Pharmacophore modeling for anti-Chagas drug design using the fragment molecular orbital method

Evaluation of Pregabalin bioadhesive multilayered microemulsion IOP-lowering eye drops

In spite of available treatment options, glaucoma continues to be a leading cause of irreversible blindness in the world. Current glaucoma medications have multiple limitations including: lack of sustained action; requirement for multiple dosing per day, ocular irritation and limited options for drugs with different mechanisms of action. Previously, we demonstrated that pregabalin, a drug with high affinity and selectivity for CACNA2D1, lowered IOP in a dose-dependent manner. The current study was designed to evaluate pregabalin microemulsion eye drops and to estimate its efficacy in humans using in silico methods. Molecular docking studies of pregabalin against CACNA2D1 of mouse, rabbit, and human were performed. Pregabalin microemulsion eye drops were characterized using multiple in vivo studies and its stability was evaluated over one year at different storage conditions. Molecular docking analyses and QSPR of pregabalin confirmed its suitability as a new IOP-lowering medication that functions using a new mechanism of action by binding to CACNA2D1 in all species evaluated. Because of its prolonged corneal residence time and corneal penetration enhancement, a single topical application of pregabalin ME can provide an extended IOP reduction of more than day in different animal models. Repeated daily dosing for 2 months confirms the lack of any tachyphylactic effect, which is a common drawback among marketed IOP-lowering medications. In addition, pregabalin microemulsion demonstrated good physical stability for one year, and chemical stability for 3-6 months if stored below 25 °C. Collectively, these outcomes greatly support the use of pregabalin eye drops as once daily IOP-lowering therapy for glaucoma management.

Exploring the interactions of antihistamine with retinoic acid receptor beta (RARB) by molecular dynamics simulations and genome-wide meta-analysis

Kaposi sarcoma (KS) is one of the most common AIDS-related malignant neoplasms, which can leave lesions on the skin among HIV patients. These lesions can be treated with 9-cis-retinoic acid (9-cis-RA), an endogenous ligand of retinoic acid receptors that has been FDA-approved for treatment of KS. However, topical application of 9-cis-RA can induce several unpleasant side effects, like headache, hyperlipidemia, and nausea. Hence, alternative therapeutics with less side effects are desirable. There are case reports associating over-the-counter antihistamine usage with regression of KS. Antihistamines competitively bind to H1 receptor and block the action of histamine, best known for being released in response to allergens. Furthermore, there are already dozens of antihistamines that are FDA-approved with less side effects than 9-cis-RA. This led our team to conduct a series of in-silico assays to determine whether antihistamines can activate retinoic acid receptors. First, we utilized high-throughput virtual screening and molecular dynamics simulations to model high-affinity interactions between antihistamines and retinoic acid receptor beta (RARβ). We then performed systems genetics analysis to identify a genetic association between H1 receptor itself and molecular pathways involved in KS. Together, these findings advocate for exploration of antihistamines against KS, starting with our two promising hit compounds, bepotastine and hydroxyzine, for experimental validation study in the future.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

The FMO2 analysis of the ligand-receptor binding energy: the Biscarbene-Gold(I)/DNA G-Quadruplex case study

In this work, the ab initio fragment molecular orbital (FMO) method was applied to calculate and analyze the binding energy of two biscarbene-Au(I) derivatives, [Au(9-methylcaffein-8-ylidene)₂]⁺ and [Au(1,3-dimethylbenzimidazol-2-ylidene)₂]⁺, to the DNA G-Quadruplex structure. The FMO2 binding energy considers the ligand-receptor complex as well as the isolated forms of energy-minimum state of ligand and receptor, providing a better description of ligand-receptor affinity compared with simple pair interaction energies (PIE). Our results highlight important features of the binding process of biscarbene-Au(I) derivatives to DNA G-Quadruplex, indicating that the total deformation-polarization energy and desolvation penalty of the ligands are the main terms destabilizing the binding. The pair interaction energy decomposition analysis (PIEDA) between ligand and nucleobases suggest that the main interaction terms are electrostatic and charge-transfer energies supporting the hypothesis that Au(I) ion can be involved in π-cation interactions further stabilizing the ligand-receptor complex. Moreover, the presence of polar groups on the carbene ring, as C = O, can improve the charge-transfer interaction with K⁺ ion. These findings can be employed to design new powerful biscarbene-Au(I) DNA-G quadruplex binders as promising anticancer drugs. The procedure described in this work can be applied to investigate any ligand-receptor system and is particularly useful when the binding process is strongly characterized by polarization, charge-transfer and dispersion interactions, properly evaluated by ab initio methods.

Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes

To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.

Halogenated Baicalein as a Promising Antiviral Agent toward SARS-CoV-2 Main Protease

The coronavirus disease pandemic is a constant reminder that global citizens are in imminent danger of exposure to emerging infectious diseases. Therefore, developing a technique for inhibitor discovery is essential for effective drug design. Herein, we proposed fragment molecular orbital (FMO)-based virtual screening to predict the molecular binding energy of potential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease inhibitors. The integration of quantum mechanical approaches and trajectory analysis from a microsecond molecular dynamics simulation was used to identify potential inhibitors. We identified brominated baicalein as a potent inhibitor of the SARS-CoV-2 main protease and confirmed its inhibitory activity in an in vitro assay. Brominated baicalein did not demonstrate significant toxicity in either in vitro or in vivo studies. The pair interaction energy from FMO-RIMP2/PCM and inhibitory constants based on the protease enzyme assay suggested that the brominated baicalein could be further developed into novel SARS-CoV-2 protease inhibitors.

Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project

Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.

Guiding Medicinal Chemistry with Fragment Molecular Orbital (FMO) Method

The understanding of binding interactions between a protein and a small molecule plays a key role in the rationalization of potency and selectivity and in design of new ideas. However, even when a target of interest is structurally enabled, visual inspection and force field-based molecular mechanics calculations cannot always explain the full complexity of the molecular interactions that are critical in drug design. Quantum mechanical methods have the potential to address this shortcoming, but traditionally, computational expense has made the application of these calculations impractical. The fragment molecular orbital (FMO) method offers a solution that combines accuracy, speed, and the ability to characterize important interactions (i.e. its strength in kcal/mol and chemical nature: hydrophobic, electrostatic, etc) that would otherwise be hard to detect. In this chapter, we describe the FMO method and illustrate its application in the discovery of the benzothiazole (BZT) series as novel tyrosine kinase ITK inhibitors for treatment of allergic asthma.

Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates

The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against SARS-CoV Main protease (Mpro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 Mpro. However, the mechanism of action of SARS-CoV-2 Mpro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 Mpro and three drug candidates by performing pharmacophore modeling and 1 μs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 Mpro.

Characterizing Interhelical Interactions of G-Protein Coupled Receptors with the Fragment Molecular Orbital Method

G-protein coupled receptors (GPCRs) are the largest superfamily of membrane proteins, regulating almost every aspect of cellular activity and serving as key targets for drug discovery. We have identified an accurate and reliable computational method to characterize the strength and chemical nature of the interhelical interactions between the residues of transmembrane (TM) domains during different receptor activation states, something that cannot be characterized solely by visual inspection of structural information. Using the fragment molecular orbital (FMO) quantum mechanics method to analyze 35 crystal structures representing different branches of the class A GPCR family, we have identified 69 topologically equivalent TM residues that form a consensus network of 51 inter-TM interactions, providing novel results that are consistent with and help to rationalize experimental data. This discovery establishes a comprehensive picture of how defined molecular forces govern specific interhelical interactions which, in turn, support the structural stability, ligand binding, and activation of GPCRs.

Integrating Ligand and Target-Driven Based Virtual Screening Approaches With in vitro Human Cell Line Models and Time-Resolved Fluorescence Resonance Energy Transfer Assay to Identify Novel Hit Compounds Against BCL-2

Antiapoptotic members of B-cell leukemia/lymphoma-2 (BCL-2) family proteins are one of the overexpressed proteins in cancer cells that are oncogenic targets. As such, targeting of BCL-2 family proteins raises hopes for new therapeutic discoveries. Thus, we used multistep screening and filtering approaches that combine structure and ligand-based drug design to identify new, effective BCL-2 inhibitors from a small molecule database (Specs SC), which includes more than 210,000 compounds. This database is first filtered based on binary “cancer-QSAR” model constructed with 886 training and 167 test set compounds and common 26 toxicity quantitative structure-activity relationships (QSAR) models. Predicted non-toxic compounds are considered for target-driven studies. Here, we applied two different approaches to filter and select hit compounds for further in vitro biological assays and human cell line experiments. In the first approach, a molecular docking and filtering approach is used to rank compounds based on their docking scores and only a few top-ranked molecules are selected for further long (100-ns) molecular dynamics (MD) simulations and in vitro tests. While docking algorithms are promising in predicting binding poses, they can be less prone to precisely predict ranking of compounds leading to decrease in the success rate of in silico studies. Hence, in the second approach, top-docking poses of each compound filtered through QSAR studies are subjected to initially short (1 ns) MD simulations and their binding energies are calculated via molecular mechanics generalized Born surface area (MM/GBSA) method. Then, the compounds are ranked based on their average MM/GBSA energy values to select hit molecules for further long MD simulations and in vitro studies. Additionally, we have applied text-mining approaches to identify molecules that contain “indol” phrase as many of the approved drugs contain indole and indol derivatives. Around 2700 compounds are filtered based on “cancer-QSAR” model and are then docked into BCL-2. Short MD simulations are performed for the top-docking poses for each compound in complex with BCL-2. The complexes are again ranked based on their MM/GBSA values to select hit molecules for further long MD simulations and in vitro studies. In total, seven molecules are subjected to biological activity tests in various human cancer cell lines as well as Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) assay. Inhibitory concentrations are evaluated, and biological activities and apoptotic potentials are assessed by cell culture studies. Four molecules are found to be limiting the proliferation capacity of cancer cells while increasing the apoptotic cell fractions.

Molecular Dynamics Simulation reveals the mechanism by which the Influenza Cap-dependent Endonuclease acquires resistance against Baloxavir marboxil

Baloxavir marboxil (BXM), an antiviral drug for influenza virus, inhibits RNA replication by binding to RNA replication cap-dependent endonuclease (CEN) of influenza A and B viruses. Although this drug was only approved by the FDA in October 2018, drug resistant viruses have already been detected from clinical trials owing to an I38 mutation of CEN. To investigate the reduction of drug sensitivity by the I38 mutant variants, we performed a molecular dynamics (MD) simulation on the CEN-BXM complex structure to analyze variations in the mode of interaction. Our simulation results suggest that the side chain methyl group of I38 in CEN engages in a CH-pi interaction with the aromatic ring of BXM. This interaction is abolished in various I38 mutant variants. Moreover, MD simulation on various mutation models and binding free energy prediction by MM/GBSA method suggest that the I38 mutation precludes any interaction with the aromatic ring of BXA and thereby reduces BXA sensitivity.

Dihydroorotate dehydrogenase inhibitors in anti-infective drug research

Pyrimidines are essential for the cell survival and proliferation of living parasitic organisms, such as Helicobacter pylori, Plasmodium falciparum and Schistosoma mansoni, that are able to impact upon human health. Pyrimidine building blocks, in human cells, are synthesised via both de novo biosynthesis and salvage pathways, the latter of which is an effective way of recycling pre-existing nucleotides. As many parasitic organisms lack pyrimidine salvage pathways for pyrimidine nucleotides, blocking de novo biosynthesis is seen as an effective therapeutic means to selectively target the parasite without effecting the human host. Dihydroorotate dehydrogenase (DHODH), which is involved in the de novo biosynthesis of pyrimidines, is a validated target for anti-infective drug research. Recent advances in the DHODH microorganism field are discussed herein, as is the potential for the development of DHODH-targeted therapeutics.

Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning

Virtual screening is a promising method for obtaining novel hit compounds in drug discovery. It aims to enrich potentially active compounds from a large chemical library for further biological experiments. However, the accuracy of current virtual screening methods is insufficient. In this study, we develop a new virtual screening method named Similarity of Interaction Energy VEctor Score (SIEVE-Score), in which protein-ligand interaction energies are extracted to represent docking poses for machine learning. SIEVE-Score offers substantial improvements compared to other state-of-the-art virtual screening methods, namely, other machine-learning-based scoring functions, interaction fingerprints, and docking software, for the enrichment factor 1% results on the Directory of Useful Decoys, Enhanced (DUD-E). The screening results are also human-interpretable in the form of important interactions for distinguishing between active and inactive compounds. The source code is available at https://github.com/sekijima-lab/SIEVE-Score .

Towards good correlation between fragment molecular orbital interaction energies and experimental IC50 for ligand binding: A case study of p38 MAP kinase

We describe several procedures for the preprocessing of fragment molecular orbital (FMO) calculations on p38 mitogen-activated protein (MAP) kinase and discuss the influence of the procedures on the protein–ligand interaction energies represented by inter-fragment interaction energies (IFIEs). The correlation between the summation of IFIEs for a ligand and amino acid residues of protein (IFIE-sum) and experimental affinity values (IC₅₀) was poor when considered for the whole set of protein–ligand complexes. To improve the correlation for prediction of ligand binding affinity, we carefully classified data set by the ligand charge, the DFG-loop state (DFG-in/out loop), which is characteristic of kinase, and the scaffold of ligand. The correlation between IFIE-sums and the activity values was examined using the classified data set. As a result, it was confirmed that there was a selected data set that showed good correlation between IFIE-sum and activity value by appropriate classification. In addition, we found that the differences in protonation and hydrogen orientation caused by subtle differences in preprocessing led to a relatively large difference in IFIE values. Further, we also examined the effect of structure optimization with different force fields. It was confirmed that the difference in the force field had no significant effect on IFIE-sum. From the viewpoint of drug design using FMO calculations, various investigations on IFIE-sum in this research, such as those regarding several classifications of data set and the different procedures of structural preparation, would be expected to provide useful knowledge for improvement of prediction ability about the ligand binding affinity.

Compound property enhancement by virtual compound synthesis

During drug discovery, drug candidates are narrowed down over several steps to develop pharmaceutical products. The theoretical chemical space in such steps is estimated to be [Formula: see text]. To cover that space, extensive virtual compound libraries have been developed; however, the compilation of extensive libraries comes at large computational cost. Thus, to reduce the computational cost, researchers have constructed custom-made virtual compound libraries that focus on target diseases. In this study, we develop a system that generates virtual compound libraries from input compounds. When a user inputs a compound, the system recursively applies virtual synthetic reaction rules to the compound to improve its properties. The synthetic pathway can also be traced by the user because the reaction rules in this system are based on real organic synthesis reactions. This system has useful functions for effective drug design, such as structural preservation, allowing the substructures necessary for potency to be maintained. In this paper, to confirm the effect of directional reaction sets, we applied the reaction sets to 100 compounds. Moreover, to confirm that the system can reproduce real synthetic pathways, the synthetic pathways of Ibuprofen and Ofloxacin were explored by inputting isobutyl benzene and 7,8-difluoro-2,3-dihydro-3-methyl-4H-benzoxazine. This application is available at the following URL: http://enh.sekijima-lab.org .

Computational Methods to Discover Compounds for the Treatment of Chagas Disease

Infectious diseases continue to be a major public health. Among these diseases, American trypanosomiasis or Chagas disease (CD) is a major cause of morbidity and death for millions of people in Latin America. The two drugs currently available for the treatment of CD have poor efficacy and major side effects. Thus, there is a pressing need to develop safe and effective drugs against this disease. Herein we review the diversity and coverage of chemical space of compounds tested as inhibitors of Trypanosoma cruzi, a parasite causing CD. We also review major molecular targets currently pursued to kill the parasite and recent computational approaches to identify inhibitors for such targets.

Biochemical studies of membrane bound Plasmodium falciparum mitochondrial L-malate:quinone oxidoreductase, a potential drug target

Plasmodium falciparum is an apicomplexan parasite that causes the most severe malaria in humans. Due to a lack of effective vaccines and emerging of drug resistance parasites, development of drugs with novel mechanisms of action and few side effects are imperative. To this end, ideal drug targets are those essential to parasite viability as well as absent in their mammalian hosts. The mitochondrial electron transport chain (ETC) of P. falciparum is one source of such potential targets because enzymes, such as L-malate:quinone oxidoreductase (PfMQO), in this pathway are absent humans. PfMQO catalyzes the oxidation of L-malate to oxaloacetate and the simultaneous reduction of ubiquinone to ubiquinol. It is a membrane protein, involved in three pathways (ETC, the tricarboxylic acid cycle and the fumarate cycle) and has been shown to be essential for parasite survival, at least, in the intra-erythrocytic asexual stage. These findings indicate that PfMQO would be a valuable drug target for development of antimalarial with novel mechanism of action. Up to this point in time, difficulty in producing active recombinant mitochondrial MQO has hampered biochemical characterization and targeted drug discovery with MQO. Here we report for the first time recombinant PfMQO overexpressed in bacterial membrane and the first biochemical study. Furthermore, about 113 compounds, consisting of ubiquinone binding site inhibitors and antiparasitic agents, were screened resulting in the discovery of ferulenol as a potent PfMQO inhibitor. Finally, ferulenol was shown to inhibit parasite growth and showed strong synergism in combination with atovaquone, a well-described anti-malarial and bc₁ complex inhibitor.

Exploring the selectivity of inhibitor complexes with Bcl-2 and Bcl-XL: A molecular dynamics simulation approach

B-cell lymphoma 2 (Bcl-2) family proteins are potential drug targets in cancer and have a relatively flat and flexible binding site. ABT-199 is one of the most promising selective Bcl-2 inhibitors, and A-1155463 selectively inhibits Bcl-XL. Although the amino acid sequences of the binding sites of these two inhibitors are similar, the inhibitors selectively bind the target protein. In order to determine the origin of the selectivity of these inhibitors, we conducted molecular dynamics simulations using protein-inhibitor modeling. We confirmed that ASP103 of Bcl-2 is a key residue and that hydrogen bonding between ASP103 and ABT-199 confers the Bcl-2 selectivity of this inhibitor. For Bcl-XL selectivity, the secondary structure of α-helix 3 is a key factor. PHE105, SER106, and LEU108 in the loose α-helix 3 interact with A-1155463 to confer Bcl-XL selectivity. These findings provide important insights into the molecular mechanisms of selective inhibitors of Bcl-2 family proteins.

In silico, in vitro, X-ray crystallography, and integrated strategies for discovering spermidine synthase inhibitors for Chagas disease

Chagas disease results from infection by Trypanosoma cruzi and is a neglected tropical disease (NTD). Although some treatment drugs are available, their use is associated with severe problems, including adverse effects and limited effectiveness during the chronic disease phase. To develop a novel anti-Chagas drug, we virtually screened 4.8 million small molecules against spermidine synthase (SpdSyn) as the target protein using our super computer “TSUBAME2.5” and conducted in vitro enzyme assays to determine the half-maximal inhibitory concentration values. We identified four hit compounds that inhibit T. cruzi SpdSyn (TcSpdSyn) by in silico and in vitro screening. We also determined the TcSpdSyn–hit compound complex structure using X-ray crystallography, which shows that the hit compound binds to the putrescine-binding site and interacts with Asp171 through a salt bridge.

The dihydroorotate dehydrogenases: Past and present

The flavoenzyme dihydroorotate dehydrogenase catalyzes the stereoselective oxidation of (S)-dihydroorotate to orotate in the fourth of the six conserved enzymatic reactions involved in the de novo pyrimidine biosynthetic pathway. Inhibition of pyrimidine metabolism by selectively targeting DHODHs has been exploited in the development of new therapies against cancer, immunological disorders, bacterial and viral infections, and parasitic diseases. Through a chronological narrative, this review summarizes the efforts of the scientific community to achieve our current understanding of structural and biochemical properties of DHODHs. It also attempts to describe the latest advances in medicinal chemistry for therapeutic development based on the selective inhibition of DHODH, including an overview of the experimental techniques used for ligand screening during the process of drug discovery.

Using the fragment molecular orbital method to investigate agonist-orexin-2 receptor interactions

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity and is essential for an efficient structure-based drug discovery (SBDD) process. Clearly, to begin SBDD, a structure is needed, and although there has been fantastic progress in solving G-protein-coupled receptor (GPCR) crystal structures, the process remains quite slow and is not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of X-ray crystallography to impact the drug discovery process for GPCR targets in 'real-time' and hence there is still a need for other practical and cost-efficient alternatives. We present here an approach that integrates our previously described hierarchical GPCR modelling protocol (HGMP) and the fragment molecular orbital (FMO) quantum mechanics (QM) method to explore the interactions and selectivity of the human orexin-2 receptor (OX2R) and its recently discovered nonpeptidic agonists. HGMP generates a 3D model of GPCR structures and its complexes with small molecules by applying a set of computational methods. FMO allowsab initioapproaches to be applied to systems that conventional QM methods would find challenging. The key advantage of FMO is that it can reveal information on the individual contribution and chemical nature of each residue and water molecule to the ligand binding that normally would be difficult to detect without QM. We illustrate how the combination of both techniques provides a practical and efficient approach that can be used to analyse the existing structure-function relationships (SAR) and to drive forward SBDD in a real-world example for which there is no crystal structure of the complex available.

Fragment Molecular Orbital Method Applied to Lead Optimization of Novel Interleukin-2 Inducible T-Cell Kinase (ITK) Inhibitors

Inhibition of inducible T-cell kinase (ITK), a nonreceptor tyrosine kinase, may represent a novel treatment for allergic asthma. In our previous reports, we described the discovery of sulfonylpyridine (SAP), benzothiazole (BZT), indazole (IND), and tetrahydroindazole (THI) series as novel ITK inhibitors and how computational tools such as dihedral scans and docking were used to support this process. X-ray crystallography and modeling were applied to provide essential insight into ITK-ligand interactions. However, "visual inspection" traditionally used for the rationalization of protein-ligand affinity cannot always explain the full complexity of the molecular interactions. The fragment molecular orbital (FMO) quantum-mechanical (QM) method provides a complete list of the interactions formed between the ligand and protein that are often omitted from traditional structure-based descriptions. FMO methodology was successfully used as part of a rational structure-based drug design effort to improve the ITK potency of high-throughput screening hits, ultimately delivering ligands with potency in the subnanomolar range.

The fragment molecular orbital method combined with density-functional tight-binding and the polarizable continuum model

The electronic gap in proteins is analyzed in detail, and it is shown that FMO-DFTB/PCM is efficient and accurate in describing the molecular structure of proteins in solution.

The Fragment Molecular Orbital Method Reveals New Insight into the Chemical Nature of GPCR–Ligand Interactions

Our interpretation of ligand-protein interactions is often informed by high-resolution structures, which represent the cornerstone of structure-based drug design. However, visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum Mechanics approaches are often too computationally expensive, but one method, Fragment Molecular Orbital (FMO), offers an excellent compromise and has the potential to reveal key interactions that would otherwise be hard to detect. To illustrate this, we have applied the FMO method to 18 Class A GPCR-ligand crystal structures, representing different branches of the GPCR genome. Our work reveals key interactions that are often omitted from structure-based descriptions, including hydrophobic interactions, nonclassical hydrogen bonds, and the involvement of backbone atoms. This approach provides a more comprehensive picture of receptor-ligand interactions than is currently used and should prove useful for evaluation of the chemical nature of ligand binding and to support structure-based drug design.

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.