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

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.

Potential repurposing of four FDA approved compounds with antiplasmodial activity identified through proteome scale computational drug discovery and in vitro assay

Malaria elimination can benefit from time and cost-efficient approaches for antimalarials such as drug repurposing. In this work, 796 DrugBank compounds were screened against 36 Plasmodium falciparum targets using QuickVina-W. Hits were selected after rescoring using GRaph Interaction Matching (GRIM) and ligand efficiency metrics: surface efficiency index (SEI), binding efficiency index (BEI) and lipophilic efficiency (LipE). They were further evaluated in Molecular dynamics (MD). Twenty-five protein-ligand complexes were finally retained from the 28,656 (36 × 796) dockings. Hit GRIM scores (0.58 to 0.78) showed their molecular interaction similarity to co-crystallized ligands. Minimum LipE (3), SEI (23) and BEI (7) were in at least acceptable thresholds for hits. Binding energies ranged from -6 to -11 kcal/mol. Ligands showed stability in MD simulation with good hydrogen bonding and favorable protein-ligand interactions energy (the poorest being -140.12 kcal/mol). In vitro testing showed 4 active compounds with two having IC50 values in the single-digit μM range.

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.

Sertraline, chlorprothixene, and chlorpromazine characteristically interact with the REST-binding site of the corepressor mSin3, showing medulloblastoma cell growth inhibitory activities

Dysregulation of repressor-element 1 silencing transcription factor REST/NRSF is related to several neuropathies, including medulloblastoma, glioblastoma, Huntington’s disease, and neuropathic pain. Inhibitors of the interaction between the N-terminal repressor domain of REST/NRSF and the PAH1 domain of its corepressor mSin3 may ameliorate such neuropathies. In-silico screening based on the complex structure of REST/NRSF and mSin3 PAH1 yielded 52 active compounds, including approved neuropathic drugs. We investigated their binding affinity to PAH1 by NMR, and their inhibitory activity toward medulloblastoma cell growth. Interestingly, three antidepressant and antipsychotic medicines, sertraline, chlorprothixene, and chlorpromazine, were found to strongly bind to PAH1. Multivariate analysis based on NMR chemical shift changes in PAH1 residues induced by ligand binding was used to identify compound characteristics associated with cell growth inhibition. Active compounds showed a new chemo-type for inhibitors of the REST/NRSF-mSin3 interaction, raising the possibility of new therapies for neuropathies caused by dysregulation of REST/NRSF.

Prediction of Protein-compound Binding Energies from Known Activity Data: Docking-score-based Method and its Applications

We used protein-compound docking simulations to develop a structure-based quantitative structure-activity relationship (QSAR) model. The prediction model used docking scores as descriptors. The binding free energy was approximated by a weighted average of docking scores for multiple proteins. This approximation was based on a pharmacophore model of receptor pockets and compounds. The weights of the docking scores were restricted to small values to avoid unrealistic weights by a regularization term. Additional outlier elimination improved the results. We applied this method to two groups of targets. The first target was the kinase family. The cross-validation results of 107 kinase proteins showed that the RMSE of predicted binding free energies was 1.1 kcal/mol. The second target was the matrix metalloproteinase (MMP) family, which has been difficult for docking programs. MMPs require metal-binding groups in their inhibitor structures in many cases. A quantum effect contributes to the metal-ligand interaction. Despite this difficulty, the present method worked well for the MMPs. This method showed that the RMSE of predicted binding free energies was 1.1 kcal/mol. In comparison, with the original docking method the RMSE was 1.7 kcal/mol. The results suggest that the present QSAR model should be applied to general target proteins.

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.

Structure-Based Development of a Protein-Protein Interaction Inhibitor Targeting Tumor Necrosis Factor Receptor-Associated Factor 6

The interactions between tumor necrosis factor (TNF) receptor-associated factor 6 (TRAF6) and TNF superfamily receptors (TNFRSFs) are promising targets for rheumatoid arthritis (RA) treatment. However, due to the challenging nature of protein-protein interactions (PPIs), a potent inhibitor that surpasses the affinity of the TRAF6-TNFRSF interactions has not been developed. We developed a small-molecule PPI inhibitor of TRAF6-TNFRSF interactions using NMR and in silico techniques. The most potent compound, TRI4, exhibited an affinity higher than those of TNFRSFs and competitively inhibited a TRAF6-TNFRSF interaction. Structural characterization of the TRAF6-TRI4 complex revealed that TRI4 supplants key interactions in the TRAF6-TNFRSF interfaces. In addition, some TRAF6-TRI4 interactions extend beyond the TRAF6-TNFRSF interfaces and increase the binding affinity. Our successful development of TRI4 provides a new opportunity for RA treatment and implications for structure-guided development of PPI inhibitors.

Integration of ligand-based drug screening with structure-based drug screening by combining maximum volume overlapping score with ligand docking

Ligand-based and structure-based drug screening methods were integrated for in silico drug development by combining the maximum-volume overlap (MVO) method with a protein-compound docking program. The MVO method is used to select reliable docking poses by calculating volume overlaps between the docking pose in question and the known ligand docking pose, if at least a single protein-ligand complex structure is known. In the present study, the compounds in a database were docked onto a target protein that had a known protein-ligand complex structure. The new score is the summation of the docking score and the MVO score, which is the measure of the volume overlap between the docking poses of the compound in question and the known ligand. The compounds were sorted according to the new score. The in silico screening results were improved by comparing the MVO score to the original docking score only. The present method was also applied to some target proteins with known ligands, and the results demonstrated that it worked well.

Selection of in silico drug screening results for G-protein-coupled receptors by using universal active probes

We developed a new protocol for in silico drug screening for G-protein-coupled receptors (GPCRs) using a set of "universal active probes" (UAPs) with an ensemble docking procedure. UAPs are drug-like compounds, which are actual active compounds of a variety of known proteins. The current targets were nine human GPCRs whose three-dimensional (3D) structures are unknown, plus three GPCRs, namely β(2)-adrenergic receptor (ADRB2), A(2A) adenosine receptor (A(2A)), and dopamine D3 receptor (D(3)), whose 3D structures are known. Homology-based models of the GPCRs were constructed based on the crystal structures with careful sequence inspection. After subsequent molecular dynamics (MD) simulation taking into account the explicit lipid membrane molecules with periodic boundary conditions, we obtained multiple model structures of the GPCRs. For each target structure, docking-screening calculations were carried out via the ensemble docking procedure, using both true active compounds of the target proteins and the UAPs with the multiple target screening (MTS) method. Consequently, the multiple model structures showed various screening results with both poor and high hit ratios, the latter of which could be identified as promising for use in in silico screening to find candidate compounds to interact with the proteins. We found that the hit ratio of true active compounds showed a positive correlation to that of the UAPs. Thus, we could retrieve appropriate target structures from the GPCR models by applying the UAPs, even if no active compound is known for the GPCRs. Namely, the screening result that showed a high hit ratio for the UAPs could be used to identify actual hit compounds for the target GPCRs.

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.

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.

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.

AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening

Background

Virtual or in silico ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization.

Results

The program AMMOS carries out an automatic procedure that allows for the structural refinement of compound collections and energy minimization of protein-ligand complexes using the open source program AMMP. The performance of our package was evaluated by comparing the structures of small chemical entities minimized by AMMOS with those minimized with the Tripos and MMFF94s force fields. Next, AMMOS was used for full flexible minimization of protein-ligands complexes obtained from a mutli-step virtual screening. Enrichment studies of the selected pre-docked complexes containing 60% of the initially added inhibitors were carried out with or without final AMMOS minimization on two protein targets having different binding pocket properties. AMMOS was able to improve the enrichment after the pre-docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection.

Conclusion

The open source AMMOS program can be helpful in a broad range of in silico drug design studies such as optimization of small molecules or energy minimization of pre-docked protein-ligand complexes. Our enrichment study suggests that AMMOS, designed to minimize a large number of ligands pre-docked in a protein target, can successfully be applied in a final post-processing step and that it can take into account some receptor flexibility within the binding site area.

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.

Prediction of protein–ligand complex structure by docking software guided by other complex structures

We developed a new scoring method that selects a protein-ligand complex structure with higher geometrical accuracy than the top-scoring complex structure, using the structural information of known protein-ligand complexes. To apply this method, one or more protein-ligand complex structures must be known for the target protein. A number of predicted structures were generated by the protein-compound docking program for a new ligand, and one of these structures, which showed the maximum overlap with the ligand coordinates of the known protein-ligand complex, was selected as the most probable complex structure.

Chemogenomic approaches to rational drug design

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.