Classification of Chemical Compounds by Protein−Compound Docking for Use in Designing a Focused Library

Fused Tetrahydroquinolines Are Interfering with Your Assay

Tricyclic tetrahydroquinolines (THQs) have been repeatedly reported as hits across a diverse range of high-throughput screening (HTS) campaigns. The activities of these compounds, however, are likely due to reactive byproducts that interfere with the assay. As a lesser studied class of pan-assay interference compounds, the mechanism by which fused THQs react with protein targets remains largely unknown. During HTS follow-up, we characterized the behavior and stability of several fused tricyclic THQs. We synthesized key analogues to pinpoint the cyclopentene ring double bond as a source of reactivity of fused THQs. We found that these compounds degrade in solution under standard laboratory conditions in days. Importantly, these observations make it likely that fused THQs, which are ubiquitously found within small molecule screening libraries, are unlikely the intact parent compounds. We urge deprioritization of tricylic THQ hits in HTS follow-up and caution against the investment of resources to follow-up on these problematic compounds.

Introduction of pseudo-base benzimidazole derivatives into nucleosides via base exchange by a nucleoside metabolic enzyme

In alternate organic synthesis, biocatalysis using enzymes provides a more stereoselective and cost-effective approach. Synthesis of unnatural nucleosides by nucleoside base exchange reactions using nucleoside-metabolizing enzymes has previously shown that the 5-position recognition of pyrimidine bases on nucleoside substrates is loose and can be used to introduce functional molecules into pyrimidine nucleosides. Here we explored the incorporation of purine pseudo bases into nucleosides by the base exchange reaction of pyrimidine nucleoside phosphorylase (PyNP), demonstrating that an imidazole five-membered ring is an essential structure for the reaction. In the case of benzimidazole, the base exchange proceeded to give the deoxyribose form in 96 % yield, and the ribose form in 23 % yield. The reaction also proceeded with 1H-imidazo[4,5-b]phenazine, a benzimidazole analogue with an additional ring, although the yield of nucleoside was only 31 %. Docking simulations between 1H and imidazo[4,5-b]phenazine nucleoside and the active site of PyNP (PDB 1BRW) supported our observation that 1H-imidazo[4,5-b]phenazine can be used as a substrate by PyNP. Thus, the enzymatic substitution reaction using PyNP can be used to incorporate many purine pseudo bases and benzimidazole derivatives with various functional groups into nucleoside structures, which have potential utility as diagnostic or therapeutic agents.

Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.

Use of a deoxynojirimycin-fluorophore conjugate as a cell-specific imaging probe targeting α-glucosidase on cell membranes

Molecules designed for cell-specific imaging were studied, taking advantage of an enzyme-inhibitor interaction. 1-Deoxynojirimycin (DNJ) can be actively captured by cells which express the surface membrane protein α-glucosidase. New probes composed of DNJ for recognition linked to a fluorophore signal portion were prepared (DNJ-CF₃1, DNJ-Dans 2 and DNJ-DEAC 3). Docking simulations revealed that the inhibitors acarbose and miglitol and the inhibitor portion of the probes bind at the same position in the pocket of α-glucosidase (human-derived PDB: 3TON). The ability of probes 1-3 to detect the difference between HeLa cells (from human cervical cancer tissue), Neuro-2a cells (from a mouse neuroblastoma C1300 tumor), N1E-115 cells (from a mouse brain neuroblastoma C1300 tumor), A1 cells (from the astrocyte of a newborn mouse brain), and Caco-2 cells (from a human colon carcinoma) was evaluated, and cell-specific fluorescence imaging was possible for conjugate probes 1 and 2. Caco-2 cells treated with probes 1 and 2 showed blue and green fluorescence, respectively, from the cell membrane, and did not stain the Caco-2 cells inside. These results show that DNJ-CF₃1 and DNJ-Dans 2 recognize an α-glucosidase protein on the surface of Caco-2 cells. Probes 1 and 2 did not stain any part of the other cells. This cell-specific imaging strategy is applicable for a variety of therapeutic agents for many diseases.

Bio-catalytic synthesis of unnatural nucleosides possessing a large functional group such as a fluorescent molecule by purine nucleoside phosphorylase

Unnatural nucleosides are attracting interest as potential diagnostic tools, medicines, and functional molecules.

Quantitative Structure-activity Relationship (QSAR) Models for Docking Score Correction

In order to improve docking score correction, we developed several structure-based quantitative structure activity relationship (QSAR) models by protein-drug docking simulations and applied these models to public affinity data. The prediction models used descriptor-based regression, and the compound descriptor was a set of docking scores against multiple (∼600) proteins including nontargets. The binding free energy that corresponded to the docking score was approximated by a weighted average of docking scores for multiple proteins, and we tried linear, weighted linear and polynomial regression models considering the compound similarities. In addition, we tried a combination of these regression models for individual data sets such as IC50 , Ki , and %inhibition values. The cross-validation results showed that the weighted linear model was more accurate than the simple linear regression model. Thus, the QSAR approaches based on the affinity data of public databases should improve docking scores.

From crystal to compound: structure-based antimalarial drug discovery

Despite a century of control and eradication campaigns, malaria remains one of the world's most devastating diseases. Our once-powerful therapeutic weapons are losing the war against the Plasmodium parasite, whose ability to rapidly develop and spread drug resistance hamper past and present malaria-control efforts. Finding new and effective treatments for malaria is now a top global health priority, fuelling an increase in funding and promoting open-source collaborations between researchers and pharmaceutical consortia around the world. The result of this is rapid advances in drug discovery approaches and technologies, with three major methods for antimalarial drug development emerging: (i) chemistry-based, (ii) target-based, and (iii) cell-based. Common to all three of these approaches is the unique ability of structural biology to inform and accelerate drug development. Where possible, SBDD (structure-based drug discovery) is a foundation for antimalarial drug development programmes, and has been invaluable to the development of a number of current pre-clinical and clinical candidates. However, as we expand our understanding of the malarial life cycle and mechanisms of resistance development, SBDD as a field must continue to evolve in order to develop compounds that adhere to the ideal characteristics for novel antimalarial therapeutics and to avoid high attrition rates pre- and post-clinic. In the present review, we aim to examine the contribution that SBDD has made to current antimalarial drug development efforts, covering hit discovery to lead optimization and prevention of parasite resistance. Finally, the potential for structural biology, particularly high-throughput structural genomics programmes, to identify future targets for drug discovery are discussed.

Virtual affinity fingerprints for target fishing: a new application of Drug Profile Matching

We recently introduced Drug Profile Matching (DPM), a novel virtual affinity fingerprinting bioactivity prediction method. DPM is based on the docking profiles of ca. 1200 FDA-approved small-molecule drugs against a set of nontarget proteins and creates bioactivity predictions based on this pattern. The effectiveness of this approach was previously demonstrated for therapeutic effect prediction of drug molecules. In the current work, we investigated the applicability of DPM for target fishing, i.e. for the prediction of biological targets for compounds. Predictions were made for 77 targets, and their accuracy was measured by Receiver Operating Characteristic (ROC) analysis. Robustness was tested by a rigorous 10-fold cross-validation procedure. This procedure identified targets (N = 45) with high reliability based on DPM performance. These 45 categories were used in a subsequent study which aimed at predicting the off-target profiles of currently approved FDA drugs. In this data set, 79% of the known drug-target interactions were correctly predicted by DPM, and additionally 1074 new drug-target interactions were suggested. We focused our further investigation on the suggested interactions of antipsychotic molecules and confirmed several interactions by a review of the literature.

The in silico screening and X-ray structure analysis of the inhibitor complex of Plasmodium falciparum orotidine 5'-monophosphate decarboxylase

Orotidine 5'-monophosphate decarboxylase from Plasmodium falciparum (PfOMPDC) catalyses the final step in the de novo synthesis of uridine 5'-monophosphate (UMP) from orotidine 5'-monophosphate (OMP). A defective PfOMPDC enzyme is lethal to the parasite. Novel in silico screening methods were performed to select 14 inhibitors against PfOMPDC, with a high hit rate of 9%. X-ray structure analysis of PfOMPDC in complex with one of the inhibitors, 4-(2-hydroxy-4-methoxyphenyl)-4-oxobutanoic acid, was carried out to at 2.1 Å resolution. The crystal structure revealed that the inhibitor molecule occupied a part of the active site that overlaps with the phosphate-binding region in the OMP- or UMP-bound complexes. Space occupied by the pyrimidine and ribose rings of OMP or UMP was not occupied by this inhibitor. The carboxyl group of the inhibitor caused a dramatic movement of the L1 and L2 loops that play a role in the recognition of the substrate and product molecules. Combining part of the inhibitor molecule with moieties of the pyrimidine and ribose rings of OMP and UMP represents a suitable avenue for further development of anti-malarial drugs.

Structural ensemble in computational drug screening

IMPORTANCE OF THE FIELD

Structure-based in silico drug screening is now widely used in drug development projects. Structure-based in silico drug screening is generally performed using a protein-compound docking program and docking scoring function. Many docking programs have been developed over the last 2 decades, but their prediction accuracy remains insufficient.

AREAS COVERED IN THIS REVIEW

This review highlights the recent progress of the post-processing of protein-compound complexes after docking.

WHAT THE READER WILL GAIN

These methods utilize ensembles of docking poses of compounds to improve the prediction accuracy for the ligand-docking pose and screening results. While the individual docking poses are not reliable, the free energy surface or the most probable docking pose can be estimated from the ensemble of docking poses.

TAKE HOME MESSAGE

The protein-compound docking program provides an arbitral rather than a canonical ensemble of docking poses. When the ensemble of docking poses satisfies the canonical ensemble, we can discuss how these post-docking analysis methods work and fail. Thus, improvements to the docking software will be needed in order to generate well-defined ensembles of docking poses.

Semantic similarity for automatic classification of chemical compounds

With the increasing amount of data made available in the chemical field, there is a strong need for systems capable of comparing and classifying chemical compounds in an efficient and effective way. The best approaches existing today are based on the structure-activity relationship premise, which states that biological activity of a molecule is strongly related to its structural or physicochemical properties. This work presents a novel approach to the automatic classification of chemical compounds by integrating semantic similarity with existing structural comparison methods. Our approach was assessed based on the Matthews Correlation Coefficient for the prediction, and achieved values of 0.810 when used as a prediction of blood-brain barrier permeability, 0.694 for P-glycoprotein substrate, and 0.673 for estrogen receptor binding activity. These results expose a significant improvement over the currently existing methods, whose best performances were 0.628, 0.591, and 0.647 respectively. It was demonstrated that the integration of semantic similarity is a feasible and effective way to improve existing chemical compound classification systems. Among other possible uses, this tool helps the study of the evolution of metabolic pathways, the study of the correlation of metabolic networks with properties of those networks, or the improvement of ontologies that represent chemical information.

Among the existing systems capable of computationally comparing chemical compounds, the majority use only structural and physicochemical properties. However, with the emergence of ChEBI and other chemical compound databases, it has become feasible to create a system that can use the relevance of compounds in a biological context as well. This setting enables the distinction of molecules with different roles in nature but similar structures, or similar roles and different structures. ChEBI is organized as an ontology that classifies chemical compounds, which we use to derive a semantic similarity measure that reflects the biological relevance of molecules. In an effort to use as much information as possible, we introduce Chym, a system that integrates structural and semantic information in a single hybrid metric, and we show the accuracy of the system in three distinct classification problems, which consist in deciding whether a compound crosses the blood brain barrier, is a P-glycoprotein substrate or an estrogen receptor ligand. Chym outperforms the previous attempts to solve these three problems, with a maximum accuracy of 90.0%.

A similarity search using molecular topological graphs

A molecular similarity measure has been developed using molecular topological graphs and atomic partial charges. Two kinds of topological graphs were used. One is the ordinary adjacency matrix and the other is a matrix which represents the minimum path length between two atoms of the molecule. The ordinary adjacency matrix is suitable to compare the local structures of molecules such as functional groups, and the other matrix is suitable to compare the global structures of molecules. The combination of these two matrices gave a similarity measure. This method was applied to in silico drug screening, and the results showed that it was effective as a similarity measure.

Discovery of covalent inhibitors for MIF tautomerase via cocrystal structures with phantom hits from virtual screening

Biochemical and X-ray crystallographic studies confirmed that hydroxyquinoline derivatives identified by virtual screening were actually covalent inhibitors of the MIF tautomerase. Adducts were formed by N-alkylation of the Pro-1 at the catalytic site with a loss of an amino group of the inhibitor.

A new method for in-silico drug screening and similarity search using molecular dynamics maximum volume overlap (MD-MVO) method

We developed a new molecular dynamics simulation method for molecular overlapping (alignment) and ligand-based in-silico drug screening based on molecular similarity. The molecular system consists of the query compound and the other compound(s) selected from a compound library. The newly introduced intermolecular interaction between compounds is proportional to the molecular overlap instead of the van der Waals and Coulomb interactions between atoms of different molecules. This method was able to achieve both conformer generation of molecules and molecular overlapping (alignment) at the same time. After an energy minimization and following short-time MD simulation, the molecules in the system were overlapped with each other and the similarity between compounds was measured by the volume of the overlap. We applied this MD simulation method to ligand-based in-silico drug screening and found that it worked well for several targets.

A method to enhance the hit ratio by a combination of structure-based drug screening and ligand-based screening

We examined the procedures to combine two different in silico drug-screening results to achieve a high hit ratio. When the 3D structure of the target protein and some active compounds are known, both structure-based and ligand-based in silico screening methods can be applied. In the present study, the machine-learning score modification multiple target screening (MSM-MTS) method was adopted as a structure-based screening method, and the machine-learning docking score index (ML-DSI) method was adopted as a ligand-based screening method. To combine the predicted compound’s sets by these two screening methods, we examined the product of the sets (consensus set) and the sum of the sets. As a result, the consensus set achieved a higher hit ratio than the sum of the sets and than either individual predicted set. In addition, the current combination was shown to be robust enough for the structural diversities both in different crystal structure and in snapshot structures during molecular dynamics simulations.

Structure-directed combinatorial library design

In recent years pharmaceutical companies have utilized structure-based drug design and combinatorial library design techniques to speed up their drug discovery efforts. Both approaches are routinely used in the lead discovery and lead optimization stages of the drug discovery process. Fragment-based drug design, a new power tool in the drug design toolbox, is also gaining acceptance across the pharmaceutical industry. This review will focus on the interplay between these three design techniques and recent developments in computational methodologies that enhance their integration. Examples of successful synergistic applications of these three techniques will be highlighted. Opinion regarding possible future directions of the field will be given.

In-silico drug screening method based on the protein–compound affinity matrix using the factor selection technique

We have developed a new in-silico drug screening method, a modified version of a docking score index (DSI) method, based on a protein-compound docking affinity matrix. By using this method, the docking scores are converted to the docking score indexes by the principal component analysis (PCA) method and each compound is projected into a PCA space. In this study, we propose a method to select a set of suitable principal component axes and evaluate the database enrichment for 12 target proteins. This method selects the new active compounds or hits, which are close to the known active compounds, thereby enhancing the database enrichment.

A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand binding sites

Background

An accurate description of protein shape derived from protein structure is necessary to establish an understanding of protein-ligand interactions, which in turn will lead to improved methods for protein-ligand docking and binding site analysis. Most current shape descriptors characterize only the local properties of protein structure using an all-atom representation and are slow to compute. We need new shape descriptors that have the ability to capture both local and global structural information, are robust for application to models and low quality structures and are computationally efficient to permit high throughput analysis of protein structures.

Results

We introduce a new shape description that requires only the Cα atoms to represent the protein structure, thus making it both fast and suitable for use on models and low quality structures. The notion of a geometric potential is introduced to quantitatively describe the shape of the structure. This geometric potential is dependent on both the global shape of the protein structure as well as the surrounding environment of each residue. When applying the geometric potential for binding site prediction, approximately 85% of known binding sites can be accurately identified with above 50% residue coverage and 80% specificity. Moreover, the algorithm is fast enough for proteome-scale applications. Proteins with fewer than 500 amino acids can be scanned in less than two seconds.

Conclusion

The reduced representation of the protein structure combined with the geometric potential provides a fast, quantitative description of protein-ligand binding sites with potential for use in large-scale predictions, comparisons and analysis.

Finding ligands for G protein-coupled receptors based on the protein–compound affinity matrix

We developed a novel method of identifying new active ligands based on information related to known active compounds using protein-compound docking simulations, even when the tertiary structure of the actual target receptor protein is unknown. This method was used to find ligands of G protein-coupled receptors (GPCRs), i.e., agonists and antagonists of histamine, adrenaline, serotonin and dopamine receptors. The principal component analysis (PCA) method was applied to the protein-compound affinity matrix, which was given by thorough docking calculations between sets of many protein pockets and chemical compounds. The set of protein pockets did not necessary include the target protein. Each compound was depicted as a point in the PCA space. Compounds in a sphere, whose center was set to the known active compound in the multi-dimensional PCA space or to the average position of several known active compounds, were selected as candidate-hit compounds. Our method was found to be effective for finding the ligands of GPCRs based on known native ligands, even when only the soluble protein structures were used in the docking simulations.

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.

An Efficient in Silico Screening Method Based on the Protein−Compound Affinity Matrix and Its Application to the Design of a Focused Library for Cytochrome P450 (CYP) Ligands

A new method has been developed to design a focused library based on available active compounds using protein-compound docking simulations. This method was applied to the design of a focused library for cytochrome P450 (CYP) ligands, not only to distinguish CYP ligands from other compounds but also to identify the putative ligands for a particular CYP. Principal component analysis (PCA) was applied to the protein-compound affinity matrix, which was obtained by thorough docking calculations between a large set of protein pockets and chemical compounds. Each compound was depicted as a point in the PCA space. Compounds that were close to the known active compounds were selected as candidate hit compounds. A machine-learning technique optimized the docking scores of the protein-compound affinity matrix to maximize the database enrichment of the known active compounds, providing an optimized focused library.

A virtual active compound produced from the negative image of a ligand-binding pocket, and its application to in-silico drug screening

We developed a new structure-based in-silico screening method using a negative image of a ligand-binding pocket and a multi-protein-compound interaction matrix. Based on the structure of the ligand pocket of the target protein, we designed a negative image, which consists of virtual atoms whose radii are close to those of carbon atoms. The virtual atoms fit the pocket ideally and achieve an optimal Coulomb interaction. A protein-compound docking program calculates the protein-compound interaction matrix for many proteins and many compounds including the negative image, which can be treated as a virtual compound. With specific attention to a vector of docking scores for a single compound with many proteins, we selected a compound whose score vector was similar to that of the negative image as a candidate hit compound. This method was applied to representative target proteins and showed high database enrichment with a relatively quick procedure.