Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations

Systematic exploitation of multiple receptor conformations for virtual ligand screening

The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not only to enrich a higher number of active compounds, but also to enhance their chemical diversity. Finally, some clear indications can be gathered on how to select a subset of receptor conformations that is most likely to provide the best performance in a real life scenario.

Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library

Malaria, in particular that caused by Plasmodium falciparum , is prevalent across the tropics, and its medicinal control is limited by widespread drug resistance. Cysteine proteases of P. falciparum , falcipain-2 (FP-2) and falcipain-3 (FP-3), are major hemoglobinases, validated as potential antimalarial drug targets. Structure-based virtual screening of a focused cysteine protease inhibitor library built with soft rather than hard electrophiles was performed against an X-ray crystal structure of FP-2 using the Glide docking program. An enrichment study was performed to select a suitable scoring function and to retrieve potential candidates against FP-2 from a large chemical database. Biological evaluation of 50 selected compounds identified 21 diverse nonpeptidic inhibitors of FP-2 with a hit rate of 42%. Atomic Fukui indices were used to predict the most electrophilic center and its electrophilicity in the identified hits. Comparison of predicted electrophilicity of electrophiles in identified hits with those in known irreversible inhibitors suggested the soft-nature of electrophiles in the selected target compounds. The present study highlights the importance of focused libraries and enrichment studies in structure-based virtual screening. In addition, few compounds were screened against homologous human cysteine proteases for selectivity analysis. Further evaluation of structure-activity relationships around these nonpeptidic scaffolds could help in the development of selective leads for antimalarial chemotherapy.

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.

VoteDock: consensus docking method for prediction of protein-ligand interactions

Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein-ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time-consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein-ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein-ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein-ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5.

iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis

BACKGROUND

Pharmacological interactions are useful for understanding ligand binding mechanisms of a therapeutic target. These interactions are often inferred from a set of active compounds that were acquired experimentally. Moreover, most docking programs loosely coupled the stages (binding-site and ligand preparations, virtual screening, and post-screening analysis) of structure-based virtual screening (VS). An integrated VS environment, which provides the friendly interface to seamlessly combine these VS stages and to identify the pharmacological interactions directly from screening compounds, is valuable for drug discovery.

RESULTS

We developed an easy-to-use graphic environment, iGEMDOCK, integrating VS stages (from preparations to post-screening analysis). For post-screening analysis, iGEMDOCK provides biological insights by deriving the pharmacological interactions from screening compounds without relying on the experimental data of active compounds. The pharmacological interactions represent conserved interacting residues, which often form binding pockets with specific physico-chemical properties, to play the essential functions of a target protein. Our experimental results show that the pharmacological interactions derived by iGEMDOCK are often hot spots involving in the biological functions. In addition, iGEMDOCK provides the visualizations of the protein-compound interaction profiles and the hierarchical clustering dendrogram of the compounds for post-screening analysis.

CONCLUSIONS

We have developed iGEMDOCK to facilitate steps from preparations of target proteins and ligand libraries toward post-screening analysis. iGEMDOCK is especially useful for post-screening analysis and inferring pharmacological interactions from screening compounds. We believe that iGEMDOCK is useful for understanding the ligand binding mechanisms and discovering lead compounds. iGEMDOCK is available at http://gemdock.life.nctu.edu.tw/dock/igemdock.php.

Structure-based drug design and AutoDock study of potential protein tyrosine kinase inhibitors

Different classes of compounds were investigated for their binding affinities into different protein tyrosine kinases (PTKs) employing a novel flexible ligand docking approach by using AutoDock 3.05 and 4. These compounds include many flavin analogs, which were developed in our group with varying degrees of cytotoxic activity (comparable or moderately superior to cisplatin and ara-c), and database selected analogs. They were docked onto twelve different families of PTKs retrieved from the Protein Data Bank. These proteins are representatives of plausible models of interactions with chemotherapeutic agents. A comparative study of the intact co-crystallized ligands of various types of PTKs was carried out. Results revealed that the new class of 5-deazapteridine and steroid hybrid compounds VIa,b, and d, and the vertical-type bispyridodipyrimidine with n-hexyl chain junction between its N-10 and N-10 atoms Xa, exhibited non-selective PTK binding capacities, with the lowest (Gb). On the other hand, 2-amino benzoic acid analog IIa, phenoxypyrido [3, 4-d]pyrimidine derivative IVc, tyrosine containing tripeptide Vd, and the one from Sumisho data base 831 are proposed to have selective PTK binding affinities to certain classes of tyrosine kinases, namely, HGFR (c-met), ZAP-70, insulin receptor kinase, EGFR, respectively. All These compounds of highest affinities were docked within the binding sites of PTKs with reasonable RMSD and 1-5 hydrogen bonds.

FLAP: GRID molecular interaction fields in virtual screening. validation using the DUD data set

The performance of FLAP (Fingerprints for Ligands and Proteins) in virtual screening is assessed using a subset of the DUD (Directory of Useful Decoys) benchmarking data set containing 13 targets each with more than 15 different chemotype classes. A variety of ligand and receptor-based virtual screening approaches are examined, using combinations of individual templates 2D structures of known actives, a cocrystallized ligand, a receptor structure, or a cocrystallized ligand-biased receptor structure. We examine several data fusion approaches to combine the results of the individual virtual screens. In doing so, we show that excellent chemotype enrichment is achieved in both single target ligand-based and receptor-based approaches, of approximately 17-fold over random on average at a false positive rate of 1%. We also show that using as much starting knowledge as possible improves chemotype enrichment, and that data fusion using Pareto ranking is an effective method to do this giving up to 50% improvement in enrichment over the single methods. Finally we show that if inactivity or decoy data is incorporated, automatically training the scoring function in FLAP improves recovery still further, with almost 2-fold improvement over the enrichments shown by the single methods. The results clearly demonstrate the utility of FLAP for virtual screening when either a limited or wide range of prior knowledge is available.

TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features

BACKGROUND

The increasing numbers of 3D compounds and protein complexes stored in databases contribute greatly to current advances in biotechnology, being employed in several pharmaceutical and industrial applications. However, screening and retrieving appropriate candidates as well as handling false positives presents a challenge for all post-screening analysis methods employed in retrieving therapeutic and industrial targets.

RESULTS

Using the TSCC method, virtually screened compounds were clustered based on their protein-ligand interactions, followed by structure clustering employing physicochemical features, to retrieve the final compounds. Based on the protein-ligand interaction profile (first stage), docked compounds can be clustered into groups with distinct binding interactions. Structure clustering (second stage) grouped similar compounds obtained from the first stage into clusters of similar structures; the lowest energy compound from each cluster being selected as a final candidate.

CONCLUSION

By representing interactions at the atomic-level and including measures of interaction strength, better descriptions of protein-ligand interactions and a more specific analysis of virtual screening was achieved. The two-stage clustering approach enhanced our post-screening analysis resulting in accurate performances in clustering, mining and visualizing compound candidates, thus, improving virtual screening enrichment.

Novel monocyclam derivatives as HIV entry inhibitors: Design, synthesis, anti-HIV evaluation, and their interaction with the CXCR4 co-receptor

The CXCR4 receptor has been shown to interact with the human immunodeficiency virus (HIV) envelope glycoprotein gp120, leading to fusion of viral and cell membranes. Therefore, ligands that can attach to this receptor represent an important class of therapeutic agents against HIV, thus inhibiting the first step in the cycle of viral infection: the virus-cell entry/fusion. Herein we describe the in silico design, synthesis, and biological evaluation of novel monocyclam derivatives as HIV entry inhibitors. In vitro activity testing of these compounds in cell cultures against HIV strains revealed EC(50) values in the low micromolar range without cytotoxicity at the concentrations tested. Docking and molecular dynamics simulations were performed to predict the binding interactions between CXCR4 and the novel monocyclam derivatives. A binding mode of these compounds is proposed which is consistent with the main existing site-directed mutagenesis data on the CXCR4 co-receptor. Moreover, molecular modeling comparisons were performed between these novel monocyclams, previously reported non-cyclam compounds from which the monocyclams are derived, and the well-known AMD3100 bicyclam CXCR4 inhibitors. Our results suggest that these three structurally diverse CXCR4 inhibitors bind to overlapping but not identical amino acid residues in the transmembrane regions of the receptor.

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.

Pharmacophore modeling and virtual screening studies for new VEGFR-2 kinase inhibitors

Virtual screening was performed to determine potent vascular endothelial growth factor receptor (VEGFR)-2 kinase inhibitors. A database of approximately 820,000 commercial compounds was used for screening, and 100 compounds were chosen as candidate VEGFR-2 inhibitors through pharmacophore modeling and docking studies. These 100 compounds were purchased to test their biological activities: 10 compounds were found to inhibit the enzyme, with IC(50) values ranging from 10 to 1 μM. Compound 1, which has a triazinoindole ring, inhibited the enzymatic activity of VEGFR-2, with an IC(50) value of about 1.6 μM, making it the most potent inhibitor of this enzyme. The triazinoindole derivative may therefore serve as the starting point in the design of new VEGFR-2 kinase inhibitors.

Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database

Docking is one of the most commonly used techniques in drug design. It is used for both identifying correct poses of a ligand in the binding site of a protein as well as for the estimation of the strength of protein-ligand interaction. Because millions of compounds must be screened, before a suitable target for biological testing can be identified, all calculations should be done in a reasonable time frame. Thus, all programs currently in use exploit empirically based algorithms, avoiding systematic search of the conformational space. Similarly, the scoring is done using simple equations, which makes it possible to speed up the entire process. Therefore, docking results have to be verified by subsequent in vitro studies. The purpose of our work was to evaluate seven popular docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) on the extensive dataset composed of 1300 protein-ligands complexes from PDBbind 2007 database, where experimentally measured binding affinity values were also available. We compared independently the ability of proper posing [according to Root mean square deviation (or Root mean square distance) of predicted conformations versus the corresponding native one] and scoring (by calculating the correlation between docking score and ligand binding strength). To our knowledge, it is the first large-scale docking evaluation that covers both aspects of docking programs, that is, predicting ligand conformation and calculating the strength of its binding. More than 1000 protein-ligand pairs cover a wide range of different protein families and inhibitor classes. Our results clearly showed that the ligand binding conformation could be identified in most cases by using the existing software, yet we still observed the lack of universal scoring function for all types of molecules and protein families.

HPPD: ligand- and target-based virtual screening on a herbicide target

Hydroxyphenylpyruvate dioxygenase (HPPD) has proven to be a very successful target for the development of herbicides with bleaching properties, and today HPPD inhibitors are well established in the agrochemical market. Syngenta has a long history of HPPD-inhibitor research, and HPPD was chosen as a case study for the validation of diverse ligand- and target-based virtual screening approaches to identify compounds with inhibitory properties. Two-dimensional extended connectivity fingerprints, three-dimensional shape-based tools (ROCS, EON, and Phase-shape) and a pharmacophore approach (Phase) were used as ligand-based methods; Glide and Gold were used as target-based. Both the virtual screening utility and the scaffold-hopping ability of the screening tools were assessed. Particular emphasis was put on the specific pitfalls to take into account for the design of a virtual screening campaign in an agrochemical context, as compared to a pharmaceutical environment.

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.

SiMMap: a web server for inferring site-moiety map to recognize interaction preferences between protein pockets and compound moieties

The protein–ligand interacting mechanism is essential to biological processes and drug discovery. The SiMMap server statistically derives site-moiety map with several anchors, which describe the relationship between the moiety preferences and physico-chemical properties of the binding site, from the interaction profiles between query target protein and its docked (or co-crystallized) compounds. Each anchor includes three basic elements: a binding pocket with conserved interacting residues, the moiety composition of query compounds and pocket–moiety interaction type (electrostatic, hydrogen bonding or van der Waals). We provide initial validation of the site-moiety map on three targets, thymidine kinase, and estrogen receptors of antagonists and agonists. Experimental results show that an anchor is often a hot spot and the site-moiety map can help to assemble potential leads by optimal steric, hydrogen bonding and electronic moieties. When a compound highly agrees with anchors of site-moiety map, this compound often activates or inhibits the target protein. We believe that the site-moiety map is useful for drug discovery and understanding biological mechanisms. The SiMMap web server is available at http://simfam.life.nctu.edu.tw/.

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.

Estimation of the applicability domain of kernel-based machine learning models for virtual screening

BACKGROUND

The virtual screening of large compound databases is an important application of structural-activity relationship models. Due to the high structural diversity of these data sets, it is impossible for machine learning based QSAR models, which rely on a specific training set, to give reliable results for all compounds. Thus, it is important to consider the subset of the chemical space in which the model is applicable. The approaches to this problem that have been published so far mostly use vectorial descriptor representations to define this domain of applicability of the model. Unfortunately, these cannot be extended easily to structured kernel-based machine learning models. For this reason, we propose three approaches to estimate the domain of applicability of a kernel-based QSAR model.

RESULTS

We evaluated three kernel-based applicability domain estimations using three different structured kernels on three virtual screening tasks. Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity. For each prediction, the applicability of the model for the respective compound is quantitatively described using a score obtained by an applicability domain formulation. The suitability of the applicability domain estimation is evaluated by comparing the model performance on the subsets of the screening data sets obtained by different thresholds for the applicability scores. This comparison indicates that it is possible to separate the part of the chemspace, in which the model gives reliable predictions, from the part consisting of structures too dissimilar to the training set to apply the model successfully. A closer inspection reveals that the virtual screening performance of the model is considerably improved if half of the molecules, those with the lowest applicability scores, are omitted from the screening.

CONCLUSION

The proposed applicability domain formulations for kernel-based QSAR models can successfully identify compounds for which no reliable predictions can be expected from the model. The resulting reduction of the search space and the elimination of some of the active compounds should not be considered as a drawback, because the results indicate that, in most cases, these omitted ligands would not be found by the model anyway.

Protein-ligand docking using mutually orthogonal Latin squares (MOLSDOCK)

The theoretical prediction of the association of a flexible ligand with a protein receptor requires efficient sampling of the conformational space of the ligand. Several docking methodologies are currently available. We have proposed a docking technique that performs well at low computational cost. The method uses mutually orthogonal Latin squares to efficiently sample the docking space. A variant of the mean field technique is used to analyze this sample to arrive at the optimum. The method has been previously applied to search through both the conformational space of a peptide as well its docking space. Here we extend this method to simultaneously identify both the low energy conformation as well as a high scoring docking mode for the small organic ligand molecules. Application of the method to 45 protein-ligand complexes, in which the number of rotatable torsions varies from 2 to 19, and comparisons with AutoDock 4.0, showed that the method works well.

Inhibition of dipeptidyl peptidase IV (DPP IV) is one of the mechanisms explaining the hypoglycemic effect of berberine

Berberine was investigated as an inhibitor of human dipeptidyl peptidase IV (DPP IV) in an attempt to explain its anti-hyperglycemic activities. The investigation included simulated docking experiments to fit berberine within the binding pocket of DPP IV. Berberine was found to readily fit within the binding pocket of DPP IV in a low energy orientation characterized with optimal electrostatic attractive interactions bridging the isoquinolinium positively charged nitrogen atom (berberine) and the negatively charged acidic residue of glutamic acid-205 (GLU205) of DPP IV. Experimentally, berberine was found to inhibit human recombinant DPP IV in vitro with IC(50) = 13.3 microM. Our findings suggest that DPP IV inhibition is, at least, one of the mechanisms that explain the anti-hyperglycemic activity of berberine. The fact that berberine was recently reported to potently inhibit the pro-diabetic target human protein tyrosine phosphatase 1B (h-PTP 1B) discloses a novel dual natural h-PTP 1B/DPP IV inhibitor.

Improving virtual screening performance against conformational variations of receptors by shape matching with ligand binding pocket

In this report, we present a novel virtual high-throughput screening methodology to assist in computer-aided drug discovery. Our method, designated as SLIM, involves ligand-free shape and chemical feature matching. The procedure takes advantage of a negative image of a binding pocket in a target receptor. The negative image is a set of virtual atoms representing the inner shape and chemical features of the binding pocket. Using this image, SLIM implements a shape-based similarity search based on molecular volume superposition for the ensemble of conformers of each molecule. The superposed structures, prioritized by shape similarity, are subjected to comparison of chemical feature similarities. To validate the merits of the SLIM method, we compared its performance with those of three distinct widely used tools ROCS, GLIDE, and GOLD. ROCS was selected as a representative of the ligand-centric methods, and docking programs GLIDE and GOLD as representatives of the receptor-centric methods. Our data suggest that SLIM has overall hit ranking ability that is comparable to that of the docking method, retaining the high computational speed of the ligand-centric method. It is notable that the SLIM method offers consistently reliable screening quality against conformational variations of receptors, whereas the docking methods have limited screening performance.

Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets

AIM

This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods.

METHODS

All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors alpha (ERalpha), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS.

RESULTS

Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS.

CONCLUSION

The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.

Chain termination and inhibition of mammalian poly(A) polymerase by modified ATP analogues

We report the inhibition of mammalian polyadenylation by the triphosphate derivatives of adenosine analogues, 8-chloroadenosine (8-Cl-Ado) and 8-aminoadenosine (8-amino-Ado), which are under preclinical and clinical investigations for the treatment of hematological malignancies. The nucleotide substrate specificity of bovine poly(A) polymerase (PAP) towards C8-modified ATP analogues was examined using primer extension assays. Radiolabeled RNA primers were incubated with bovine PAP, and in the absence of ATP, no primer extension was observed with 8-Cl-ATP, whereas 8-amino-ATP resulted in chain termination. The effects of modified ATP analogues on ATP-dependent poly(A)-tail synthesis by bovine PAP also were determined, and incubation with analogue triphosphate resulted in significant reduction of poly(A)-tail length. To model the biochemical consequences of 8-Cl-Ado incorporation into RNA, a synthetic RNA primer containing a 3'-terminal 8-Cl-AMP residue was evaluated, and polyadenylation of the primer by bovine PAP with ATP was blocked completely. To explain these experimental observations and probe the possible structural mechanisms, molecular modeling was employed to examine the interactions between PAP and various ATP analogues. Molecular docking demonstrated that C8-modifications of ATP led to increased distance between the 3'-hydroxyl group of the RNA oligonucleotide terminus and the alpha-phosphate of ATP that render the molecules in an unfavorable position for incorporation into RNA. Similarly, C8-substitution with a chlorine or amino group at the 3'-terminal residue of RNA also inhibits further chain elongation by PAP. In conclusion, modified ATP analogues may exert their biological effects through polyadenylation inhibition, and thus may provide an RNA-directed mechanism of action for 8-Cl-Ado and 8-amino-Ado.

Computational modeling of novel inhibitors targeting the Akt pleckstrin homology domain

Computational modeling continues to play an important role in novel therapeutics discovery and development. In this study, we have investigated the use of in silico approaches to develop inhibitors of the pleckstrin homology (PH) domain of AKT (protein kinase B). Various docking/scoring schemes have been evaluated, and the best combination was selected to study the system. Using this strategy, two hits were identified and their binding behaviors were investigated. Robust and predictive QSAR models were built using the k nearest neighbor (kNN) method to study their cellular permeability. Based on our in silico results, long flexible aliphatic tails were proposed to improve the Caco-2 penetration without affecting the binding mode. The modifications enhanced the AKT inhibitory activity of the compounds in cell-based assays, and increased their activity as in vivo antitumor testing.

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.

A novel approach to local similarity of protein binding sites substantially improves computational drug design results

We present a novel notion of binding site local similarity based on the analysis of complete protein environments of ligand fragments. Comparison of a query protein binding site (target) against the 3D structure of another protein (analog) in complex with a ligand enables ligand fragments from the analog complex to be transferred to positions in the target site, so that the complete protein environments of the fragment and its image are similar. The revealed environments are similarity regions and the fragments transferred to the target site are considered as binding patterns. The set of such binding patterns derived from a database of analog complexes forms a cloud-like structure (fragment cloud), which is a powerful tool for computational drug design. It has been shown on independent test sets that the combined use of a traditional energy-based score together with the cloud-based score responsible for the quality of embedding of a ligand into the fragment cloud improves the self-docking and screening results dramatically. The usage of a fragment cloud as a source of positioned molecular fragments fitting the binding protein environment has been validated by reproduction of experimental ligand optimization results.

A statistical framework to evaluate virtual screening

Background

Receiver operating characteristic (ROC) curve is widely used to evaluate virtual screening (VS) studies. However, the method fails to address the "early recognition" problem specific to VS. Although many other metrics, such as RIE, BEDROC, and pROC that emphasize "early recognition" have been proposed, there are no rigorous statistical guidelines for determining the thresholds and performing significance tests. Also no comparisons have been made between these metrics under a statistical framework to better understand their performances.

Results

We have proposed a statistical framework to evaluate VS studies by which the threshold to determine whether a ranking method is better than random ranking can be derived by bootstrap simulations and 2 ranking methods can be compared by permutation test. We found that different metrics emphasize "early recognition" differently. BEDROC and RIE are 2 statistically equivalent metrics. Our newly proposed metric SLR is superior to pROC. Through extensive simulations, we observed a "seesaw effect" – overemphasizing early recognition reduces the statistical power of a metric to detect true early recognitions.

Conclusion

The statistical framework developed and tested by us is applicable to any other metric as well, even if their exact distribution is unknown. Under this framework, a threshold can be easily selected according to a pre-specified type I error rate and statistical comparisons between 2 ranking methods becomes possible. The theoretical null distribution of SLR metric is available so that the threshold of SLR can be exactly determined without resorting to bootstrap simulations, which makes it easy to use in practical virtual screening studies.

APIF: a new interaction fingerprint based on atom pairs and its application to virtual screening

A new interaction fingerprint (IF) called APIF (atom-pairs-based interaction fingerprint) has been developed for postprocessing protein-ligand docking results. Unlike other existing fingerprints which employ absolute locations of individual interactions, APIF considers the relative positions of pairs of interacting atoms. Docking-based virtual screening was performed with GOLD using the crystal structures of trypsin, rhinovirus, HIV protease, carboxypeptidase, and estrogen receptor-alpha as targets. A score derived from the similarity of the bit strings for each docking solution to that of a known reference binding mode was obtained. Comparisons between APIF, GoldScore function, and standard interaction fingerprint (CHIF) scores were performed using enrichment plots. Superior recovery rates were observed in the IF score cases. Comparable results were achieved by using either of the two interaction fingerprints, substantially improving GoldScore function enrichment factors. Binding mode analyses were also carried out in order to study the best method for selecting conformations with a binding mode similar to that of the reference crystallized complex. These showed that the first conformations retrieved by interaction fingerprint scores had a more similar binding mode to the reference complex than those retrieved by the GoldScore function.

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.