues through pharmacophore screening methodology. Among these twenty compounds, two analogues namely, ZINC89341287 and ZINC12128321 showed the highest inhibitory potentials against native and our hypothetical mutant Mpro, respectively (-7.7 and -7.3 kcal mol-1) when compared with the control drug Telaprevir (-5.9 and -6.0 kcal mol-1). Finally, we observed that functional groups of ligands namely two aromatic and one acceptor groups were responsible for the residual interaction with the target proteins. The molecular dynamic simulation further revealed that these compounds could make a stable complex with their respective protein targets in the near-native physiological condition.

CONCLUSION

To conclude, Astrakurkurone analogues ZINC89341287 and ZINC12128321 can be potential therapeutic agents against the highly infectious SARS-CoV-2 virus.

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.

Virtual screening of potential anticancer drugs based on microbial products

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Potential activity of Linezolid against SARS-CoV-2 using electronic and molecular docking study

The crescent evolution of a global pandemic COVID-19 and its respiratory syndrome (SARS-Cov-2) has been a constant concern (Ghosh 2021; Khan et al. 2021; Alazmi and Motwalli 2020; Vargas et al. 2020). The absence of a proven and effective medication has compelled all the scientific community to search for a new drug. The use of known drugs is a faster way to develop new therapies. Molecular docking is a powerful tool (Gao et al. J Mol Model 10: 44-54, 2004; Singh et al. J Mol Model 18: 39-51, 2012; Schulz-Gasch and Stahl J Mol Model 9:47-57, 2003) to study the interaction of potential drugs with SARS-CoV-2, Alsalme et al. (2020) and Sanders et al. (2020) spike protein as a consequence the main goal of this article is to present the result of the study of an interaction between (R and S)-Linezolid with receptor-binding domain (RBD) of SARS-Cov-2 spike protein complexed with human Angiostensin-converting enzyme 2 (ACE2) (6vW1 - from PDB). The Linezolid enantiomers were optimized at B3LYP/6-311++G(2d,p) level of theory. Molecular docking of the system (S)-Linezolid⋯RBD⋯ACE2 and (R)-Linezolid⋯RBD⋯ACE2 was performed, the analysis was made using LigPlot+ and NCIplot software packages, to understand the intermolecular interactions. The UV-Vis and ECD of the complexes - (R and S)-Linezolid⋯RBD⋯ACE2 were performed in two layers with DFT/6-311++G(3df,2p) and DFT/6-31G(d), respectively. The results showed that only the (S)-Linezolid had a stable interaction with - 8.05 kcal.mol- 1, whereas all the R-enantiomeric configurations had positive values of binding energy. The (S)-Linezolid had the same interactions as in the (S)-Linezolid ⋯ Haluarcula morismortui Ribosomal system, where it is well-known the fact that the latter has biological activity. A specific interaction on the fluorine ring justified an attenuation on the ECD signal, in comparison to isolated species. Therefore, some biological activity of (S)-Linezolid with SARS-CoV-2 RBD was expected, indicated by the modification of its ECD signal and justified by a similar interaction in the S-Linezolid⋯Haluarcula marismortui Ribosomal system.

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.

Are losartan and imatinib effective against SARS-CoV2 pathogenesis? A pathophysiologic-based in silico study

Proposing a theory about the pathophysiology of cytokine storm in COVID19, we were to find the potential drugs to treat this disease and to find any effect of these drugs on the virus infectivity through an in silico study. COVID-19-induced ARDS is linked to a cytokine storm phenomenon not explainable solely by the virus infectivity. Knowing that ACE2, the hydrolyzing enzyme of AngII and SARS-CoV2 receptor, downregulates when the virus enters the host cells, we hypothesize that hyperacute AngII upregulation is the eliciting factor of this ARDS. We were to validate this theory through reviewing previous studies to figure out the role of overzealous activation of AT1R in ARDS. According to this theory losartan may attenuate ARDS in this disease. Imatinib, has previously been elucidated to be promising in modulating lung inflammatory reactions and virus infectivity in SARS and MERS. We did an in silico study to uncover any probable other unconsidered inhibitory effects of losartan and imatinib against SARS-CoV2 pathogenesis. Reviewing the literature, we could find that over-activation of AT1R could explain precisely the mechanism of cytokine storm in COVID19. Our in silico study revealed that losartan and imatinib could probably: (1) decline SARS-CoV2 affinity to ACE2. (2) inhibit the main protease and furin, (3) disturb papain-like protease and p38MAPK functions. Our reviewing on renin-angiotensin system showed that overzealous activation of AT1R by hyper-acute excess of AngII due to acute downregulation of ACE2 by SARS-CoV2 explains precisely the mechanism of cytokine storm in COVID-19. Besides, based on our in silico study we concluded that losartan and imatinib are promising in COVID19.

Designing a novel mRNA vaccine against SARS-CoV-2: An immunoinformatics approach

SARS-CoV-2 is the deadly virus behind COVID-19, the disease that went on to ravage the world and caused the biggest pandemic 21st century has witnessed so far. On the face of ongoing death and destruction, the urgent need for the discovery of a vaccine against the virus is paramount. This study resorted to the emerging discipline of immunoinformatics in order to design a multi-epitope mRNA vaccine against the spike glycoprotein of SARS-CoV-2. Various immunoinformatics tools were utilized to predict T and B lymphocyte epitopes. The epitopes were channeled through a filtering pipeline comprised of antigenicity, toxicity, allergenicity, and cytokine inducibility evaluation with the goal of selecting epitopes capable of generating both T and B cell-mediated immune responses. Molecular docking simulation between the epitopes and their corresponding MHC molecules was carried out. 13 epitopes, a highly immunogenic adjuvant, elements for proper sub-cellular trafficking, a secretion booster, and appropriate linkers were combined for constructing the vaccine. The vaccine was found to be antigenic, almost neutral at physiological pH, non-toxic, non-allergenic, capable of generating a robust immune response and had a decent worldwide population coverage. Based on these parameters, this design can be considered a promising choice for a vaccine against SARS-CoV-2.

In Silico, Molecular Docking and In Vitro Antimicrobial Activity of the Major Rapeseed Seed Storage Proteins

BACKGROUND

In addition to their use as an edible oil and condiment crop, mustard and rapeseed (Brassica napus L., B. juncea (L.) Czern., B. nigra (L.) W.D.J.Koch, B. rapa L. and Sinapis alba L.) have been commonly used in traditional medicine for relieving pain, coughs and treating infections. The seeds contain high amounts of oil, while the remaining by-product meal after oil extraction, about 40% of seed dry weight, has a low value despite its high protein-content (~85%). The seed storage proteins (SSP) 2S albumin-type napin and 12S globulin-type cruciferin are the two predominant proteins in the seeds and show potential for value adding to the waste stream; however, information on their biological activities is scarce. In this study, purified napin and cruciferin were tested using in silico, molecular docking, and in vitro approaches for their bioactivity as antimicrobial peptides.

MATERIALS AND METHODS

The 3D-structure of 2S albumin and 12S globulin storage proteins from B. napus were investigated to predict antimicrobial activity employing an antimicrobial peptide database survey. To gain deeper insights into the potential antimicrobial activity of these SSP, in silico molecular docking was performed. The purified B. napus cruciferin and napin were then tested against both Gram-positive and Gram-negative bacteria for in vitro antimicrobial activity by disc diffusion and microdilution antimicrobial susceptibility testing.

RESULTS

In silico analysis demonstrated both SSP share similar 3D-structure with other well studied antimicrobial proteins. Molecular docking revealed that the proteins exhibited high binding energy to bacterial enzymes. Cruciferin and napin proteins appeared as a double triplet and a single doublet, respectively, following SDS-PAGE. SDS-PAGE and Western blotting also confirmed the purity of the protein samples used for assessment of antimicrobial activity. Antimicrobial susceptibility testing provided strong evidence for antimicrobial activity for the purified napin protein; however, cruciferin showed no antimicrobial activity, even at the highest dose applied.

DISCUSSION

In silico and molecular docking results presented evidence for the potential antimicrobial activity of rapeseed cruciferin and napin SSP. However, only the in vitro antimicrobial activity of napin was confirmed. These findings warrant further investigation of this SSP protein as a potential new agent against infectious disease.

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.

Distinct characteristics of the substrate binding between highly homologous catalase-related allene oxide synthase and hydroperoxide lyase

A catalase-related allene oxide synthase (cAOS) or a hydroperoxide lyase (cHPL) fused together with an 8R-lipoxygenase is involved in the stress signaling of corals via an arachidonic acid pathway. cAOS gives rise to α-ketol and cyclopentenone, while cHPL catalyzes the cleavage of 8R-hydroperoxyeicosatetraenoic acid (8R-HpETE) to C8-oxo acid and C12 aldehyde. In silico analysis of the substrate entry sites of highly identical coral cAOS and cHPL indicated that two positively charged residues of cAOS, K60 and K107, and the corresponding residues of cHPL, E60 and K107, may be involved in the anchoring of the carboxy group of polyunsaturated fatty acid (PUFA) hydroperoxides. A mutational analysis of cAOS and cHPL revealed that K60 or E60 and K107 were not necessary in the tethering of 8R-HpETE, however, the E60 of cHPL was essential in the productive binding of PUFA hydroperoxides. The substrate preferences of cAOS and cHPL were determined with hydroperoxy derivatives of C18, C20, C22 PUFAs, anandamide (AEA), 1-arachidonoyl glycerol (1-AG) and selected methylated substrates. Although cAOS and cHPL were able to metabolize different free PUFA substrates and arachidonoyl derivatives, only cHPL catalyzed the reaction with methylated PUFA hydroperoxides. The differences in the substrate binding and preferences between cAOS and cHPL can be explained by the distinct properties of their substrate entry sites. The current study demonstrated that homologous PUFA metabolizing enzymes may contribute to the versatile usage of the substrate pool.

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.

Synergistic Effects of Curcumin and Piperine as Potent Acetylcholine and Amyloidogenic Inhibitors With Significant Neuroprotective Activity in SH-SY5Y Cells via Computational Molecular Modeling and in vitro Assay

Hallmarks of Alzheimer's disease (AD) pathology include acetylcholine (ACh) deficiency and plaque deposition. Emerging studies suggest that acetylcholinesterase (AChE) may interact with amyloid β (Aβ) to promote aggregation of insoluble Aβ plaques in brains of patients. Current therapeutic options available for AD patients, such as AChE inhibitors, provide only symptomatic relief. In this study, we screened four natural compounds believed to harbor cognitive benefits-curcumin, piperine, bacoside A, and chebulinic acid. In the first section, preliminary screening through computational molecular docking simulations gauged the suitability of the compounds as novel AChE inhibitors. From here, only compounds that met the in silico selection criteria were selected for the second section through in vitro investigations, including AChE enzyme inhibition assay, 3-(4,5-dimenthylthiazol-2-yl)-2,5-dimethyltetrazolium bromide (MTT) assay, Thioflavin T (ThT) assay, and biochemical analysis via a neuronal cell line model. Of the four compounds screened, only curcumin (-9.6 kcal/mol) and piperine (-10.5 kcal/mol) showed favorable binding affinities and interactions towards AChE and were hence selected. In vitro AChE inhibition demonstrated that combination of curcumin and piperine showed greater AChE inhibition with an IC50 of 62.81 ± 0.01 μg/ml as compared to individual compounds, i.e., IC50 of curcumin at 134.5 ± 0.06 μg/ml and IC50 of piperine at 76.6 ± 0.08 μg/ml. In the SH-SY5Y cell model, this combination preserved cell viability up to 85%, indicating that the compounds protect against Aβ-induced neuronal damage (p < 0.01). Interestingly, our results also showed that curcumin and piperine achieved a synergistic effect at 35 μM with an synergism quotient (SQ) value of 1.824. Synergistic behavior indicates that the combination of these two compounds at lower concentrations may provide a better outcome than singularly used for Aβ proteins. Combined curcumin and piperine managed to inhibit aggregation (reduced ThT intensity at 0.432 a.u.; p < 0.01) as well as disaggregation (reduced ThT intensity at 0.532 a.u.; p < 0.01) of fibrillar Aβ42. Furthermore, combined curcumin and piperine reversed the Aβ-induced up-regulation of neuronal oxidative stress (p < 0.01). In conclusion, curcumin and piperine demonstrated promising neuroprotective effects, whereas bacoside A and chebulinic acid may not be suitable lead compounds. These results are hoped to advance the field of natural products research as potentially therapeutic and curative AD agents.

[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.

Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein-Ligand Complexes in Cognate Docking

General and reliable description of structures and energetics in protein-ligand (PL) binding using the docking/scoring methodology has until now been elusive. We address this urgent deficiency of scoring functions (SFs) by the systematic development of corrected semiempirical quantum mechanical (SQM) methods, which correctly describe all types of noncovalent interactions and are fast enough to treat systems of thousands of atoms. Two most accurate SQM methods, PM6-D3H4X and SCC-DFTB3-D3H4X, are coupled with the conductor-like screening model (COSMO) implicit solvation model in so-called "SQM/COSMO" SFs and have shown unique recognition of native ligand poses in cognate docking in four challenging PL systems, including metalloprotein. Here, we apply the two SQM/COSMO SFs to 17 diverse PL complexes and compare their performance with four widely used classical SFs (Glide XP, AutoDock4, AutoDock Vina, and UCSF Dock). We observe superior performance of the SQM/COSMO SFs and identify challenging systems. This method, due to its generality, comparability across the chemical space, and lack of need for any system-specific parameters, gives promise of becoming, after comprehensive large-scale testing in the near future, a useful computational tool in structure-based drug design and serving as a reference method for the development of other SFs.

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.