How to do an evaluation: pitfalls and traps

Mechanism of AGT-Mediated Repair of dG-dC Cross-Links in the Drug Resistance to Chloroethylnitrosoureas: Molecular Docking, MD Simulation, and ONIOM (QM/MM) Investigation

Chloroethylnitrosoureas (CENUs) are important chemotherapies applied in the treatment of cancer. They exert anticancer activity by inducing DNA interstrand cross-links (ICLs) via the formation of two O6-alkylguanine intermediates, O6-chloroethylguanine (O6-ClEtG) and N1,O6-ethanoguanine (N1,O6-EtG). However, O6-alkylguanine-DNA alkyltransferase (AGT), a DNA-repair enzyme, can restore the O6-alkylguanine damages and thereby obstruct the formation of ICLs (dG-dC cross-link). In this study, the inhibitory mechanism of ICL formation was investigated to elucidate the drug resistance of CENUs mediated by AGT in detail. Based on the structures of the substrate-enzyme complexes obtained from docking and MD simulations, two ONIOM (QM/MM) models with different sizes of the QM region were constructed. The model with a larger QM region, which included the substrate (O6-ClEtG or N1,O6-EtG), a water molecule, and five residues (Tyr114, Cys145, His146, Lys165, and Glu172) in the active pocket of AGT, accurately described the repairing reaction and generated the results coinciding with the experimental outcomes. The repair process consists of two sequential steps: hydrogen transfer to form a thiolate anion on Cys145 and alkyl transfer from the O6 site of guanine (the rate-limiting step). The repair of N1,O6-EtG was more favorable than that of O6-ClEtG from both kinetics and thermodynamics aspects. Moreover, the comparison of the repairing process with the formation of dG-dC cross-link and the inhibition of AGT by O6-benzylguanine (O6-BG) showed that the presence of AGT could effectively interrupt the formation of ICLs leading to drug resistance, and the inhibition of AGT by O6-BG that was energetically more favorable than the repair of O6-ClEtG could not prevent the repair of N1,O6-EtG. Therefore, it is necessary to completely eliminate AGT activity before CENUs medication to enhance the chemotherapeutic effectiveness. This work provides reasonable explanations for the supposed mechanism of AGT-mediated drug resistance of CENUs and will assist in the development of novel CENU chemotherapies and their medication strategies.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

Improvement of binding pose prediction of the MR1 covalent ligands by inclusion of simple pharmacophore constraints and structural waters in the docking process

The major histocompatibility complex (MHC) class I-related molecule, MR1, is a key component of the immune system, presenting antigens to T-cell receptors (TCRs) and modulating the immune response against various antigens. MR1 possesses a compact ligand-binding pocket despite its ability to interact with ligands that can have either agonistic or antagonistic effects on the immune system. Agonistic ligands can stimulate the immune response, while antagonistic ligands do not elicit an immune response. In most cases, ligand binding to MR1 is mediated through a covalent bond with Lys43. However, recent studies have suggested that a variety of small molecules can interact with the MR1-binding site. In this study, we have used several approaches to improve the binding pose prediction of covalent ligands to MR1, including docking in mutated receptors, and imposing simple pharmacophore constraints and structural water molecules. The careful assignment of pharmacophore constraints and inclusion of structural water molecules in the challenging docking process of covalent docking improved the binding pose prediction and virtual screening performance. In a retrospective virtual screening, the proposed approach exhibited EF1% and EF2% values of 7.4 and 5.5, respectively. Conversely, when using the mutated receptor, both EF1% and EF2% were recorded as 0 for the conventional docking method. The performance of the pharmacophore constraints was also evaluated on other covalent docking cases, and compared to previously reported results for common covalent docking methods. The proposed approach achieved an average RMSD of 2.55, while AutoDock4, CovDock, FITTED, GOLD, ICM-Pro, and MOE exhibited average RMSD values of 3.0, 2.93, 3.04, 4.93, 2.44, and 3.36, respectively. Our results demonstrate that the inclusion of simple pharmacophore constraints and structural waters can improve the prediction of binding poses of covalent ligands to MR1, which can aid in the discovery of novel immunotherapeutic agents.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03694-w.

Antifungal Activity, Antibiofilm and Association Studies with O-Alkylamidoximes against Cryptococcus spp

This is the first study that describes the antifungal and anti-biofilm potential of O-alkylamidoximes against strains of Cryptococcus neoformans and Cryptococcus gattii. In vitro tests have shown that O-alkylamidoximes are capable of inhibiting fungal growth and biofilm formation of the C. neoformans and C. gattii strains, suggesting, from molecular docking, the potential for interaction with the Hsp90. The associations between O-alkylamidoximes and amphotericin B were beneficial. Therefore, O-alkylamidoximes can be a useful alternative to contribute to the limited arsenal of drugs, since they showed a powerful action against the primary agents of Cryptococcosis.

AlteQ: a new complementarity principle-centered method for the evaluation of docking poses

Molecular docking is the most popular and widely used method for identifying novel molecules against a target of interest. However, docking procedures and their validation are still under intense development. In the present investigation, we evaluate a quantum free-orbital AlteQ method for evaluating docking complexes generated by taking EGFR complexes as an example. The AlteQ method calculates the electron density using Slater's type atomic contributions in the interspace between the receptor and the ligand. Since the interactions are determined by the overlap of electron clouds, they follow the complementarity principle, and an equation can be obtained that describes these interactions. The AlteQ method evaluates the quality of the interaction between the receptor and the ligand, how complementary the interactions are, and due to this, it is used to reject less realistic structures obtained by docking methods. Here, three different equations were used to determine the quality of the interactions in experimental complexes and docked complexes obtained using AutoDock Vina and AutoDock 4.2.6.Communicated by Ramaswamy H. Sarma.

An Effective Swarm Intelligence Optimization Algorithm for Flexible Ligand Docking

In general, flexible ligand docking is used for docking simulations under the premise that the position of the binding site is already known, and meanwhile it can also be used without prior knowledge of the binding site. However, most of the optimization search algorithms used in popular docking software are far from being ideal in the first case, and they can hardly be directly utilized for the latter case due to the relatively large search area. In order to design an algorithm that can flexibly adapt to different sizes of the search area, we propose an effective swarm intelligence optimization algorithm in this paper, called diversity-controlled Lamarckian quantum particle swarm optimization (DCL-QPSO). The highlights of the algorithm are a diversity-controlled strategy and a modified local search method. Integrated with the docking environment of Autodock, the DCL-QPSO is compared with Autodock Vina, Glide and other two Autodock-based search algorithms for flexible ligand docking. Experimental results revealed that the proposed algorithm has a performance comparable to those of Autodock Vina and Glide for dockings within a certain area around the binding sites, and is a more effective solver than all the compared methods for dockings without prior knowledge of the binding sites.

Confidence bands and hypothesis tests for hit enrichment curves

In virtual screening for drug discovery, hit enrichment curves are widely used to assess the performance of ranking algorithms with regard to their ability to identify early enrichment. Unfortunately, researchers almost never consider the uncertainty associated with estimating such curves before declaring differences between performance of competing algorithms. Uncertainty is often large because the testing fractions of interest to researchers are small. Appropriate inference is complicated by two sources of correlation that are often overlooked: correlation across different testing fractions within a single algorithm, and correlation between competing algorithms. Additionally, researchers are often interested in making comparisons along the entire curve, not only at a few testing fractions. We develop inferential procedures to address both the needs of those interested in a few testing fractions, as well as those interested in the entire curve. For the former, four hypothesis testing and (pointwise) confidence intervals are investigated, and a newly developed EmProc approach is found to be most effective. For inference along entire curves, EmProc-based confidence bands are recommended for simultaneous coverage and minimal width. While we focus on the hit enrichment curve, this work is also appropriate for lift curves that are used throughout the machine learning community. Our inferential procedures trivially extend to enrichment factors, as well.

Withasomniferol C, a new potential SARS-CoV-2 main protease inhibitor from the Withania somnifera plant proposed by in silico approaches

Exploring potent herbal medicine candidates is a promising strategy for combating a pandemic in the present global health crisis. In Ayurveda (a traditional medicine system in India), Withania somnifera (WS) is one of the most important herbs and it has been used for millennia as Rasayana (a type of juice) for its wide-ranging health benefits. WS phytocompounds display a broad spectrum of biological activities (such as antioxidant, anticancer and antimicrobial) modulate detoxifying enzymes, and enhance immunity. Inspired by the numerous biological actions of WS phytocompounds, the present investigation explored the potential of the WS phytocompounds against the SARS-CoV-2 main protease (3CLpro). We selected 11 specific withanolide compounds, such as withaphysalin, withasomniferol, and withafastuosin, through manual literature curation against 3CLpro. A molecular similarity analysis showed their similarity with compounds that have an established inhibitory activity against the SARS-CoV-2. In silico molecular docking and molecular dynamics simulations elucidated withasomniferol C (WS11) as a potential candidate against SARS-CoV-2 3CLpro. Additionally, the present work also presents a new method of validating docking poses using the AlteQ method.

Parallel multi-swarm cooperative particle swarm optimization for protein-ligand docking and virtual screening

BACKGROUND

A high-quality docking method tends to yield multifold gains with half pains for the new drug development. Over the past few decades, great efforts have been made for the development of novel docking programs with great efficiency and intriguing accuracy. AutoDock Vina (Vina) is one of these achievements with improved speed and accuracy compared to AutoDock4. Since it was proposed, some of its variants, such as PSOVina and GWOVina, have also been developed. However, for all these docking programs, there is still large room for performance improvement.

RESULTS

In this work, we propose a parallel multi-swarm cooperative particle swarm model, in which one master swarm and several slave swarms mutually cooperate and co-evolve. Our experiments show that multi-swarm programs possess better docking robustness than PSOVina. Moreover, the multi-swarm program based on random drift PSO can achieve the best highest accuracy of protein-ligand docking, an outstanding enrichment effect for drug-like activate compounds, and the second best AUC screening accuracy among all the compared docking programs, but with less computation consumption than most of the other docking programs.

CONCLUSION

The proposed multi-swarm cooperative model is a novel algorithmic modeling suitable for protein-ligand docking and virtual screening. Owing to the existing coevolution between the master and the slave swarms, this model in parallel generates remarkable docking performance. The source code can be freely downloaded from https://github.com/li-jin-xing/MPSOVina .

Large-scale comparison between the diffraction-component precision indexes favors Cruickshank’s Rfree function

This study aims to provide a first large-scale comparison between the various diffraction-component precision index (DPI) equations, assess the applicability of the parameter, and make recommendations on DPI computation. The DPI estimates the average accuracy of the atomic coordinates obtained by the structural refinement of protein diffraction data, with application in crystallography and cheminformatics. Although, Cruickshank and Blow proposed DPI equations based on R and Rfree in order to calculate DPI values, which remain scarcely employed in the quality assessment of the Protein Data Base (PDB) files, due to the unclear data extraction protocols (to assign variables), the complex equations, the lack of extensive applicability studies and the limited access to automated computations. In order to address these shortcomings, the entire RCSB PDB database was evaluated using Cruickshank?s and Blow?s R and Rfree DPI variations. Computations of 143070 X-ray structures indicate that Rfree-based DPI equations apply to 30 % more protein structures compared to R-based DPI equations, with Cruickshank Rfree-based DPI (CRF) exceeding the number of successful Blow?s Rfree-based DPI (BRF) computations. Although our results indicate that, in general, the resolutions < 2 ? assure consistency among the various DPIs computations (differences <0.05 ?), we recommend the use of CRF DPI because of its wider applicability.

MSLDOCK: Multi-Swarm Optimization for Flexible Ligand Docking and Virtual Screening

Autodock and its various variants are widely utilized docking approaches, which adopt optimization methods as search algorithms for flexible ligand docking and virtual screening. However, many of them have their limitations, such as poor accuracy for dockings with highly flexible ligands and low docking efficiency. In this paper, a multi-swarm optimization algorithm integrated with Autodock environment is proposed to design a high-performance and high-efficiency docking program, namely, MSLDOCK. The search algorithm is a combination of the random drift particle swarm optimization with a novel multi-swarm strategy and the Solis and Wets local search method with a modified implementation. Due to the algorithm's structure, MSLDOCK also has a multithread mode. The experimental results reveal that MSLDOCK outperforms other two Autodock-based approaches in many aspects, such as self-docking, cross-docking, and virtual screening accuracies as well as docking efficiency. Moreover, compared with three non-Autodock-based docking programs, MSLDOCK can be a reliable choice for self-docking and virtual screening, especially for dealing with highly flexible ligand docking problems. The source code of MSLDOCK can be downloaded for free from https://github.com/lcmeteor/MSLDOCK.

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Structure- and Ligand-Based Virtual Screening on DUD-E+: Performance Dependence on Approximations to the Binding Pocket

Using the DUD-E⁺ benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets, ligands, and sets thereof. For both structure-based and ligand-based approaches, the precise characterization of the binding site in question had a significant impact on screening performance. Using the single original DUD-E protein, Surflex-Dock yielded mean ROC area of 0.81 ± 0.11. Using the cognate ligand instead, with the eSim method for screening, yielded 0.77 ± 0.14. Moving to ensembles of five protein pocket variants increased docking performance to 0.84 ± 0.09. Results for the analogous ligand-based approach (using the five crystallographically aligned cognate ligands) was 0.83 ± 0.11. Using the same ligands, but making use of an automatically generated mutual alignment, yielded mean AUC nearly as good as from single-structure docking: 0.80 ± 0.12. Detailed results and statistical analyses show that structure- and ligand-based methods are complementary and can be fruitfully combined to enhance screening efficiency. A hybrid approach combining ensemble docking with eSim-based screening produced the best and most consistent performance (mean ROC area of 0.89 ± 0.08 and 1% early enrichment of 46-fold). Based on results from both the docking and ligand-similarity approaches, it is clearly unwise to make use of a single arbitrarily chosen protein structure for docking or single ligand query for similarity-based screening.

Electrostatic-field and surface-shape similarity for virtual screening and pose prediction

We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called "eSim"). Rather than employing heuristic "colors" or user-defined molecular feature types to represent conformation-dependent molecular electrostatics, eSim calculates the similarity of the electrostatic fields of two molecules (in addition to shape and hydrogen-bonding). We present detailed virtual screening performance data on the standard 102 target DUD-E set. In its moderately fast screening mode, eSim running on a single computing core is capable of processing over 60 molecules per second. In this mode, eSim performed significantly better than all alternate methods for which full DUD-E data were available (mean ROC area of 0.74, p [Formula: see text], by paired t-test, compared with the best performing alternate method). In addition, for 92 targets of the DUD-E set where multiple ligand-bound crystal structures were available, screening performance was assessed using alternate ligands or sets thereof (in their bound poses) as similarity targets. Using the joint alignment of five ligands for each protein target, mean ROC area exceeded 0.82 for the 92 targets. Design-focused application of ligand similarity methods depends on accurate predictions of geometric molecular relationships. We comprehensively assessed pose prediction accuracy by curating nearly 400,000 bound ligand pose pairs across the DUD-E targets. Overall, beginning from agnostic initial poses, we observed an 80% success rate for RMSD [Formula: see text] Å  among the top 20 predicted eSim poses. These examples were split roughly 50/50 into cases with high direct atomic overlap (where a shared scaffold exists between a pair) and low direct atomic overlap (where where a ligand pair has dissimilar scaffolds but largely occupies the same space). Within the high direct atomic overlap subset, the pose prediction success rate was 93%. For the more challenging subset (where dissimilar scaffolds are to be aligned), the success rate was 70%. The eSim approach enables both large-scale screening and rational design of ligands and is rooted in physically meaningful, non-heuristic, molecular comparisons.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets

Drug discovery is a long and difficult process that benefits from the integration of virtual screening methods in experimental screening campaigns such as to generate testable hypotheses, accelerate and/or reduce the cost of drug development. Current drug attrition rate is still a major issue in all therapeutic areas and especially in the field of cancer. Drug repositioning as well as the screening of natural compounds constitute promising approaches to accelerate and improve the success rate of drug discovery. We developed three compounds libraries of purchasable compounds: Drugs-lib, FOOD-lib and NP-lib that contain approved drugs, food constituents and natural products, respectively, that are optimized for structure-based virtual screening studies. The three compounds libraries are implemented in the MTiOpenScreen web server that allows users to perform structure-based virtual screening computations on their selected protein targets. The server outputs a list of 1,500 molecules with predicted binding scores that can then be processed further by the users and purchased for experimental validation. To illustrate the potential of our service for drug repositioning endeavours, we selected five recently published drugs that have been repositioned in vitro and/or in vivo on cancer targets. For each drug, we used the MTiOpenScreen service to screen the Drugs-lib collection against the corresponding anti-cancer target and we show that our protocol is able to rank these drugs within the top ranked compounds. This web server should assist the discovery of promising molecules that could benefit patients, with faster development times, and reduced costs and risk.

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

Structural Features and Ligand Selectivity for 10 Intermediates in the Activation Process of β2-Adrenergic Receptor

It has already been suggested by researchers that there should be multiple intermediate states in the activation process for G-protein-coupled receptors (GPCRs). However, the intermediate states are very short-lived and hardly captured by the experiments, leading to very limited understanding of their structural features and drug efficacies. In this work, a novel joint strategy of targeted molecular dynamics simulation, conventional molecular dynamics simulation, and virtual screening is developed to address the problems. The results from 10 intermediate conformations obtained from the work reveal that the ligand pocket is very unstable and fluctuates between the inactive state and the active one in the case of ligand-free, in particular for ECL2 as a gate-keeper of the ligand-binding. The ligand-binding site could be stable in the active state with a small volume and a completely closed ECL2, only when the G-protein-binding region is fully activated. In addition, the activations of the ligand-binding pocket and G-protein-binding site are relatively independent and exhibit a loose allosteric coupling, which contributes to the existence of multiple intermediate conformations. Interestingly, the screening performance of the agonists does not increase on increasing the overall activity of the intermediate state, but is dependent on the activated extent of the ligand pocket. The receptor is prone to bind the agonist when closing ECL2 and reducing the ligand-binding pocket volume, whereas it is more favorable for binding the antagonist when opening ECL2 and increasing the pocket volume. These observations added to previous studies could help us better understand the activation mechanism of GPCRs and provide valuable information for drug design.

Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.

An in silico high-throughput screen identifies potential selective inhibitors for the non-receptor tyrosine kinase Pyk2

The non-receptor tyrosine kinase proline-rich tyrosine kinase 2 (Pyk2) is a critical mediator of signaling from cell surface growth factor and adhesion receptors to cell migration, proliferation, and survival. Emerging evidence indicates that signaling by Pyk2 regulates hematopoietic cell response, bone density, neuronal degeneration, angiogenesis, and cancer. These physiological and pathological roles of Pyk2 warrant it as a valuable therapeutic target for invasive cancers, osteoporosis, Alzheimer’s disease, and inflammatory cellular response. Despite its potential as a therapeutic target, no potent and selective inhibitor of Pyk2 is available at present. As a first step toward discovering specific potential inhibitors of Pyk2, we used an in silico high-throughput screening approach. A virtual library of six million lead-like compounds was docked against four different high-resolution Pyk2 kinase domain crystal structures and further selected for predicted potency and ligand efficiency. Ligand selectivity for Pyk2 over focal adhesion kinase (FAK) was evaluated by comparative docking of ligands and measurement of binding free energy so as to obtain 40 potential candidates. Finally, the structural flexibility of a subset of the docking complexes was evaluated by molecular dynamics simulation, followed by intermolecular interaction analysis. These compounds may be considered as promising leads for further development of highly selective Pyk2 inhibitors.

The power metric: a new statistically robust enrichment-type metric for virtual screening applications with early recovery capability

A new metric for the evaluation of model performance in the field of virtual screening and quantitative structure-activity relationship applications is described. This metric has been termed the power metric and is defined as the fraction of the true positive rate divided by the sum of the true positive and false positive rates, for a given cutoff threshold. The performance of this metric is compared with alternative metrics such as the enrichment factor, the relative enrichment factor, the receiver operating curve enrichment factor, the correct classification rate, Matthews correlation coefficient and Cohen's kappa coefficient. The performance of this new metric is found to be quite robust with respect to variations in the applied cutoff threshold and ratio of the number of active compounds to the total number of compounds, and at the same time being sensitive to variations in model quality. It possesses the correct characteristics for its application in early-recognition virtual screening problems.

Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance

Background

In a structure-based virtual screening, the choice of the docking program is essential for the success of a hit identification. Benchmarks are meant to help in guiding this choice, especially when undertaken on a large variety of protein targets. Here, the performance of four popular virtual screening programs, Gold, Glide, Surflex and FlexX, is compared using the Directory of Useful Decoys-Enhanced database (DUD-E), which includes 102 targets with an average of 224 ligands per target and 50 decoys per ligand, generated to avoid biases in the benchmarking. Then, a relationship between these program performances and the properties of the targets or the small molecules was investigated.

Results

The comparison was based on two metrics, with three different parameters each. The BEDROC scores with α = 80.5, indicated that, on the overall database, Glide succeeded (score > 0.5) for 30 targets, Gold for 27, FlexX for 14 and Surflex for 11. The performance did not depend on the hydrophobicity nor the openness of the protein cavities, neither on the families to which the proteins belong. However, despite the care in the construction of the DUD-E database, the small differences that remain between the actives and the decoys likely explain the successes of Gold, Surflex and FlexX. Moreover, the similarity between the actives of a target and its crystal structure ligand seems to be at the basis of the good performance of Glide. When all targets with significant biases are removed from the benchmarking, a subset of 47 targets remains, for which Glide succeeded for only 5 targets, Gold for 4 and FlexX and Surflex for 2.

Conclusion

The performance dramatic drop of all four programs when the biases are removed shows that we should beware of virtual screening benchmarks, because good performances may be due to wrong reasons. Therefore, benchmarking would hardly provide guidelines for virtual screening experiments, despite the tendency that is maintained, i.e., Glide and Gold display better performance than FlexX and Surflex. We recommend to always use several programs and combine their results. Graphical Abstract

3D-Lab: a collaborative web-based platform for molecular modeling

AIM

The use of 3D information has shown impact in numerous applications in drug design. However, it is often under-utilized and traditionally limited to specialists. We want to change that, and present an approach making 3D information and molecular modeling accessible and easy-to-use 'for the people'.

METHODOLOGY/RESULTS

A user-friendly and collaborative web-based platform (3D-Lab) for 3D modeling, including a blazingly fast virtual screening capability, was developed. 3D-Lab provides an interface to automatic molecular modeling, like conformer generation, ligand alignments, molecular dockings and simple quantum chemistry protocols. 3D-Lab is designed to be modular, and to facilitate sharing of 3D-information to promote interactions between drug designers. Recent enhancements to our open-source virtual reality tool Molecular Rift are described.

CONCLUSION

The integrated drug-design platform allows drug designers to instantaneously access 3D information and readily apply advanced and automated 3D molecular modeling tasks, with the aim to improve decision-making in drug design projects.

Importance of consensus region of multiple-ligand templates in a virtual screening method

We discuss methods and ideas of virtual screening (VS) for drug discovery by examining the performance of VS-APPLE, a recently developed VS method, which extensively utilizes the tendency of single binding pockets to bind diversely different ligands, i.e. promiscuity of binding pockets. In VS-APPLE, multiple ligands bound to a pocket are spatially arranged by maximizing structural overlap of the protein while keeping their relative position and orientation with respect to the pocket surface, which are then combined into a multiple-ligand template for screening test compounds. To greatly reduce the computational cost, comparison of test compound structures are made only with limited regions of the multiple-ligand template. Even when we use the narrow regions with most densely populated atoms for the comparison, VSAPPLE outperforms other conventional VS methods in terms of Area Under the Curve (AUC) measure. This region with densely populated atoms corresponds to the consensus region among multiple ligands. It is typically observed that expansion of the sampled region including more atoms improves screening efficiency. However, for some target proteins, considering only a small consensus region is enough for the effective screening of test compounds. These results suggest that the performance test of VS methods sheds light on the mechanisms of protein-ligand interactions, and elucidation of the protein-ligand interactions should further help improvement of VS methods.

CrossDocker: a tool for performing cross-docking using Autodock Vina

Background

Cross-docking is an approach to find the best holo structures among multiple structures available for a target protein.

Results

CrossDocker significantly decreases the time needed for setting parameters and inputs for performing multiple dockings, data collection and subsequent analysis.

Conclusion

CrossDocker was written in Python language and is available as executable binary for Windows operating system. It is available at http://www.pharm-sbg.com. Some example data sets were also provided.

Discover natural compounds as potential phosphodiesterase-4B inhibitors via computational approaches

cAMP, intracellular cyclic adenosine monophosphate, is a ubiquitous second messenger that plays a key role in many physiological processes. PDE4B which can reduce the cAMP level by hydrolyzing cAMP to 5'-AMP has become a therapeutic target for the treatment of human diseases such as respiratory disorders, inflammation diseases, neurological and psychiatric disorders. However, the use of currently available PDE4B inhibitors is restricted due to serious side effects caused by targeting PDE4D. Hence, we are attempting to find out subfamily-selective PDE4B inhibitors from natural products, using computer-aided approaches such as virtual screening, docking, and molecular dynamics simulation. Finally, four potential PDE4B-selective inhibitors (ZINC67912770, ZINC67912780, ZINC72320169, and ZINC28882432) were found. Compared to the reference drug (roflumilast), they scored better during the virtual screening process. Binding free energy for them was -317.51, -239.44, -215.52, and -165.77 kJ/mol, better than -129.05 kJ/mol of roflumilast. The pharmacophore model of the four candidate inhibitors comprised six features, including one hydrogen bond donor, four hydrogen bond acceptors, and one aromatic ring feature. It is expected that our study will pave the way for the design of potent PDE4B-selective inhibitors of new drugs to treat a wide variety of diseases such as asthma, COPD, psoriasis, depression, etc.

Models of protein-ligand crystal structures: trust, but verify

X-ray crystallography provides the most accurate models of protein-ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein-ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein-ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein-ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein-ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein-ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein-ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.

Benchmarking Data Sets for the Evaluation of Virtual Ligand Screening Methods: Review and Perspectives

Virtual screening methods are commonly used nowadays in drug discovery processes. However, to ensure their reliability, they have to be carefully evaluated. The evaluation of these methods is often realized in a retrospective way, notably by studying the enrichment of benchmarking data sets. To this purpose, numerous benchmarking data sets were developed over the years, and the resulting improvements led to the availability of high quality benchmarking data sets. However, some points still have to be considered in the selection of the active compounds, decoys, and protein structures to obtain optimal benchmarking data sets.

Online_DPI: a web server to calculate the diffraction precision index for a protein structure

An online computing server,Online_DPI(where DPI denotes the diffraction precision index), has been created to calculate the `Cruickshank DPI' value for a given three-dimensional protein or macromolecular structure. It also estimates the atomic coordinate error for all the atoms available in the structure. It is an easy-to-use web server that enables users to visualize the computed values dynamically on the client machine. Users can provide the Protein Data Bank (PDB) identification code or upload the three-dimensional atomic coordinates from the client machine. The computed DPI value for the structure and the atomic coordinate errors for all the atoms are included in the revised PDB file. Further, users can graphically view the atomic coordinate error along with `temperature factors' (i.e.atomic displacement parameters). In addition, the computing engine is interfaced with an up-to-date local copy of the Protein Data Bank. New entries are updated every week, and thus users can access all the structures available in the Protein Data Bank. The computing engine is freely accessible online at http://cluster.physics.iisc.ernet.in/dpi/.

Magnesium-binding architectures in RNA crystal structures: validation, binding preferences, classification and motif detection

The ubiquitous presence of magnesium ions in RNA has long been recognized as a key factor governing RNA folding, and is crucial for many diverse functions of RNA molecules. In this work, Mg(2+)-binding architectures in RNA were systematically studied using a database of RNA crystal structures from the Protein Data Bank (PDB). Due to the abundance of poorly modeled or incorrectly identified Mg(2+) ions, the set of all sites was comprehensively validated and filtered to identify a benchmark dataset of 15 334 'reliable' RNA-bound Mg(2+) sites. The normalized frequencies by which specific RNA atoms coordinate Mg(2+) were derived for both the inner and outer coordination spheres. A hierarchical classification system of Mg(2+) sites in RNA structures was designed and applied to the benchmark dataset, yielding a set of 41 types of inner-sphere and 95 types of outer-sphere coordinating patterns. This classification system has also been applied to describe six previously reported Mg(2+)-binding motifs and detect them in new RNA structures. Investigation of the most populous site types resulted in the identification of seven novel Mg(2+)-binding motifs, and all RNA structures in the PDB were screened for the presence of these motifs.

Discovery of TRAF-2 and NCK-interacting kinase (TNIK) inhibitors by ligand-based virtual screening methods

TRAF-2 and NCK-interacting kinase (TNIK) is a serine–threonine kinase with a proposed role in Wnt/β-catenin and JNK pathways.

Benchmarking methods and data sets for ligand enrichment assessment in virtual screening

Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. "analogue bias", "artificial enrichment" and "false negative". In addition, we introduce our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylases (HDACs) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The leave-one-out cross-validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased as measured by property matching, ROC curves and AUCs.

Improving the use of ranking in virtual screening against HIV-1 integrase with triangular numbers and including ligand profiling with antitargets

A delicate balance exists between a drug molecule's toxicity and its activity. Indeed, efficacy, toxicity, and side effect problems are a common cause for the termination of drug candidate compounds and development projects. To address this, an antitarget interaction profile is built and combined with virtual screening and cross docking for new inhibitors of HIV-1 integrase, in order to consider possible off-target interactions as early as possible in a drug or hit discovery program. New ranking techniques using triangular numbers improve ranking information on the compounds and recovery of known inhibitors into the top compounds using different docking programs. This improved ranking arises from using consensus of ranks between docking programs and ligand efficiencies to derive a new rank, instead of using absolute score values, or average of ranks. The triangular number rerank also allowed the objective combination of results from several protein targets or screen conditions and several programs. Triangular number reranking conserves more information than other reranking methods such as average of scores or averages of ranks. In addition, the use of triangular numbers for reranking makes possible the use of thresholds with a justified leeway based on the number of available known inhibitors, so that the majority of the compounds above the threshold in ranks compare to the compounds that have known experimentally determined biological activity. The battery of anti- or off-targets can be tailored to specific molecular or drug design challenges. In silico filters can thus be deployed in successive stages, for prefiltering, activity profiling, and for further analysis and triaging of libraries of compounds.

Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility

In this study, to accommodate receptor flexibility, based on multiple receptor conformations, a novel ensemble docking protocol was developed by using the naïve Bayesian classification technique, and it was evaluated in terms of the prediction accuracy of docking-based virtual screening (VS) of three important targets in the kinase family: ALK, CDK2, and VEGFR2. First, for each target, the representative crystal structures were selected by structural clustering, and the capability of molecular docking based on each representative structure to discriminate inhibitors from non-inhibitors was examined. Then, for each target, 50 ns molecular dynamics (MD) simulations were carried out to generate an ensemble of the conformations, and multiple representative structures/snapshots were extracted from each MD trajectory by structural clustering. On average, the representative crystal structures outperform the representative structures extracted from MD simulations in terms of the capabilities to separate inhibitors from non-inhibitors. Finally, by using the naïve Bayesian classification technique, an integrated VS strategy was developed to combine the prediction results of molecular docking based on different representative conformations chosen from crystal structures and MD trajectories. It was encouraging to observe that the integrated VS strategy yields better performance than the docking-based VS based on any single rigid conformation. This novel protocol may provide an improvement over existing strategies to search for more diverse and promising active compounds for a target of interest.

ColBioS-FlavRC: a collection of bioselective flavonoids and related compounds filtered from high-throughput screening outcomes

Flavonoids, the vastest class of natural polyphenols, are extensively investigated for their multiple benefits on human health. Due to their physicochemical or biological properties, many representatives are considered to exhibit low selectivity among various protein targets or to plague high-throughput screening (HTS) outcomes. The aim of this study is to highlight reliable, bioselective compounds sharing flavonoidic scaffolds in HTS experiments. A filtering scheme was applied to remove undesired flavonoids (and related compounds) from confirmatory PubChem bioassays. A number of 433 compounds addressing various protein targets form the core of the collection of bioselective flavonoids and related compounds (ColBioS-FlavRC). With an additional set of 2908 inactive related compounds, ColBioS-FlavRC offers the grounds for method optimization and validation. We exemplified the use of ColBioS-FlavRC by pharmacophore modeling, subsequently (externally) validated for virtual screening purposes. The early enrichment capabilities of the pharmacophore hypotheses were measured by means of the median exponential retriever operating curve enrichment (MeROCE), a suited metric in comparative evaluations of virtual screening methods. ColBioS-FlavRC is available in the Supporting Information and is freely accessible for further studies.