Detailed analysis of scoring functions for virtual screening

Docking-based comparative intermolecular contacts analysis as new 3-D QSAR concept for validating docking studies and in silico screening: NMT and GP inhibitors as case studies

The significant role played by docking algorithms in drug discovery combined with their serious pitfalls prompted us to envisage a novel concept for validating docking solutions, namely, docking-based comparative intermolecular contacts analysis (dbCICA). This novel approach is based on the number and quality of contacts between docked ligands and amino acid residues within the binding pocket. It assesses a particular docking configuration on the basis of its ability to align a set of ligands within a corresponding binding pocket in such a way that potent ligands come into contact with binding site spots distinct from those approached by low-affinity ligands and vice versa. In other words, dbCICA evaluates the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models can be translated into valid pharmacophore models that can be used as 3-D search queries to mine structural databases for new bioactive compounds. dbCICA was implemented to search for new inhibitors of candida N-myristoyl transferase as potential antifungal agents and glycogen phosphorylase (GP) inhibitors as potential antidiabetic agents. The process culminated in five selective micromolar antifungal leads and nine GP inhibitory leads.

FRED pose prediction and virtual screening accuracy

Results of a previous docking study are reanalyzed and extended to include results from the docking program FRED and a detailed statistical analysis of both structure reproduction and virtual screening results. FRED is run both in a traditional docking mode and in a hybrid mode that makes use of the structure of a bound ligand in addition to the protein structure to screen molecules. This analysis shows that most docking programs are effective overall but highly inconsistent, tending to do well on one system and poorly on the next. Comparing methods, the difference in mean performance on DUD is found to be statistically significant (95% confidence) 61% of the time when using a global enrichment metric (AUC). Early enrichment metrics are found to have relatively poor statistical power, with 0.5% early enrichment only able to distinguish methods to 95% confidence 14% of the time.

A unified, probabilistic framework for structure- and ligand-based virtual screening

We present a probabilistic framework for interpreting structure-based virtual screening that returns a quantitative likelihood of observing bioactivity and can be quantitatively combined with ligand-based screening methods to yield a cumulative prediction that consistently outperforms any single screening metric. The approach has been developed and validated on more than 30 different protein targets. Transforming structure-based in silico screening results into robust probabilities of activity enables the general fusion of multiple structure- and ligand-based approaches and returns a quantitative expectation of success that can be used to prioritize (or deprioritize) further discovery activities. This unified probabilistic framework offers a paradigm shift in how docking and scoring results are interpreted, which can enhance early lead-finding efforts by maximizing the value of in silico computational tools.

Small molecule inhibitors of histone acetyltransferase Tip60

Tip60 is a key member of the MYST family of histone acetyltransferases and involved in a broad spectrum of cellular pathways and disease conditions. So far, small molecule inhibitors of Tip60 and other members of MYST HATs are rarely reported. To discover new small molecule inhibitors of Tip60 as mechanistic tools for functional study and as chemical leads for therapeutic development, we performed virtual screening using the crystal structure of Esa1 (the yeast homolog of Tip60) on a small molecule library database. Radioactive acetylation assays were carried out to further evaluate the virtual screen hits. Several compounds with new structural scaffolds were identified with micromolar inhibition potency for Tip60 from the biochemical studies. Further, computer modeling and kinetic assays suggest that these molecules target the acetyl-CoA binding site in Tip60. These new inhibitors provide valuable chemical hits to develop further potent inhibitors for the MYST HATs.

Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method

BACKGROUND

The reliable and robust estimation of ligand binding affinity continues to be a challenge in drug design. Many current methods rely on molecular mechanics (MM) calculations which do not fully explain complex molecular interactions. Full quantum mechanical (QM) computation of the electronic state of protein-ligand complexes has recently become possible by the latest advances in the development of linear-scaling QM methods such as the ab initio fragment molecular orbital (FMO) method. This approximate molecular orbital method is sufficiently fast that it can be incorporated into the development cycle during structure-based drug design for the reliable estimation of ligand binding affinity. Additionally, the FMO method can be combined with approximations for entropy and solvation to make it applicable for binding affinity prediction for a broad range of target and chemotypes.

RESULTS

We applied this method to examine the binding affinity for a series of published cyclin-dependent kinase 2 (CDK2) inhibitors. We calculated the binding affinity for 28 CDK2 inhibitors using the ab initio FMO method based on a number of X-ray crystal structures. The sum of the pair interaction energies (PIE) was calculated and used to explain the gas-phase enthalpic contribution to binding. The correlation of the ligand potencies to the protein-ligand interaction energies gained from FMO was examined and was seen to give a good correlation which outperformed three MM force field based scoring functions used to appoximate the free energy of binding. Although the FMO calculation allows for the enthalpic component of binding interactions to be understood at the quantum level, as it is an in vacuo single point calculation, the entropic component and solvation terms are neglected. For this reason a more accurate and predictive estimate for binding free energy was desired. Therefore, additional terms used to describe the protein-ligand interactions were then calculated to improve the correlation of the FMO derived values to experimental free energies of binding. These terms were used to account for the polar and non-polar solvation of the molecule estimated by the Poisson-Boltzmann equation and the solvent accessible surface area (SASA), respectively, as well as a correction term for ligand entropy. A quantitative structure-activity relationship (QSAR) model obtained by Partial Least Squares projection to latent structures (PLS) analysis of the ligand potencies and the calculated terms showed a strong correlation (r2 = 0.939, q2 = 0.896) for the 14 molecule test set which had a Pearson rank order correlation of 0.97. A training set of a further 14 molecules was well predicted (r2 = 0.842), and could be used to obtain meaningful estimations of the binding free energy.

CONCLUSIONS

Our results show that binding energies calculated with the FMO method correlate well with published data. Analysis of the terms used to derive the FMO energies adds greater understanding to the binding interactions than can be gained by MM methods. Combining this information with additional terms and creating a scaled model to describe the data results in more accurate predictions of ligand potencies than the absolute values obtained by FMO alone.

PHOENIX: a scoring function for affinity prediction derived using high-resolution crystal structures and calorimetry measurements

Binding affinity prediction is one of the most critical components to computer-aided structure-based drug design. Despite advances in first-principle methods for predicting binding affinity, empirical scoring functions that are fast and only relatively accurate are still widely used in structure-based drug design. With the increasing availability of X-ray crystallographic structures in the Protein Data Bank and continuing application of biophysical methods such as isothermal titration calorimetry to measure thermodynamic parameters contributing to binding free energy, sufficient experimental data exists that scoring functions can now be derived by separating enthalpic (ΔH) and entropic (TΔS) contributions to binding free energy (ΔG). PHOENIX, a scoring function to predict binding affinities of protein-ligand complexes, utilizes the increasing availability of experimental data to improve binding affinity predictions by the following: model training and testing using high-resolution crystallographic data to minimize structural noise, independent models of enthalpic and entropic contributions fitted to thermodynamic parameters assumed to be thermodynamically biased to calculate binding free energy, use of shape and volume descriptors to better capture entropic contributions. A set of 42 descriptors and 112 protein-ligand complexes were used to derive functions using partial least-squares for change of enthalpy (ΔH) and change of entropy (TΔS) to calculate change of binding free energy (ΔG), resulting in a predictive r2 (r(pred)2) of 0.55 and a standard error (SE) of 1.34 kcal/mol. External validation using the 2009 version of the PDBbind "refined set" (n = 1612) resulted in a Pearson correlation coefficient (R(p)) of 0.575 and a mean error (ME) of 1.41 pK(d). Enthalpy and entropy predictions were of limited accuracy individually. However, their difference resulted in a relatively accurate binding free energy. While the development of an accurate and applicable scoring function was an objective of this study, the main focus was evaluation of the use of high-resolution X-ray crystal structures with high-quality thermodynamic parameters from isothermal titration calorimetry for scoring function development. With the increasing application of structure-based methods in molecular design, this study suggests that using high-resolution crystal structures, separating enthalpy and entropy contributions to binding free energy, and including descriptors to better capture entropic contributions may prove to be effective strategies toward rapid and accurate calculation of binding affinity.

Docking methods for structure-based library design

The drug discovery process mainly relies on the experimental high-throughput screening of huge compound libraries in their pursuit of new active compounds. However, spiraling research and development costs and unimpressive success rates have driven the development of more rational, efficient, and cost-effective methods. With the increasing availability of protein structural information, advancement in computational algorithms, and faster computing resources, in silico docking-based methods are increasingly used to design smaller and focused compound libraries in order to reduce screening efforts and costs and at the same time identify active compounds with a better chance of progressing through the optimization stages. This chapter is a primer on the various docking-based methods developed for the purpose of structure-based library design. Our aim is to elucidate some basic terms related to the docking technique and explain the methodology behind several docking-based library design methods. This chapter also aims to guide the novice computational practitioner by laying out the general steps involved for such an exercise. Selected successful case studies conclude this chapter.

Identification of non-peptide malignant brain tumor (MBT) repeat antagonists by virtual screening of commercially available compounds

The malignant brain tumor (MBT) repeat is an important epigenetic-code "reader" and is functionally associated with differentiation, gene silencing, and tumor suppression. (1-3) Small molecule probes of MBT domains should enable a systematic study of MBT-containing proteins and potentially reveal novel druggable targets. We designed and applied a virtual screening strategy that identified potential MBT antagonists in a large database of commercially available compounds. A small set of virtual hits was purchased and submitted to experimental testing. Nineteen of the purchased compounds showed a specific dose-dependent protein binding and will provide critical structure-activity information for subsequent lead generation and optimization.

Exploring the conformational changes of the ATP binding site of gyrase B from Escherichia coli complexed with different established inhibitors by using molecular dynamics simulation: protein-ligand interactions in the light of the alanine scanning and free energy decomposition methods

Currently, bacterial diseases cause a death toll around 2 million people a year encouraging the search for new antimicrobial agents. DNA gyrase is a well-established antibacterial target consisting of two subunits, GyrA and GyrB, in a heterodimer A(2)B(2). GyrA is involved in DNA breakage and reunion and GyrB catalyzes the hydrolysis of ATP. The GyrB subunit from Escherichia coli has been investigated, namely the ATP binding pocket both considering the protein without ligands and bound with the inhibitors clorobiocin, novobiocin and 5'-adenylyl-β-γ-imidodiphosphate. The stability of the systems was studied by molecular dynamics simulation with the further analysis of the time dependent root-mean-square coordinate deviation (RMSD) from the initial structure, and temperature factors. Moreover, exploration of the conformational space of the systems during the MD simulation was carried out by a clustering data mining technique using the average-linkage algorithm. Recognizing the key residues in the binding site of the enzyme that are involved in the binding mode with the aforementioned inhibitors was investigated by using two techniques: free energy decomposition and computational alanine scanning. The results from these simulations highlight the important residues in the ATP binding site and can be useful in the design process of potential new inhibitors.

Computational determination of binding structures and free energies of phosphodiesterase-2 with benzo[1,4]diazepin-2-one derivatives

Phosphodiesterase-2 (PDE2) is a key enzyme catalyzing hydrolysis of both cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) that serve as intracellular second messengers. PDE2 has been recognized as an attractive drug target, and selective inhibitors of PDE2 are expected to be promising candidates for the memory enhancer, antidepressant, and anxiolytic agent. In the present study, we examined the detailed binding structures and free energies for PDE2 interacting with a promising series of inhibitors, i.e., benzo[1,4]diazepin-2-one derivatives, by carrying out molecular docking, molecular dynamics (MD) simulations, binding free energy calculations, and binding energy decompositions. The computational results provide valuable insights into the detailed enzyme-inhibitor binding modes including important intermolecular interactions, e.g., the π-π stacking interactions with the common benzo[1,4]diazepin-2-one scaffold of the inhibitors, hydrogen bonding and hydrophobic interactions with the substituents on the benzo[1,4]diazepin-2-one scaffold. Future rational design of new, more potent inhibitors of PDE2 should carefully account for all of these favorable intermolecular interactions. By use of the MD-simulated binding structures, the calculated binding free energies are in good agreement with the experimental activity data for all of the examined benzo[1,4]diazepin-2-one derivatives. The enzyme-inhibitor binding modes determined and the agreement between the calculated and experimental results are expected to be valuable for future rational design of more potent inhibitors of PDE2.

Structure-based virtual screening and electrophysiological evaluation of new chemotypes of K(v)1.5 channel blockers

Atrial fibrillation (AF) is the most prevalent nonfatal cardiac rhythm disorder associated with an increased risk of heart failure and stroke. Considering the ventricular side effects induced by anti-arrhythmic agents in current use, K(v)1.5 channel blockers have attracted a great deal of deliberation owing to their selective actions on atrial electrophysiology. Herein we report new chemotypes of K(v)1.5 channel blockers that were identified through a combination of structure-based virtual screening and in silico druglike property prediction including six scoring functions, as well as electrophysiological evaluation. Among them, five of the 18 compounds exhibited >50 % blockade ratio at 10 microM, and have structural features different from conventional K(v)1.5 channel blockers. These novel scaffolds could serve as hits for further optimization and SAR studies for the discovery of selective agents to treat AF.

When is chemical similarity significant? The statistical distribution of chemical similarity scores and its extreme values

As repositories of chemical molecules continue to expand and become more open, it becomes increasingly important to develop tools to search them efficiently and assess the statistical significance of chemical similarity scores. Here, we develop a general framework for understanding, modeling, predicting, and approximating the distribution of chemical similarity scores and its extreme values in large databases. The framework can be applied to different chemical representations and similarity measures but is demonstrated here using the most common binary fingerprints with the Tanimoto similarity measure. After introducing several probabilistic models of fingerprints, including the Conditional Gaussian Uniform model, we show that the distribution of Tanimoto scores can be approximated by the distribution of the ratio of two correlated Normal random variables associated with the corresponding unions and intersections. This remains true also when the distribution of similarity scores is conditioned on the size of the query molecules to derive more fine-grained results and improve chemical retrieval. The corresponding extreme value distributions for the maximum scores are approximated by Weibull distributions. From these various distributions and their analytical forms, Z-scores, E-values, and p-values are derived to assess the significance of similarity scores. In addition, the framework also allows one to predict the value of standard chemical retrieval metrics, such as sensitivity and specificity at fixed thresholds, or receiver operating characteristic (ROC) curves at multiple thresholds, and to detect outliers in the form of atypical molecules. Numerous and diverse experiments that have been performed, in part with large sets of molecules from the ChemDB, show remarkable agreement between theory and empirical results.

Scoring functions and their evaluation methods for protein-ligand docking: recent advances and future directions

The scoring function is one of the most important components in structure-based drug design. Despite considerable success, accurate and rapid prediction of protein-ligand interactions is still a challenge in molecular docking. In this perspective, we have reviewed three basic types of scoring functions (force-field, empirical, and knowledge-based) and the consensus scoring technique that are used for protein-ligand docking. The commonly-used assessment criteria and publicly available protein-ligand databases for performance evaluation of the scoring functions have also been presented and discussed. We end with a discussion of the challenges faced by existing scoring functions and possible future directions for developing improved scoring functions.

Advances and challenges in protein-ligand docking

Molecular docking is a widely-used computational tool for the study of molecular recognition, which aims to predict the binding mode and binding affinity of a complex formed by two or more constituent molecules with known structures. An important type of molecular docking is protein-ligand docking because of its therapeutic applications in modern structure-based drug design. Here, we review the recent advances of protein flexibility, ligand sampling, and scoring functions—the three important aspects in protein-ligand docking. Challenges and possible future directions are discussed in the Conclusion.

Application of a novel in silico high-throughput screen to identify selective inhibitors for protein-protein interactions

Increasing numbers of target protein structures available for computational studies makes the structure-based screening paradigm more attractive for initial hit indentification. We have developed a novel in silico screening methodology incorporating Molecular Mechanics (MM)/implicit solvent methods to evaluate binding free energies and applied this technology to the identification of inhibitors of the TLR4/MD-2 interaction.

Chemical validation of phosphodiesterase C as a chemotherapeutic target in Trypanosoma cruzi, the etiological agent of Chagas' disease

Trypanosoma cruzi phosphodiesterase (PDE) C (TcrPDEC), a novel and rather unusual PDE in which, unlike all other class I PDEs, the catalytic domain is localized in the middle of the polypeptide chain, is able to hydrolyze cyclic GMP (cGMP), although it prefers cyclic AMP (cAMP), and has a FYVE-type domain in its N-terminal region (S. Kunz et al., FEBS J. 272:6412-6422, 2005). TcrPDEC shows homology to the mammalian PDE4 family members. PDE4 inhibitors are currently under development for the treatment of inflammatory diseases, such as asthma, chronic pulmonary diseases, and psoriasis, and for treating depression and serving as cognitive enhancers. We therefore tested a number of compounds originally synthesized as potential PDE4 inhibitors on T. cruzi amastigote growth, and we obtained several useful hits. We then conducted homology modeling of T. cruzi PDEC and identified other compounds as potential inhibitors through virtual screening. Testing of these compounds against amastigote growth and recombinant TcrPDEC activity resulted in several potent inhibitors. The most-potent inhibitors were found to increase the cellular concentration of cAMP. Preincubation of cells in the presence of one of these compounds stimulated volume recovery after hyposmotic stress, in agreement with their TcrPDEC inhibitory activity in vitro, providing chemical validation of this target. The compounds found could be useful tools in the study of osmoregulation in T. cruzi. In addition, their further optimization could result in the development of new drugs against Chagas' disease and other trypanosomiases.

Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors

Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99% of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1% of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone.

De novo design: balancing novelty and confined chemical space

IMPORTANCE OF THE FIELD

De novo drug design serves as a tool for the discovery of new ligands for macromolecular targets as well as optimization of known ligands. Recently developed tools aim to address the multi-objective nature of drug design in an unprecedented manner.

AREAS COVERED IN THIS REVIEW

This article discusses recent advances in de novo drug design programs and accessory programs used to evaluate compounds post-generation.

WHAT THE READER WILL GAIN

The reader is introduced to the challenges inherent in de novo drug design and will become familiar with current trends in de novo design. Furthermore, the reader will be better prepared to assess the value of a tool, and be equipped to design more elegant tools in the future.

TAKE HOME MESSAGE

De novo drug design can assist in the efficient discovery of new compounds with a high affinity for a given target. The inclusion of existing chemoinformatic methods with current structure-based de novo design tools provides a means of enhancing the therapeutic value of these generated compounds.

Ligand-protein cross-docking with water molecules

The accuracy of ligand-protein docking may be affected by the presence of water molecules on the surface of the protein. Cross-docking simulations have been performed on a number of ligand-protein complexes for various proteins whose crystal structures contain water molecules in their binding sites. Only common sets of water molecules found in the binding site of the proteins were considered. A statistically significant overall increase in accuracy was observed when water molecules were included in cross-docking simulations. These results confirm the importance of including water molecules whenever possible in ligand-protein docking simulations.

Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.

Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analysed for their effectiveness in virtual screening against four different targets: angiotensin-converting enzyme (ACE), cyclooxygenase 2 (COX-2), thrombin and human immunodeficiency virus 1 (HIV-1) protease. The relative performance of the tools was compared by examining their ability to recognise known active compounds from a set of actives and nonactives. Furthermore, we investigated whether the application of different virtual-screening methods in parallel provides complementary or redundant hit lists. Docking was performed with GOLD, Glide, FlexX and Surflex. The obtained docking poses were rescored by using nine different scoring functions in addition to the scoring functions implemented as objective functions in the docking algorithms. Ligand-based virtual screening was done with ROCS (3D-similarity searching), Feature Trees and Scitegic Functional Fingerprints (2D-similarity searching). The results show that structure- and ligand-based virtual-screening methods provide comparable enrichments in detecting active compounds. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary. These results suggest that a parallel application of different structure- and ligand-based virtual-screening methods increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign.

Molecular docking screens using comparative models of proteins

Two orders of magnitude more protein sequences can be modeled by comparative modeling than have been determined by X-ray crystallography and NMR spectroscopy. Investigators have nevertheless been cautious about using comparative models for ligand discovery because of concerns about model errors. We suggest how to exploit comparative models for molecular screens, based on docking against a wide range of crystallographic structures and comparative models with known ligands. To account for the variation in the ligand-binding pocket as it binds different ligands, we calculate "consensus" enrichment by ranking each library compound by its best docking score against all available comparative models and/or modeling templates. For the majority of the targets, the consensus enrichment for multiple models was better than or comparable to that of the holo and apo X-ray structures. Even for single models, the models are significantly more enriching than the template structure if the template is paralogous and shares more than 25% sequence identity with the target.

High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins.

Lead discovery is one of the most important processes in rational drug design. To improve the rate of the detection of lead compounds, various technologies such as high-throughput screening and combinatorial chemistry have been introduced into the pharmaceutical industry. However, since these technologies alone may not improve lead productivity, computational screening has become important. A central method for computational screening is molecular docking. This method generally docks many flexible ligands to a rigid protein and predicts the binding affinity for each ligand in a practical time. However, its ability to detect lead compounds is less reliable. In contrast, molecular dynamics simulations can treat both proteins and ligands in a flexible manner, directly estimate the effect of explicit water molecules, and provide more accurate binding affinity, although their computational costs and times are significantly greater than those of molecular docking. Therefore, we developed a special purpose computer “MDGRAPE-3” for molecular dynamics simulations and applied it to computational screening. In this paper, we report an effective method for computational screening; this method is a combination of molecular docking and massive-scale molecular dynamics simulations. The proposed method showed a higher and more stable enrichment performance than the molecular docking method used alone.

Comparison of several molecular docking programs: pose prediction and virtual screening accuracy

Molecular docking programs are widely used modeling tools for predicting ligand binding modes and structure based virtual screening. In this study, six molecular docking programs (DOCK, FlexX, GLIDE, ICM, PhDOCK, and Surflex) were evaluated using metrics intended to assess docking pose and virtual screening accuracy. Cognate ligand docking to 68 diverse, high-resolution X-ray complexes revealed that ICM, GLIDE, and Surflex generated ligand poses close to the X-ray conformation more often than the other docking programs. GLIDE and Surflex also outperformed the other docking programs when used for virtual screening, based on mean ROC AUC and ROC enrichment values obtained for the 40 protein targets in the Directory of Useful Decoys (DUD). Further analysis uncovered general trends in accuracy that are specific for particular protein families. Modifying basic parameters in the software was shown to have a significant effect on docking and virtual screening results, suggesting that expert knowledge is critical for optimizing the accuracy of these methods.

Comparative assessment of scoring functions on a diverse test set

Scoring functions are widely applied to the evaluation of protein-ligand binding in structure-based drug design. We have conducted a comparative assessment of 16 popular scoring functions implemented in main-stream commercial software or released by academic research groups. A set of 195 diverse protein-ligand complexes with high-resolution crystal structures and reliable binding constants were selected through a systematic nonredundant sampling of the PDBbind database and used as the primary test set in our study. All scoring functions were evaluated in three aspects, that is, "docking power", "ranking power", and "scoring power", and all evaluations were independent from the context of molecular docking or virtual screening. As for "docking power", six scoring functions, including GOLD::ASP, DS::PLP1, DrugScore(PDB), GlideScore-SP, DS::LigScore, and GOLD::ChemScore, achieved success rates over 70% when the acceptance cutoff was root-mean-square deviation < 2.0 A. Combining these scoring functions into consensus scoring schemes improved the success rates to 80% or even higher. As for "ranking power" and "scoring power", the top four scoring functions on the primary test set were X-Score, DrugScore(CSD), DS::PLP, and SYBYL::ChemScore. They were able to correctly rank the protein-ligand complexes containing the same type of protein with success rates around 50%. Correlation coefficients between the experimental binding constants and the binding scores computed by these scoring functions ranged from 0.545 to 0.644. Besides the primary test set, each scoring function was also tested on four additional test sets, each consisting of a certain number of protein-ligand complexes containing one particular type of protein. Our study serves as an updated benchmark for evaluating the general performance of today's scoring functions. Our results indicate that no single scoring function consistently outperforms others in all three aspects. Thus, it is important in practice to choose the appropriate scoring functions for different purposes.

Large scale study of multiple-molecule queries

Background

In ligand-based screening, as well as in other chemoinformatics applications, one seeks to effectively search large repositories of molecules in order to retrieve molecules that are similar typically to a single molecule lead. However, in some case, multiple molecules from the same family are available to seed the query and search for other members of the same family.

Multiple-molecule query methods have been less studied than single-molecule query methods. Furthermore, the previous studies have relied on proprietary data and sometimes have not used proper cross-validation methods to assess the results. In contrast, here we develop and compare multiple-molecule query methods using several large publicly available data sets and background. We also create a framework based on a strict cross-validation protocol to allow unbiased benchmarking for direct comparison in future studies across several performance metrics.

Results

Fourteen different multiple-molecule query methods were defined and benchmarked using: (1) 41 publicly available data sets of related molecules with similar biological activity; and (2) publicly available background data sets consisting of up to 175,000 molecules randomly extracted from the ChemDB database and other sources. Eight of the fourteen methods were parameter free, and six of them fit one or two free parameters to the data using a careful cross-validation protocol. All the methods were assessed and compared for their ability to retrieve members of the same family against the background data set by using several performance metrics including the Area Under the Accumulation Curve (AUAC), Area Under the Curve (AUC), F1-measure, and BEDROC metrics.

Consistent with the previous literature, the best parameter-free methods are the MAX-SIM and MIN-RANK methods, which score a molecule to a family by the maximum similarity, or minimum ranking, obtained across the family. One new parameterized method introduced in this study and two previously defined methods, the Exponential Tanimoto Discriminant (ETD), the Tanimoto Power Discriminant (TPD), and the Binary Kernel Discriminant (BKD), outperform most other methods but are more complex, requiring one or two parameters to be fit to the data.

Conclusion

Fourteen methods for multiple-molecule querying of chemical databases, including novel methods, (ETD) and (TPD), are validated using publicly available data sets, standard cross-validation protocols, and established metrics. The best results are obtained with ETD, TPD, BKD, MAX-SIM, and MIN-RANK. These results can be replicated and compared with the results of future studies using data freely downloadable from http://cdb.ics.uci.edu/.

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data

Refined nearest neighbor analysis was recently introduced for the analysis of virtual screening benchmark data sets. It constitutes a technique from the field of spatial statistics and provides a mathematical framework for the nonparametric analysis of mapped point patterns. Here, refined nearest neighbor analysis is used to design benchmark data sets for virtual screening based on PubChem bioactivity data. A workflow is devised that purges data sets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies monitored by refined nearest neighbor analysis functions is applied to generate corresponding data sets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These data sets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods. The data sets and a software package implementing the MUV design workflow are freely available at http://www.pharmchem.tu-bs.de/lehre/baumann/MUV.html.

Validation of molecular docking programs for virtual screening against dihydropteroate synthase

Dihydropteroate synthase (DHPS) is the target of the sulfonamide class of antibiotics and has been a validated antibacterial drug target for nearly 70 years. The sulfonamides target the p-aminobenzoic acid (pABA) binding site of DHPS and interfere with folate biosynthesis and ultimately prevent bacterial replication. However, widespread bacterial resistance to these drugs has severely limited their effectiveness. This study explores the second and more highly conserved pterin binding site of DHPS as an alternative approach to developing novel antibiotics that avoid resistance. In this study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, and nine scoring functions, were evaluated for their ability to rank-order potential lead compounds for an extensive virtual screening study of the pterin binding site of B. anthracis DHPS. Their performance in ligand docking and scoring was judged by their ability to reproduce a known inhibitor conformation and to efficiently detect known active compounds seeded into three separate decoy sets. Two other metrics were used to assess performance; enrichment at 1% and 2% and Receiver Operating Characteristic (ROC) curves. The effectiveness of postdocking relaxation prior to rescoring and consensus scoring were also evaluated. Finally, we have developed a straightforward statistical method of including the inhibition constants of the known active compounds when analyzing enrichment results to more accurately assess scoring performance, which we call the 'sum of the sum of log rank' or SSLR. Of the docking and scoring functions evaluated, Surflex with Surflex-Score and Glide with GlideScore were the best overall performers for use in virtual screening against the DHPS target, with neither combination showing statistically significant superiority over the other in enrichment studies or pose selection. Postdocking ligand relaxation and consensus scoring did not improve overall enrichment.

De novo drug design using multiobjective evolutionary graphs

Drug discovery and development is a complex, lengthy process, and failure of a candidate molecule can occur as a result of a combination of reasons, such as poor pharmacokinetics, lack of efficacy, or toxicity. Successful drug candidates necessarily represent a compromise between the numerous, sometimes competing objectives so that the benefits to patients outweigh potential drawbacks and risks. De novo drug design involves searching an immense space of feasible, druglike molecules to select those with the highest chances of becoming drugs using computational technology. Traditionally, de novo design has focused on designing molecules satisfying a single objective, such as similarity to a known ligand or an interaction score, and ignored the presence of the multiple objectives required for druglike behavior. Recently, methods have appeared in the literature that attempt to design molecules satisfying multiple predefined objectives and thereby produce candidate solutions with a higher chance of serving as viable drug leads. This paper describes the Multiobjective Evolutionary Graph Algorithm (MEGA), a new multiobjective optimization de novo design algorithmic framework that can be used to design structurally diverse molecules satisfying one or more objectives. The algorithm combines evolutionary techniques with graph-theory to directly manipulate graphs and perform an efficient global search for promising solutions. In the Experimental Section we present results from the application of MEGA for designing molecules that selectively bind to a known pharmaceutical target using the ChillScore interaction score family. The primary constraints applied to the design are based on the identified structure of the protein target and a known ligand currently marketed as a drug. A detailed explanation of the key elements of the specific implementation of the algorithm is given, including the methods for obtaining molecular building blocks, evolving the chemical graphs, and scoring the designed molecules. Our findings demonstrate that MEGA can produce structurally diverse candidate molecules representing a wide range of compromises of the supplied constraints and thus can be used as an "idea generator" to support expert chemists assigned with the task of molecular design.

Hybrid structure-based virtual screening protocol for the identification of novel BACE1 inhibitors

BACE1, also called beta-secretase or memapsin 2, is an extensively studied aspartic protease, involved in etiopathogenesis and progression of Alzheimer's disease (AD). We report herein a modified structure-based virtual screening protocol that augments the lead identification process against BACE1 during virtual screening endeavors. A hybrid structure-based virtual screening protocol that incorporates elements from both ligand-based and structure-based techniques was used for the identification of prospective small molecule inhibitors. Virtual screening, using an active-site-derived pharmacophore, followed by ROCS (rapid overlay of chemical structures)-based GOLD (genetic optimization in ligand docking) docking was used to identify a library of focused candidates. The efficacy of the ROCS-based GOLD docking method together with our customized weighted consensus scoring function was evaluated against conventional docking methods for its ability to discern true positives from a screening library. An in-depth structural analysis of the binding mode of the top-ranking molecules reveals that emulation of the curial interaction patterns deemed necessary for BACE1 inhibition. The results obtained from our validation study ensure the superiority of our docking methodology over conventional docking methods in yielding higher enrichment rates.

Identification of xenoestrogens in food additives by an integrated in silico and in vitro approach

In the search for xenoestrogens within food additives, we have analyzed the Joint FAO-WHO expert committee database, containing 1500 compounds, using an integrated in silico and in vitro approach. This analysis identified 31 potential estrogen receptor alpha ligands that were reduced to 13 upon applying a stringent filter based on ligand volume and binding mode. Among the 13 potential xenoestrogens, four were already known to exhibit an estrogenic activity, and the other nine were assayed in vitro, determining the binding affinity to the receptor and biological effects. Propyl gallate was found to act as an antagonist, and 4-hexylresorcinol was found to act as a potent transactivator; both ligands were active at nanomolar concentrations, as predicted by the in silico analysis. Some caution should be issued for the use of propyl gallate and 4-hexylresorcinol as food additives.