Docking Performance Research Articles

In silico and in vitro assessment of androgen receptor antagonists

There is a growing concern for male reproductive health as studies suggest that there is a sharp increase in prostate cancer and other fertility related problems. Apart from lifestyle, pollutants are also known to negatively affect the reproductive system. In addition to many other compounds that have been shown to alter androgen signaling, several environmental pollutants are known to disrupt androgen signaling via binding to androgen receptor (AR) or indirectly affecting the androgen synthesis. We analyzed here the molecular mechanism of the interaction between the human AR Ligand Binding Domain (hAR-LBD) and two environmental pollutants, linuron (a herbicide) and procymidone (a pesticide), and compared with the steroid agonist dihydrotestosterone (DHT) and well-known hAR antagonists bicalutamide and enzalutamide. Using molecular docking and dynamics simulations, we showed that the co-activator interaction site of the hAR-LBD is disrupted in different ways by different ligands. Binding free energies of the ligands were also ordered in increasing order as follows: linuron, procymidone, DHT, bicalutamide, and enzalutamide. These data were confirmed by in vitro assays. Reporter assay with MDA-kb2 cells showed that linuron, procymidone, bicalutamide and enzalutamide can inhibit androgen mediated activation of luciferase activity. Gene expression analysis further showed that these compounds can inhibit the expression of prostate specific antigen (PSA) and microseminoprotein beta (MSMB) in prostate cell line LNCaP. Comparative analysis showed that procymidone is more potent than linuron in inhibiting AR activity. Furthermore, procymidone at 10 μM dose showed equivalent and higher activity to AR inhibitor enzalutamide and bicalutamide respectively.

Relative assessment of different statistical instruments and measures for the prediction of promising outcomes using docking, virtual screening and ADMET analysis against HIV-RT

Reverse transcriptase is the most therapeutic target for the discovery of novel, potent, and non-toxic new anti-retroviral drugs. In the present work, various docking software such as Sybyl Surflex-Dock, OpenEye FRED, and Hermes GOLD were evaluated for their efficiency to reproduce known cognate inhibitors' conformations. Three metrics were used and compared to assess the performance of the applied scoring functions, i.e. enrichment factor, receiver operating characteristic (ROC) curves, and Bedroc analysis. Twelve different scoring functions of three softwares were used to assess their ability to rank the cognate ligand within the active site of its proteins. The extensive virtual screening task was performed on eight crystal structures, and the performance of docking and scoring was assessed by their ability to efficiently detect known active compounds enriched in the top-ranked of the list among a randomly selected dataset of the ten thousand compounds of the NCI database. The effectiveness of post-docking relaxation in Surflex was also evaluated. The top 20, 50, and 100 compounds were selected based on consensus scoring functions from all 48 proteins with different ligand complexes. Further, the shortlisted leads were subjected to ADMET via using Discovery Studio. The results further implicate the importance of various statistical tools that should be followed before large-scale virtual screening for the drug discovery process. In silico results demonstrating the experiment was successful. The study of the research covers the combinatorial in silico techniques such as benchmarking of the softwares and scoring functions, statistical tools applied for screening and different conformations of HIV-RT crystal structures for virtual screening with rigid and flexible molecular docking and molecular dynamics simulation approach. This study reveals a clear roadmap to identify novel scaffolds against HIV-RT for antiretroviral therapy, thus providing the remedial solutions of HIV related infections and other diseases caused by malfunctioning of the target protein. Communicated by Ramaswamy H. Sarma

MCSS-Based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

Computational fragment-based approaches are widely used in drug design and discovery. One of their limitations is the lack of performance of docking methods, mainly the scoring functions. With the emergence of fragment-based approaches for single-stranded RNA ligands, we analyze the performance in docking and screening powers of an MCSS-based approach. The performance is evaluated on a benchmark of protein-nucleotide complexes where the four RNA residues are used as fragments. The screening power can be considered the major limiting factor for the fragment-based modeling or design of sequence-selective oligonucleotides. We show that the MCSS sampling is efficient even for such large and flexible fragments. Hybrid solvent models based on some partial explicit representations improve both the docking and screening powers. Clustering of the n best-ranked poses can also contribute to a lesser extent to better performance. A detailed analysis of molecular features suggests various ways to optimize the performance further.

An evaluation of combined strategies for improving the performance of molecular docking.

Molecular docking is a fast and efficient computational method for the prediction of the binding mode and binding affinity between a ligand and a target protein at the atomic level. However, the performance of current docking programs is less than satisfactory. Herein, with a focus on free programs and scoring functions, the performances of LeDock and three standalone scoring functions were tested by 195 high-quality protein-ligand complexes. Results showed that the success rate for the best pose of the free available docking program LeDock achieved 89.20%, indicative of a strong sampling power. Based on the poses generated by LeDock, a comparative evaluation on other three non-commercial scoring functions, including DSX (DrugScore X), PoseScore and X-score was performed. Among all the evaluated scoring functions, DSX and X-score exhibited the best scoring power and ranking power, respectively. The performances of LeDock, DSX and X-score were similar in docking power test, which was much better than the PoseScore. Accordingly, it was suggested that the combination of pose sampling by LeDock with rescoring by DSX or X-score could improve the prediction accuracy of molecular docking and applied in the lead discovery.

The B-factor index for the binding site (BFIbs) to prioritize crystal protein structures for docking

In cheminformatics, protein-ligand docking is a powerful tool applied for virtual screening, pose prediction, and binding affinity estimation. However, docking results depend on the quality of the crystal protein structures. Although several crystallographic indices can assist the selection of proteins, these are often misused by non-experts or have little applicability. Hereby, we propose the B-factor index for the binding site (BFIbs), which indicates the atomic fluctuations of the atoms in the binding site in comparison with atoms in the protein. Using an automated docking workflow, we performed docking experiments on 26,019 protein-ligand complexes. The docking performances were analyzed based on five crystallographic quality indices, i.e., BFIbs, DPI (the diffraction–component precision index), DPIbs (DPI of the binding site atoms), RRfree (R–Rfree), and the resolution of the X-ray crystal structure. Only BFIbs was found to significantly correlate with the root–mean–square deviation (RMSD) computed between the ligand poses in the crystal structures and the predicted docking poses. The majority of the best docking results (RMSD < 2 A) were indicated by BFIbs < 1. We conclude that BFIbs, as a simple and interpretable parameter that complements other indices, can help to effectively prioritize protein structures for structure-based cheminformatics.

Visual navigation and docking for a planar type AUV docking and charging system

As being increasingly used for underwater explorations, Autonomous Underwater Vehicles (AUVs) are diversified in their structures and outlines to match up with various mission requirements, which brings challenges to the design of customized submerged docking stations. Aiming at providing a generalized solution, this paper proposes an omnidirectional and positioning-tolerant planar type AUV docking and charging platform, which has no constraints on AUV structures. In order to solve the planar-type docking issues, an integrated visual navigation and docking algorithm is developed for the miniaturized prototype AUV. The adopted monocular Simultaneous Localization and Mapping (SLAM) program takes charge of the wide-range underwater locationing, and handles practical problems such as scale drift and tracking failure. A combined control strategy of horizontal dynamic positioning and vertical visual servo docking is developed for the terminal docking process. The strategy is robust against constant water flow disturbance and performs stable docking using a roll motion adjustment. The visual navigation and docking performances and other system functions are successfully validated in simulation, water pool experiment, and sea trial.

Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities

In this study, we developed a novel algorithm to improve the screening performance of an arbitrary docking scoring function by recalibrating the docking score of a query compound based on its structure similarity with a set of training compounds, while the extra computational cost is neglectable. Two popular docking methods, Glide and AutoDock Vina were adopted as the original scoring functions to be processed with our new algorithm and similar improvement performance was achieved. Predicted binding affinities were compared against experimental data from ChEMBL and DUD-E databases. 11 representative drug receptors from diverse drug target categories were applied to evaluate the hybrid scoring function. The effects of four different fingerprints (FP2, FP3, FP4, and MACCS) and the four different compound similarity effect (CSE) functions were explored. Encouragingly, the screening performance was significantly improved for all 11 drug targets especially when CSE = S4 (S is the Tanimoto structural similarity) and FP2 fingerprint were applied. The average predictive index (PI) values increased from 0.34 to 0.66 and 0.39 to 0.71 for the Glide and AutoDock vina scoring functions, respectively. To evaluate the performance of the calibration algorithm in drug lead identification, we also imposed an upper limit on the structural similarity to mimic the real scenario of screening diverse libraries for which query ligands are general-purpose screening compounds and they are not necessarily structurally similar to reference ligands. Encouragingly, we found our hybrid scoring function still outperformed the original docking scoring function. The hybrid scoring function was further evaluated using external datasets for two systems and we found the PI values increased from 0.24 to 0.46 and 0.14 to 0.42 for A2AR and CFX systems, respectively. In a conclusion, our calibration algorithm can significantly improve the virtual screening performance in both drug lead optimization and identification phases with neglectable computational cost.

Molecular Docking Performance of Selective Organic Compounds with Target Protein

In this article, the docking analysis has been carried out to know some selective compounds' interaction to bind with a targeted protein. There are three approaches have been analyzed here with three different kinds of protein as the target species. They are 2-aminobenzimidazole (2ABZ) with Acetylcholinesterase receptor (AChE), Phenoxazine (POZ) with Penicillin-binding proteins (PBPs) receptor, and Phenothiazines with Calcium/calmodulin-dependent protein kinase IV (CAMK IV) receptor. All three studies showed that the binding is perfect at the binding site and explained with hydrogen bonding interaction via donor-acceptor interactions.

Cardamonin Exerts Antitumor Effect on Human Hepatocellular Carcinoma Xenografts in Athymic Nude Mice through Inhibiting NF-κβ Pathway.

Cardamonin (CADMN) exerts an in vitro antiproliferative and apoptotic actions against human hepatocellular carcinoma cells (HepG2). This study aimed to investigate the in vivo anti-tumorigenic action of CADMN against human hepatocellular carcinoma xenografts in an athymic nude mice, as well as to study the molecular docking and safety profile of this compound. Acute toxicity study demonstrated that CADMN is safe and well-tolerated up to 2000 mg/kg in ICR mice. Oral administration of 50 mg/kg/day of CADMN in xenografted nude mice showed a significant suppression in tumor growth as compared to untreated control group without pronounced toxic signs. Immunohistochemistry assay showed downregulation of proliferative proteins such as PCNA and Ki-67 in treated groups as compared to untreated control. Additionally, immunofluorescence analysis showed a significant downregulation in anti-apoptotic Bcl-2 protein, whereas pre-apoptotic Bax protein was significantly upregulated in nude mice treated with 25 and 50 mg/kg CADMN as compared to untreated mice. The findings also exhibited down-regulation of NF-κB-p65, and Ikkβ proteins, indicating that CADMN deactivated NF-κB pathway. The molecular docking studies demonstrated that CADMN exhibits good docking performance and binding affinities with various apoptosis and proliferation targets in hepatocellular cancer cells. In conclusion, CADMN could be a potential anticancer candidate against hepatocellular carcinoma. Other pharmacokinetics and pharmacodynamics properties, however, need to be further investigated in depth.

Performance of protein-ligand docking with CDK4/6 inhibitors: a case study.

It is widely believed that tertiary protein-ligand interactions are essential in determining protein function. Currently, the structure sampling and scoring function in traditional docking methods still have limitations. Therefore, new methods for protein-ligand docking are desirable. The accurate docking can speed up the early-stage development of new drugs. Here we present a multi-source information-based protein-ligand docking approach (pmDock). In the CDK4/6 inhibitor case study, pmDock produces a substantial accuracy increases between the predicted geometry centers of ligands and experiments compared to AutoDock and SwissDock alone. Also, pmDock improves predictions for critical binding sites and captures more tertiary binding interactions. Our results demonstrate that pmDock is a reliable docking method for accurate protein-ligand prediction.

LEADS-FRAG: A Benchmark Data Set for Assessment of Fragment Docking Performance.

Fragment-based drug design is a popular approach in drug discovery, which makes use of computational methods such as molecular docking. To assess fragment placement performance of molecular docking programs, we constructed LEADS-FRAG, a benchmark data set containing 93 high-quality protein-fragment complexes that were selected from the Protein Data Bank using a rational and unbiased process. The data set contains fully prepared protein and fragment structures and is publicly available. Moreover, we used LEADS-FRAG for evaluating the small-molecule docking programs AutoDock, AutoDock Vina, FlexX, and GOLD for their fragment docking performance. GOLD in combination with the scoring function ChemPLP and AutoDock Vina performed best and generated near-native conformations (root mean square deviation <1.5 Å) for more than 50% of the data set considering the top-ranked docking pose. Taking into account all docking poses, the tested programs generated near-native conformations for up to 86% of the fragments in LEADS-FRAG. By rescoring all docking poses with the GOLD scoring functions and the Protein-Ligand Informatics force field, the number of near-native conformations increased up to 40% with respect to the top-rescored poses. Our results show that conventional small-molecule docking programs achieve a satisfactory fragment docking performance when utilizing rescoring.

Structure–activity relationship and molecular docking analysis of cysteine‐containing dipeptides as antioxidant and ACE inhibitory

SummaryComparative molecular field analysis (CoMFA), comparative similarity index analysis (CoMSIA) and hologram QSAR (HQSAR) were used to identify the relationship between the structure of cysteine‐containing dipeptide with antioxidant and ACE inhibitory activity; then, molecular docking, ADME prediction and thermostability assay were applied to illustrate the performance in docking with ACE, metabolic information and thermostability of the strongest activity dipeptide. According to pIC50 of 2, 2‐diphenyl‐1‐picrylhydrazyl (DPPH) and 2, 2′‐azinobis‐(3‐ethyl‐benzothiazoline‐6‐sulphonate) (ABTS), constructed CoMFA, CoMSIA and HQSAR models predicted the structural properties of carboxyl of C‐terminus impacted on the antioxidant activity. The properties of side chain of C‐terminus influenced on the ACE inhibitory activity based on CoMFA, CoMSIA and HQSAR models. As the strongest multifunctional cysteine‐containing dipeptide, CW interacted with ACE by S1 pocket, S2 pocket, Zn2+. It was promising for CW to utilise in the functional foods due to less interference in normal life activities, high oral bioavailability and stability under 70 °C.

DFT Study on the Electronic Properties, Spectroscopic Profile, and Biological Activity of 2-Amino-5-trifluoromethyl-1,3,4-thiadiazole with Anticancer Properties.

Extensive investigation on the molecular and electronic structure of 2-amino-5-trifluoromethyl-1,3,4-thiadiazole in the ground state and in the first excited state has been performed. The energy barrier corresponding to the conversion between imino and amino tautomers has been calculated, which indicates the existence of amino tautomer in solid state for the title compound. The FT–Raman and FT-IR spectra were recorded and compared with theoretical vibrational wavenumbers, and a good coherence has been observed. The MESP map, dipole moment, polarizability, and hyperpolarizability have been calculated to comprehend the properties of the title molecule. High polarizability value estimation of the title compound may enhance its bioactivity. Natural bonding orbital analysis has been done on monomer and dimer to investigate the charge delocalization and strength of hydrogen bonding, respectively. Strong hydrogen bonding interaction energies of 17.09/17.49 kcal mol–1 have been calculated at the B3LYP/M06-2X functional. The UV–vis spectrum was recorded and related to the theoretical spectrum. The title compound was biologically examined for anticancer activity by studying the cytotoxic performance against two human cancer cell lines (A549 and HeLa) along with the molecular docking simulation. Both molecular docking and cytotoxic performance against cancer cell lines show positive outcomes, and the title compound appears to be a promising anticancer agent.

Cognition Test Battery Performance Is Associated with Simulated 6df Spacecraft Docking Performance.

INTRODUCTION: Environmental and operational stressors commonly encountered in spaceflight can affect astronaut cognitive performance. It is currently unclear how performance decrements on test batteries that assess individual cognitive domains translate to complex operational performance.METHODS: N 30 healthy adults (mean SD age 33.5 7.1 yr, range 2548 yr; 16 men) with demographic characteristics similar to astronauts performed all 10 tests of the Cognition test battery as well as a simulated 6 degrees-of-freedom (6df) spacecraft docking task 15 times. Performance on 60 Cognition outcome variables was rank-correlated with 6df docking performance individually as well as in models containing up to 12 predictors after accounting for sex, age, and study design effects.RESULTS: Average response time on the Digit Symbol Substitution Test (DSST)a measure of processing speed requiring complex scanning, visual tracking, and working memorywas the best individual predictor of 6df docking performance (unadjusted r 0.550; semipartial cross-validated R² 0.244). Furthermore, higher levels of spatial orientation efficiency and vigilant attention, lower levels of impulsivity, and faster response speed were associated with higher 6df performance, while sensorimotor speed, memory, and risk decision making were less relevant. After semipartial cross-validation, a model with three Cognition outcomes (DSST average response time, Abstract Matching accuracy, and conservative response bias on the Fractal 2-Back test) explained 30% of the variance in 6df performance.CONCLUSIONS: This study demonstrates direct links between performance on tests designed to assess specific cognitive domains and complex operational docking performance.Basner M, Moore TM, Hermosillo E, Nasrini J, Dinges DF, Gur RC, Johannes B. Cognition test battery performance is associated with simulated 6df spacecraft docking performance. Aerosp Med Hum Perform. 2020; 91(11):861867.

Protein–protein docking with interface residue restraints* *Project supported by the National Natural Science Foundation of China (Grant No. 31670724) and the Startup Grant of Huazhong University of Science and Technology.

The prediction of protein–protein complex structures is crucial for fundamental understanding of celluar processes and drug design. Despite significant progresses in the field, the accuracy of ab initio docking without using any experimental restraints remains relatively low. With the rapid advancement of structural biology, more and more information about binding can be derived from experimental data such as NMR experiments or chemical cross-linking. In addition, information about the residue contacts between proteins may also be derived from their sequences by using evolutionary analysis or deep learning. Here, we propose an efficient approach to incorporate interface residue restraints into protein–protein docking, which is named as HDOCKsite. Extensive evaluations on the protein–protein docking benchmark 4.0 showed that HDOCKsite significantly improved the docking performance and obtained a much higher success rate in binding mode predictions than original ab initio docking.

Development of a New Scoring Function for Virtual Screening: APBScore.

In this study, we developed a new physical-based scoring function, Atom Pair-Based Scoring function (APBScore), which includes pairwise van der Waals (VDW), electrostatic interaction, and hydrogen bond energies between the receptor and ligand. Despite the simple form of this scoring function, the tests of APBScore on several benchmark datasets show its excellent performance in scoring as compared to other widely used traditional scoring functions. Particularly, the scoring performance of APBScore is among the top-ranking scoring functions for complexes with zinc/ligand interactions. In addition to the scoring power, APBScore also shows good performance in ranking and docking as compared to some traditional scoring functions. In addition, the APBScore is sensitive to receptor/ligand atomic collisions and therefore can correctly identify decoy complex structures with atomic collisions. These features are the result of optimizing atom-pair VDW interactions, performing structural minimization of the initial structures, and treating zinc/ligand interactions more accurately. The source code of APBScore is available at https://github.com/BaoJingxiao/APBScore.

Molecular Docking Evaluation of Some Indonesian’s Popular Herbals for a Possible COVID-19 Treatment

Indonesia’s rich tropical biodiversity offers many possible options for fighting the unprecedented outbreak of coronavirus disease 2019 (COVID-19). In this paper, we evaluate the in silico (molecular docking) performance of some popular herbal supplements and natural remedies, which are usually consumed by Indonesian people and compare them to the docking performance of the control ligand (N3 inhibitor) and some protease-inhibitor drugs. The tested active compounds were grouped based on their popularity in the market, i.e., honey, black seed (Nigella sativa), Javanese turmeric (Curcuma xanthorriza roxb), and mangosteen, which usually served in the form of encapsulated oil or powder. The results show that most of the tested active compounds perform better than N3 inhibitor and protease inhibitor drugs (lopinavir and darunavir) in inhibiting the main protease of SARS-CoV-2 virus of COVID-19 (C19MP). Based on the combined scores of binding affinity, the similarity of active sites (SAS), and the drug-likeness properties of the ligand, gamma mangostin turns out to be the best possible C19MP inhibitor as compared to other ligands. The active site analysis also reveals that His41, Met49, Leu141, and Asn142 are among the most important amino acids that play a significant role in the protein-ligand binding due to their frequent occurrence in the docking simulation.

FWAVina: A novel optimization algorithm for protein-ligand docking based on the fireworks algorithm

Protein-ligand docking is an essential process that has accelerated drug discovery. How to accurately and effectively optimize the predominant position and orientation of ligands in the binding pocket of a target protein is a major challenge. This paper proposed a novel ligand binding pose search method called FWAVina based on the fireworks algorithm, which combined the fireworks algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon local search method adopted in AutoDock Vina to address the pose search problem in docking. The FWA was used as a global optimizer to rapidly search promising poses, and the Broyden-Fletcher-Goldfarb-Shannon method was incorporated into FWAVina to perform an exact local search. FWAVina was developed and tested on the PDBbind and DUD-E datasets. The docking performance of FWAVina was compared with the original Vina program. The results showed that FWAVina achieves a remarkable execution time reduction of more than 50 % than Vina without compromising the prediction accuracies in the docking and virtual screening experiments. In addition, the increase in the number of ligand rotatable bonds has almost no effect on the efficiency of FWAVina. The higher accuracy, faster convergence and improved stability make the FWAVina method a better choice of docking tool for computer-aided drug design. The source code is available at https://github.com/eddyblue/FWAVina/.

Modelling the Anti-Methicillin-Resistant Staphylococcus Aureus (MRSA) Activity of Cannabinoids: A QSAR and Docking Study

Twenty-four cannabinoids active against MRSA SA1199B and XU212 were optimized at WB97XD/6-31G(d,p), and several molecular descriptors were obtained. Using a multiple linear regression method, several mathematical models with statistical significance were obtained. The robustness of the models was validated, employing the leave-one-out cross-validation and Y-scrambling methods. The entire data set was docked against penicillin-binding protein, iso-tyrosyl tRNA synthetase, and DNA gyrase. The most active cannabinoids had high affinity to penicillin-binding protein (PBP), whereas the least active compounds had low affinities for all of the targets. Among the cannabinoid compounds, Cannabinoid 2 was highlighted due to its suitable combination of both antimicrobial activity and higher scoring values against the selected target; therefore, its docking performance was compared to that of oxacillin, a commercial PBP inhibitor. The 2D figures reveal that both compounds hit the protein in the active site with a similar type of molecular interaction, where the hydroxyl groups in the aromatic ring of cannabinoids play a pivotal role in the biological activity. These results provide some evidence that the anti-Staphylococcus aureus activity of these cannabinoids may be related to the inhibition of the PBP protein; besides, the robustness of the models along with the docking and Quantitative Structure–Activity Relationship (QSAR) results allow the proposal of three new compounds; the predicted activity combined with the scoring values against PBP should encourage future synthesis and experimental testing.

GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking.

Protein–ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, gwovina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side‐chain sampling. The new method was validated for rigid and flexible‐receptor docking using four independent datasets. In rigid docking, gwovina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible‐receptor docking, gwovina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, gwovina can play a role in solving the complex flexible‐receptor docking cases and is suitable for virtual screening of compound libraries. gwovina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.