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

Computational insights into potent USP5 inhibitors based on multistep virtual screening and molecular dynamics simulation

USP5 is widely distributed in various malignant tumors and can regulate the stability and promoting tumor progression of many tumor-related proteins. However, there is still a lack of highly active USP5 inhibitors. Therefore, effective inhibitors were screened in the TCMIO database in this study. Three hit compounds, CHEMBL3645368, CHEMBL3689818, and CHEMBL2070208, were finally obtained by molecular docking, molecular fingerprint, quantum chemistry, and molecular dynamics simulation. Molecular docking results showed hit compounds had similar binding mode comparing with positive compound. Quantum chemistry and molecular dynamics results showed hit compounds had better binding energy and higher affinity than the positive compound. ADMET predicted hit compounds had low toxicity. These results all suggest CHEMBL3645368, CHEMBL3689818, and CHEMBL2070208 may inhibit USP5 and could be candidates for further exploration.

FMOe: Preprocessing and Visualizing Package of the Fragment Molecular Orbital Method for Molecular Operating Environment and Its Applications in Covalent Ligand and Metalloprotein Analyses

The fragment molecular orbital (FMO) method is an efficient quantum chemical calculation technique for large biomolecules, dividing each into smaller fragments and providing interfragment interaction energies (IFIEs) that support our understanding of molecular recognition. The ab initio fragment MO method (ABINIT-MP), an FMO processing program, can automatically divide typical proteins and nucleic acids. In contrast, small molecules such as ligands and heterosystems must be manually divided. Thus, we developed a graphical user interface to easily handle such manual fragmentation as a library for the Molecular Operating Environment (MOE) that preprocesses and visualizes FMO calculations. We demonstrated fragmentation with IFIE analyses for the two following cases: (1) covalent cysteine-ligand bonding inside the SARS-CoV-2 main protease (Mpro) and nirmatrelvir (Paxlovid) complex and (2) the metal coordination inside a zinc-bound cyclic peptide. IFIE analysis successfully identified the key amino acid residues for the molecular recognition of nirmatrelvir with Mpro and the details of their interactions (e.g., hydrogen bonds and CH/π interactions) via ligand fragmentation of functional group units. In metalloproteins, we found an efficient and accurate scheme for the fragmentation of Zn2+ ions with four histidines coordinated to the ion. FMOe simplifies manual fragmentation, allowing users to experiment with various fragmentation patterns and perform in-depth IFIE analysis with high accuracy. In the future, our findings will provide valuable insight into complicated cases, such as ligand fragmentation in modality drug discovery, especially for medium-sized molecules and metalloprotein fragmentation around metals.

Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation, and molecular dynamic simulation

CONTEXT

SHP2 is a non-receptor protein tyrosine phosphatase to remove tyrosine phosphorylation. Functionally, SHP2 is an essential bridge to connect numerous oncogenic cell-signaling cascades including RAS-ERK, PI3K-AKT, JAK-STAT, and PD-1/PD-L1 pathways. This study aims to discover novel and potent SHP2 inhibitors using a hierarchical structure-based virtual screening strategy that combines molecular docking and the fragment molecular orbital method (FMO) for calculating binding affinity (referred to as the Dock-FMO protocol). For the SHP2 target, the FMO method prediction has a high correlation between the binding affinity of the protein-ligand interaction and experimental values (R2 = 0.55), demonstrating a significant advantage over the MM/PBSA (R2 = 0.02) and MM/GBSA (R2 = 0.15) methods. Therefore, we employed Dock-FMO virtual screening of ChemDiv database of ∼2,990,000 compounds to identify a novel SHP2 allosteric inhibitor bearing hydroxyimino acetamide scaffold. Experimental validation demonstrated that the new compound (E)-2-(hydroxyimino)-2-phenyl-N-(piperidin-4-ylmethyl)acetamide (7188-0011) effectively inhibited SHP2 in a dose-dependent manner. Molecular dynamics (MD) simulation analysis revealed the binding stability of compound 7188-0011 and the SHP2 protein, along with the key interacting residues in the allosteric binding site. Overall, our work has identified a novel and promising allosteric inhibitor that targets SHP2, providing a new starting point for further optimization to develop more potent inhibitors.

METHODS

All the molecular docking studies were employed to identify potential leads with Maestro v10.1. The protein-ligand binding affinities of potential leads were further predicted by FMO calculations at MP2/6-31G* level using GAMESS v2020 system. MD simulations were carried out with AmberTools18 by applying the FF14SB force field. MD trajectories were analyzed using VMD v1.9.3. MM/GB(PB)SA binding free energy analysis was carried out with the mmpbsa.py tool of AmberTools18. The docking and MD simulation results were visualized through PyMOL v2.5.0.

Toward Post-Hartree-Fock Accuracy for Protein-Ligand Affinities Using the Molecules-in-Molecules Fragmentation-Based Method

The complexity and size of large molecular systems, such as protein-ligand complexes, pose computational challenges for accurate post-Hartree-Fock calculations. This study delivers a thorough benchmarking of the Molecules-in-Molecules (MIM) method, presenting a clear and accessible strategy for layer/theory selections in post-Hartree-Fock computations on substantial molecular systems, notably protein-ligand complexes. An approach is articulated, enabling augmented computational efficiency by strategically canceling out common subsystem energy terms between complexes and proteins within the supermolecular equation. Employing DLPNO-based post-Hartree-Fock methods in conjunction with the three-layer MIM method (MIM3), this study demonstrates the achievement of protein-ligand binding energies with remarkable accuracy (errors <1 kcal mol-1), while significantly reducing computational costs. Furthermore, noteworthy correlations between theoretically computed interaction energies and their experimental equivalents were observed, with R2 values of approximately 0.90 and 0.78 for CDK2 and BZT-ITK sets, respectively, thus validating the efficacy of the MIM method in calculating binding energies. By highlighting the crucial role of diffuse or small Pople-style basis sets in the middle layer for reducing energy errors, this work provides valuable insights and practical methodologies for interaction energy computations in large molecular complexes and opens avenues for their application across a diverse range of molecular systems.

Revisiting of Properties and Modified Polyethylenimine-Based Cancer Gene Delivery Systems

A new era of medical technology in cancer treatment is a directly specific modification of gene expression in tumor cells by nucleic acid delivery. Currently, the main challenge to achieving this goal is to find a non-toxic, safe, and effective strategy for gene transfer to cancer cells. Synthetic composites based on cationic polymers have historically been favored in bioengineering due to their ability to mimic bimolecular structures. Among them, polyethylenimines (PEIs) with superior properties such as a wide range of molecular weight and a flexible structure may propel the development of functional combinations in the biomedical and biomaterial fields. Here, in this review, we will focus on the recent progressions in the formulation optimization of PEI-based polyplex in gene delivery to treat cancer. Also, the effect of PEI's intrinsic characteristics such as structure, molecular weight, and positive charges which influence the gene delivery efficiency will be discussed.

The Pivotal Distinction between Antagonists' and Agonists' Binding into Dopamine D4 Receptor-MD and FMO/PIEDA Studies

The dopamine D4 receptor (D4R) is a promising therapeutic target in widespread diseases, and the search for novel agonists and antagonists appears to be clinically relevant. The mechanism of binding to the receptor (R) for antagonists and agonists varies. In the present study, we conducted an in-depth computational study, teasing out key similarities and differences in binding modes, complex dynamics, and binding energies for D4R agonists and antagonists. The dynamic network method was applied to investigate the communication paths between the ligand (L) and G-protein binding site (GBS) of human D4R. Finally, the fragment molecular orbitals with pair interaction energy decomposition analysis (FMO/PIEDA) scheme was used to estimate the binding energies of L-R complexes. We found that a strong salt bridge with D3.32 initiates the inhibition of the dopamine D4 receptor. This interaction also occurs in the binding of agonists, but the change in the receptor conformation to the active state starts with interaction with cysteine C3.36. Such a mechanism may arise in the case of agonists unable to form a hydrogen bond with the serine S5.46, considered, so far, to be crucial in the activation of GPCRs. The energy calculations using the FMO/PIEDA method indicate that antagonists show higher residue occupancy of the receptor binding site than agonists, suggesting they could form relatively more stable complexes. Additionally, antagonists were characterized by repulsive interactions with S5.46 distinguishing them from agonists.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

Conformational Selectivity of ITK Inhibitors: Insights from Molecular Dynamics Simulations

Interleukin-2-inducible T-cell kinase (ITK) regulates the response to T-cell receptor signaling and is a drug target for inflammatory and immunological diseases. Molecules that bind preferentially to the active form of ITK have low selectivity between kinases, whereas those that bind preferentially to the inactive form have high selectivity for ITK. Therefore, computational methods to predict the conformational selectivity of compounds are required to design highly selective ITK inhibitors. In this study, we performed absolute binding free-energy perturbation (ABFEP) simulations for 11 compounds on both active and inactive forms of ITK, and the calculated binding free energies were compared with experimental data. The conformational selectivity of 10 of the 11 compounds was correctly predicted using ABFEP. To investigate the mechanism underlying the stabilization of the active and inactive structures by the compounds, we performed extensive, conventional molecular dynamics simulations, which revealed that the compound-induced stabilization of the P-loop and linkage of conformational changes in L489, V419, F501, and M410 upon compound binding were critical factors. A guideline for designing inactive-form binders is proposed based on these key structural factors. The ABFEP and the created guidelines are expected to facilitate the discovery of highly selective ITK inhibitors.

Structure-Based Optimization and Biological Evaluation of Potent and Selective MMP-7 Inhibitors for Kidney Fibrosis

Matrix metalloproteinase-7 (MMP-7) has been shown to play important roles in pathophysiological processes involved in the development/progression of diseases such as cancer and fibrosis. We discovered selective MMP-7 inhibitors composed of arylsulfonamide, carboxylate, and short peptides by a molecular hybridization approach. These compounds interacted with MMP-7 via multiple hydrogen bonds in the cocrystal structures. To obtain compounds for in vivo evaluation, we attempted structural optimization, particularly targeting Tyr167 at the S3 subsite through structure-based drug design, and identified compound 15 as showing improved MMP-7 potency and MMP subtype selectivity. A novel π-π stacking interaction with Tyr167 was achieved when 4-pyridylalanine was introduced as the P3 residue. Compound 15 suppressed the progression of kidney fibrosis in a dose-dependent manner in a mouse model of unilateral ureteral obstruction. Thus, we demonstrated, for the first time, that potent and selective MMP-7 inhibitors could prevent the progression of kidney fibrosis.

SophosQM: Accurate Binding Affinity Prediction in Compound Optimization

The optimization of compounds' binding affinity for a biological target is a crucial aspect of the drug development process. Being able to accurately predict binding energies in advance of synthesizing compounds would have a massive impact on the speed of the drug discovery process. The ideal binding affinity prediction method should combine accuracy, reliability, and speed. In this paper, we present SophosQM, a quantum mechanics (QM)-based approach, which can accurately predict the binding affinities of compounds to proteins. The binding affinity predictive models generated by SophosQM are based on the fragment molecular orbital (FMO) method to estimate the enthalpic component of the binding free energy, and a macroscopic descriptor, clog P, is used as an approximation of the entropic component. The affinity prediction is performed using multilinear regression, fitting the experimental values against the FMO-computed enthalpic term and clog P. The quality of the prediction can be assessed in terms of the correlation coefficient between experimental and predicted values. In this work, the method's reliability and accuracy are exemplified by applying SophosQM to 70 compounds binding to six different targets of pharmaceutical relevance. Overall, the results show a very satisfactory performance with a global correlation coefficient in the order of 0.9. Our predictions also show a satisfactory performance compared to data based on free energy perturbation. Finally, SophosQM can also be applied in high-throughput mode by using semiempirical QM methods to evaluate large portions of chemical space, while retaining a good level of accuracy, but decreasing the computing time to just a few seconds per compound.

System truncation accelerates binding affinity calculations with the fragment molecular orbital method: A benchmark study

The fragment molecular orbital (FMO) method is a fast quantum-mechanical method that divides systems into pieces of fragments and performs ab initio calculations. The system truncation enables further speed improvement. In this article, we systematically study the effects of system truncations on binding affinity calculations obtained with FMO in combination with either the polarizable continuum model (FMO/PCM) or in combination with the Møller-Plesset method (FMO-MP2). We have used five protein complexes with ligands of several charged states. The calculated binding energies of the size variants of the truncated system, including only a restricted number of atoms around the ligand, are compared to the energy obtained from a full system. The result shows that the systems could be truncated to a radius of 8 Å from neutral ligands within an error of 0.7 kcal/mol, and 12 Å from charged ligands within an error of 1.1 kcal/mol for calculating the binding energy in solution.

Benefits of hybrid QM/MM over traditional classical mechanics in pharmaceutical systems

Hybrid quantum mechanics/molecular mechanics (QM/MM) is one of the most reliable approaches for accurately modeling and studying the complex pharmaceutical discovery system. Classical mechanics has significantly accelerated the drug discovery process in the past decade. However, the current challenge is the large pool of false positives, which require extensive validation. Hybrid QM/MM is an effective solution for accurately studying ligand binding, structural mechanisms, free energy evaluation, and spectroscopic characterization. This article highlights the methodological details relevant to cost-effective hybrid QM/MM methods. This approach, combined with traditional pharmacoinformatics methods, could be a reliable strategy to balance the cost and accuracy of the calculations.

Hotspot Identification and Drug Design of Protein-Protein Interaction Modulators Using the Fragment Molecular Orbital Method

Protein-protein interactions (PPIs) are essential for the function of many proteins. Aberrant PPIs have the potential to lead to disease, making PPIs promising targets for drug discovery. There are over 64,000 PPIs in the human interactome reference database; however, to date, very few PPI modulators have been approved for clinical use. Further development of PPI-specific therapeutics is highly dependent on the availability of structural data and the existence of reliable computational tools to explore the interface between two interacting proteins. The fragment molecular orbital (FMO) quantum mechanics method offers comprehensive and computationally inexpensive means of identifying the strength (in kcal/mol) and the chemical nature (electrostatic or hydrophobic) of the molecular interactions taking place at the protein-protein interface. We have integrated FMO and PPI exploration (FMO-PPI) to identify the residues that are critical for protein-protein binding (hotspots). To validate this approach, we have applied FMO-PPI to a dataset of protein-protein complexes representing several different protein subfamilies and obtained FMO-PPI results that are in agreement with published mutagenesis data. We observed that critical PPIs can be divided into three major categories: interactions between residues of two proteins (intermolecular), interactions between residues within the same protein (intramolecular), and interactions between residues of two proteins that are mediated by water molecules (water bridges). We extended our findings by demonstrating how this information obtained by FMO-PPI can be utilized to support the structure-based drug design of PPI modulators (SBDD-PPI).

Fragmentation-Based Decomposition of a Metalloenzyme-Substrate Interaction: A Case Study for a Lytic Polysaccharide Monooxygenase

We present a novel decomposition scheme for electronic interaction energies based on the flexible formulation of fragmentation schemes through fragment combination ranges (FCRs; J. Chem. Phys., 2021, 155, 164105). We devise a clear additive decomposition with contribution of nondisjoint fragments and correction terms for overlapping fragments and apply this scheme to the metalloenzyme-substrate complex of a lytic polysaccharide monooxygenase (LPMO) with an oligosaccharide. By this, we further illustrate the straightforward adaptability of the FCR-based schemes to novel systems. Our calculations suggest that the description of the electronic structure is a larger error source than the fragmentation scheme. In particular, we find a large impact of the basis set size on the interaction energies. Still, the introduction of three-body interaction terms in the fragmentation setup improves the agreement to the supermolecular reference. Yet, the qualitative results for the decomposition scheme with two-body terms only largely agree within the investigated electronic-structure approaches and basis sets, which are B97-3c, DFT (TPSS and B3LYP), and MP2 methods. The overlap contributions are found to be small, allowing analysis of the interaction energy into individual amino acid residues: We find a particularly strong interaction between the substrate and the LPMO copper active site.

Comparative assessment of QM-based and MM-based models for prediction of protein-ligand binding affinity trends

Methods which accurately predict protein-ligand binding strengths are critical for drug discovery. In the last two decades, advances in chemical modelling have enabled steadily accelerating progress in the discovery and optimization of structure-based drug design. Most computational methods currently used in this context are based on molecular mechanics force fields that often have deficiencies in describing the quantum mechanical (QM) aspects of molecular binding. In this study, we show the competitiveness of our QM-based Molecules-in-Molecules (MIM) fragmentation method for characterizing binding energy trends for seven different datasets of protein-ligand complexes. By using molecular fragmentation, the MIM method allows for accelerated QM calculations. We demonstrate that for classes of structurally similar ligands bound to a common receptor, MIM provides excellent correlation to experiment, surpassing the more popular Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) methods. The MIM method offers a relatively simple, well-defined protocol by which binding trends can be ascertained at the QM level and is suggested as a promising option for lead optimization in structure-based drug design.

A Study on the Effect of the Substituent against PAK4 Inhibition Using In Silico Methods

The intrinsic inductive properties of atoms or functional groups depend on the chemical properties of either electron-withdrawing groups (EWGs) or electron-donating groups (EDGs). This study aimed to evaluate in silico methods to determine whether changes in chemical properties of the compound by single atomic substitution affect the biological activity of target proteins and whether the results depend on the properties of the functional groups. We found an imidazo[4,5-b]pyridine-based PAK4 inhibitor, compound 1, as an initial hit compound with the well-defined binding mode for PAK4. In this study, we used both experimental and in silico methods to investigate the effect of atomic substitution on biological activity to optimize the initial hit compound. In biological assays, in the case of EWG, as the size of the halogen atom became smaller and the electronegativity increased, the biological activity IC₅₀ value ranged from 5150 nM to inactive; in the case of EDG, biological activity was inactive. Furthermore, we analyzed the interactions of PAK4 with compounds, focusing on the hinge region residues, L398 and E399, and gatekeeper residues, M395 and K350, of the PAK4 protein using molecular docking studies and fragment molecular orbital (FMO) methods to determine the differences between the effect of EWG and EDG on the activity of target proteins. These results of the docking score and binding energy did not explain the differences in biological activity. However, the pair-interaction energy obtained from the results of the FMO method indicated that there was a difference in the interaction energy between the EWG and EDG in the hinge region residues, L398 and E399, as well as in M395 and K350. The two groups with different properties exhibited opposite electrostatic energy and charge transfer energy between L398 and E399. Additionally, we investigated the electron distribution of the parts interacting with the hinge region by visualizing the molecular electrostatic potential (MEP) surface of the compounds. In conclusion, we described the properties of functional groups that affect biological activity using an in silico method, FMO.

Ligand binding: evaluating the contribution of the water molecules network using the Fragment Molecular Orbital method

Water molecules play a crucial role in protein-ligand binding, and many tools exist that aim to predict the position and relative energies of these important, but challenging participants of biomolecular recognition. The available tools are, in general, capable of predicting the location of water molecules. However, predicting the effects of their displacement is still very challenging. In this work, a linear-scaling quantum mechanics-based approach was used to assess water network energetics and the changes in network stability upon ligand structural modifications. This approach offers a valuable way to improve understanding of SAR data and help guide compound design.

Current progress, challenges and future prospects of indazoles as protein kinase inhibitors for the treatment of cancer

The indazole core is an interesting pharmacophore due to its applications in medicinal chemistry. In the past few years, this moiety has been used for the synthesis of kinase inhibitors. Many researchers have demonstrated the use of indazole derivatives as specific kinase inhibitors, including tyrosine kinase and serine/threonine kinases. A number of anticancer drugs with an indazole core are commercially available, e.g. axitinib, linifanib, niraparib, and pazopanib. Indazole derivatives are applied for the targeted treatment of lung, breast, colon, and prostate cancers. In this review, we compile the current development of indazole derivatives as kinase inhibitors and their application as anticancer agents in the past five years.

Dynamic Cooperativity of Ligand-Residue Interactions Evaluated with the Fragment Molecular Orbital Method

By the splendid advance in computation power realized with the Fugaku supercomputer, it has become possible to perform ab initio fragment molecular orbital (FMO) calculations for thousands of dynamic structures of protein-ligand complexes in a parallel way. We thus carried out electron-correlated FMO calculations for a complex of the 3C-like (3CL) main protease (M^pro) of the new coronavirus (SARS-CoV-2) and its inhibitor N3 incorporating the structural fluctuations sampled by classical molecular dynamics (MD) simulation in hydrated conditions. Along with a statistical evaluation of the interfragment interaction energies (IFIEs) between the N3 ligand and the surrounding amino-acid residues for 1000 dynamic structure samples, in this study we applied a novel approach based on principal component analysis (PCA) and singular value decomposition (SVD) to the analysis of IFIE data in order to extract the dynamically cooperative interactions between the ligand and the residues. We found that the relative importance of each residue is modified via the structural fluctuations and that the ligand is bound in the pharmacophore in a dynamic manner through collective interactions formed by multiple residues, thus providing new insight into structure-based drug discovery.

Computational Methods for Biochemical Simulations Implemented in GAMESS

Computational methods for modeling biochemical processes implemented in GAMESS package are reviewed; in particular, quantum mechanics combined with molecular mechanics (QM/MM), semi-empirical, and fragmentation approaches. A detailed summary of capabilities is provided for the QM/MM implementation in QuanPol program and the fragment molecular orbital (FMO) method. Molecular modeling and visualization packages useful for biochemical simulations with GAMESS are described. GAMESS capabilities with corresponding references are tabulated for reader's convenience.

Current and Future Challenges in Modern Drug Discovery

Drug discovery is an expensive, time-consuming, and risky business. To avoid late-stage failure, learnings from past projects and the development of new approaches are crucial. New modalities and emerging new target spaces allow the exploration of unprecedented indications or to address so far undrugable targets. Late-stage attrition is usually attributed to the lack of efficacy or to compound-related safety issues. Efficacy has been shown to be related to a strong genetic link to human disease, a better understanding of the target biology, and the availability of biomarkers to bridge from animals to humans. Compound safety can be improved by ligand optimization, which is becoming increasingly demanding for difficult targets. Therefore, new strategies include the design of allosteric ligands, covalent binders, and other modalities. Design methods currently heavily rely on artificial intelligence and advanced computational methods such as free energy calculations and quantum chemistry. Especially for quantum chemical methods, a more detailed overview is given in this chapter.

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.

Small-Molecule Kinase Inhibitors for the Treatment of Nononcologic Diseases

Great successes have been achieved in developing small-molecule kinase inhibitors as anticancer therapeutic agents. However, kinase deregulation plays essential roles not only in cancer but also in almost all major disease areas. Accumulating evidence has revealed that kinases are promising drug targets for different diseases, including cancer, autoimmune diseases, inflammatory diseases, cardiovascular diseases, central nervous system disorders, viral infections, and malaria. Indeed, the first small-molecule kinase inhibitor for treatment of a nononcologic disease was approved in 2011 by the U.S. FDA. To date, 10 such inhibitors have been approved, and more are in clinical trials for applications other than cancer. This Perspective discusses a number of kinases and their small-molecule inhibitors for the treatment of diseases in nononcologic therapeutic fields. The opportunities and challenges in developing such inhibitors are also highlighted.

Lead optimization of 8-(methylamino)-2-oxo-1,2-dihydroquinolines as bacterial type II topoisomerase inhibitors

The global increase in multidrug-resistant pathogens has caused severe problems in the treatment of infections. To overcome these difficulties, the advent of a new chemical class of antibacterial drug is eagerly desired. We aimed at creating novel antibacterial agents against bacterial type II topoisomerases, which are well-validated targets. TP0480066 (compound 32) has been identified by using structure-based optimization originated from lead compound 1, which was obtained as a result of our previous lead identification studies. The MIC₉₀ values of TP0480066 against methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococci (VRE), and genotype penicillin-resistant Streptococcus pneumoniae (gPRSP) were 0.25, 0.015, and 0.06 μg/mL, respectively. Hence, TP0480066 can be regarded as a promising antibacterial drug candidate of this chemical class.

Measles Virus Hemagglutinin Protein Establishes a Specific Interaction With the Extreme N-Terminal Region of Human Signaling Lymphocytic Activation Molecule to Enhance Infection

Measles virus (MV) is a human pathogen that is classified in the genus Morbillivirus in the family Paramyxoviridae together with several non-human animal morbilliviruses. They cause severe systemic infections by using signaling lymphocytic activation molecule (SLAM) and poliovirus receptor-like 4 expressed on immune and epithelial cells, respectively, as receptors. The viral hemagglutinin (H) protein is responsible for the receptor-binding. Previously determined structures of MV-H and SLAM complexes revealed a major binding interface between the SLAM V domain and MV-H with four binding components (sites 1–4) in the interface. We studied the MV-H and human SLAM (hSLAM) complex structure in further detail by in silico analyses and determined missing regions or residues in the previously determined complex structures. These analyses showed that, in addition to sites 1–4, MV-H establishes a unique interaction with the extreme N-terminal region (ExNTR) of hSLAM. The first principles calculation-based fragment molecular orbital computation method revealed that methionine at position 29 (hSLAM-Met29) is the key residue for the interaction. hSLAM-Met29 was predicted to establish a CH-π interaction with phenylalanine at position 549 of MV-H (MVH-Phe549). A cell-cell fusion assay showed that the hSLAM-Met29 and MVH-Phe549 interaction is important for hSLAM-dependent MV membrane fusion. Furthermore, Jurkat cell lines expressing hSLAM with or without Met29 and recombinant MV possessing the H protein with or without Phe549 showed that the hSLAM-Met29 and MVH-Phe549 interaction enhanced hSLAM-dependent MV infection by ~10-fold. We speculate that in the evolutionary history of morbilliviruses, this interaction may have contributed to MV adaptation to humans because this interaction is unique for MV and only MV uses hSLAM efficiently among morbilliviruses.

Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies

G protein-coupled receptors (GPCRs) are major drug targets due to their ability to facilitate signal transduction across cell membranes, a process that is vital for many physiological functions to occur. The development of computational technology provides modern tools that permit accurate studies of the structures and properties of large chemical systems, such as enzymes and GPCRs, at the molecular level. The advent of multiscale molecular modeling permits the implementation of multiple levels of theories on a system of interest, for instance, assigning chemically relevant regions to high quantum mechanics (QM) level of theory while treating the rest of the system using classical force field (molecular mechanics (MM) potential). Multiscale QM/MM molecular modeling have far-reaching applications in the rational design of GPCR drugs/ligands by affording precise ligand binding configurations through the consideration of conformational plasticity. This enables the identification of key binding site residues that could be targeted to manipulate GPCR function. This review will focus on recent applications of multiscale QM/MM molecular simulations in GPCR studies that could boost the efficiency of future structure-based drug design (SBDD) strategies.

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.

Analyzing Interactions with the Fragment Molecular Orbital Method

Basic concepts in the analysis of binding using the fragment molecular orbital method are discussed at length: polarization, desolvation, and interaction. The components in the pair interaction energy decomposition analysis are introduced, and the analysis is illustrated for a water dimer and a protein-ligand complex.

Fragment-based screening identifies molecules targeting the substrate-binding ankyrin repeat domains of tankyrase

The PARP enzyme and scaffolding protein tankyrase (TNKS, TNKS2) uses its ankyrin repeat clusters (ARCs) to bind a wide range of proteins and thereby controls diverse cellular functions. A number of these are implicated in cancer-relevant processes, including Wnt/β-catenin signalling, Hippo signalling and telomere maintenance. The ARCs recognise a conserved tankyrase-binding peptide motif (TBM). All currently available tankyrase inhibitors target the catalytic domain and inhibit tankyrase's poly(ADP-ribosyl)ation function. However, there is emerging evidence that catalysis-independent "scaffolding" mechanisms contribute to tankyrase function. Here we report a fragment-based screening programme against tankyrase ARC domains, using a combination of biophysical assays, including differential scanning fluorimetry (DSF) and nuclear magnetic resonance (NMR) spectroscopy. We identify fragment molecules that will serve as starting points for the development of tankyrase substrate binding antagonists. Such compounds will enable probing the scaffolding functions of tankyrase, and may, in the future, provide potential alternative therapeutic approaches to inhibiting tankyrase activity in cancer and other conditions.

Investigation of protein-protein interactions and hot spot region between PD-1 and PD-L1 by fragment molecular orbital method

Inhibitors to interfere protein-protein interactions (PPI) between programmed cell death 1 (PD-1) and programmed death ligand-1 (PD-L1) block evasion of cancers from immune surveillance. Analyzing hot spot residues in PPI is important for small-molecule drug development. In order to find out hot spots on PPI interface in PD-1/PD-L1 complex, we analyzed PPI in PD-1/PD-L1 with a new analysis method, 3-dimensional scattered pair interactions energies (3D-SPIEs), which assorts significant interactions with fragment molecular orbital (FMO) method. By additionally analyzing PPI in PD-1/antibody and PD-L1/antibody complexes, and small-ligand interactions in PD-L1/peptide and PD-L1/small-molecule complexes, we narrowed down the hot spot region with 3D-SPIEs-based interaction map, which integrates PPI and small-ligand interactions. Based on the map, there are two hot spot regions in PPI of PD-1/PD-L1 and the first hot spot region is important for inhibitors. In particular, _LY56, _LE58, and _LN66 in the first hot spot of PD-L1 are important for PD-L1-antibodies and small-inhibitors in common, while _LM115 is important for small-inhibitors. Therefore, the 3D-SPIEs-based map would provide valuable information for designing new small-molecule inhibitors to inhibit PPI of PD-1/PD-L1 and the FMO/3D-SPIEs method provides an effectual tool to understand PPI and integrate PPI and small-ligand interactions at a quantum mechanical level.

Investigation of Hot Spot Region in XIAP Inhibitor Binding Site by Fragment Molecular Orbital Method

X-linked inhibitor of apoptosis protein (XIAP) is an important regulator of cancer cell survival whose BIR3 domain (XIAP-BIR3) recognizes the Smac N-terminal tetrapeptide sequence (AVPI), making it an attractive protein-protein interaction (PPI) target for cancer therapies. We used the fragment molecular orbital (FMO) method to study the binding modes and affinities between XIAP-BIR3 and a series of its inhibitors (1-8) that mimic the AVPI binding motif; the inhibitors had common interactions with key residues in a hot spot region of XIAP-BIR3 (P1-P4 subpockets) with increased binding affinity mainly attributed to specific interactions with the P1 and P4 subpockets. Based on the structural information from FMO results, we proposed a novel XIAP natural product inhibitor, neoeriocitrin 10, which was derived from our preciously reported XIAP-BIR3 inhibitor 9, can be used as a highly potent candidate for XIAP-BIR3 inhibition. We also performed pair interaction energy decomposition analysis to investigate the binding energies between specific binding residues and individual ligands, showing that the novel natural product neoeriocitrin 10 had a higher binding affinity than epicatechin gallate 9. Molecular docking and dynamics simulations were performed to explore the mode of binding between 10 and XIAP-BIR3, demonstrating that 10 binds more strongly to the P1 and P4 pockets than 9. Overall, we present a novel natural product, neoeriocitrin 10, and demonstrate that the FMO method can be used to identify hot spots in PPIs and design new compounds for XIAP inhibition.

A Qualified Success: Discovery of a New Series of ATAD2 Bromodomain Inhibitors with a Novel Binding Mode Using High-Throughput Screening and Hit Qualification

The bromodomain of ATAD2 has proved to be one of the least-tractable proteins within this target class. Here, we describe the discovery of a new class of inhibitors by high-throughput screening and show how the difficulties encountered in establishing a screening triage capable of finding progressible hits were overcome by data-driven optimization. Despite the prevalence of nonspecific hits and an exceptionally low progressible hit rate (0.001%), our optimized hit qualification strategy employing orthogonal biophysical methods enabled us to identify a single active series. The compounds have a novel ATAD2 binding mode with noncanonical features including the displacement of all conserved water molecules within the active site and a halogen-bonding interaction. In addition to reporting this new series and preliminary structure-activity relationship, we demonstrate the value of diversity screening to complement the knowledge-based approach used in our previous ATAD2 work. We also exemplify tactics that can increase the chance of success when seeking new chemical starting points for novel and less-tractable targets.

Characterising GPCR-ligand interactions using a fragment molecular orbital-based approach

There has been fantastic progress in solving GPCR crystal structures. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanical approaches (QM) are often too computationally expensive, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule towards ligand binding, including an analysis of their chemical nature.

Recognition of repulsive and attractive regions of selected serotonin receptor binding site using FMO-EDA approach

The complexes of selected long-chain arylpiperazines with homology models of 5-HT_1A, 5-HT_2A, and 5-HT₇ receptors were investigated using quantum mechanical methods. The molecular geometries of the ligand-receptor complexes were firstly optimized with the Our own N-layered Integrated molecular Orbital and molecular Mechanics (ONIOM) method. Next, the fragment molecular orbitals method with an energy decomposition analysis scheme (FMO-EDA) was employed to estimate the interaction energies in binding sites. The results clearly showed that orthosteric binding sites of studied serotonin receptors have both attractive and repulsive regions. In the case of 5-HT_1A and 5-HT_2A two repulsive areas, located in the lower part of the binding pocket, and one large area of attraction engaging many residues at the top of all helices were identified. Additionally, for the 5-HT₇ receptor, the third area of destabilization located at the extracellular end of the helix 6 was found.

Discovery of a Natural-Product-Derived Preclinical Candidate for Once-Weekly Treatment of Type 2 Diabetes

Poor medication adherence is one of the leading causes of suboptimal glycaemic control in approximately half of the patients with type 2 diabetes mellitus (T2DM). Long-acting antidiabetic drugs are clinically needed for improving patients' compliance. Dipeptidyl peptidase-4 (DPP-4) inhibitors play an increasingly important role in the treatment of T2DM because of their favorable properties of weight neutrality and hypoglycemia avoidance. Herein, we report the successful discovery and scale-up synthesis of compound 5, a structurally novel, potent, and long-acting DPP-4 inhibitor for the once-weekly treatment of T2DM. Inhibitor 5 has fast-associating and slow-dissociating binding kinetics profiles as well as slow clearance rate and long terminal half-life pharmacokinetic properties. A single-dose oral administration of 5 (3 mg/kg) inhibited >80% of DPP-4 activity for more than 7 days in diabetic mice. The long-term antidiabetic efficacies of 5 (10 mg/kg, qw) were better than those of the once-weekly trelagliptin and omarigliptin, especially in decreasing the hemoglobin A1c level.

Covalent vs. Non-Covalent Inhibition: Tackling Drug Resistance in EGFR - A Thorough Dynamic Perspective

A persistent challenge in the treatment of non-small cell lung cancer (NSCLC) with EGFR is the emergence of drug-resistant caused by somatic mutations. The EGFR L858R/T790 M double mutant (EGFR^DM ) was found to be the most alarming variant. Despite the development of a wide range of inhibitors, none of them could inhibit EGFR^DM effectively. Recently, 11h and 45a, have been found to be potent inhibitors against EGFR^DM through two distinctive mechanisms, non-covalent and covalent binding, respectively. However, the structural and dynamic implications of the two modes of inhibitions remain unexplored. Herein, two molecular dynamics simulation protocols, coupled with free-energy calculations, were applied to gain insight into the atomistic nature of each binding mode. The comparative analysis confirmed that there is a significant difference in the binding free energy between 11h and 45a (ΔΔG_bind =-21.17 kcal/mol). The main binding force that governs the binding of both inhibitors is vdW, with a higher contribution for 45a. Two residues ARG841 and THR854 were found to have curtailed role in the binding of 45a to EGFR^DM by stabilizing its flexible alcohol chain. The 45a binding to EGFR^DM induces structural rearrangement in the active site to allow easier accessibility of 45a to target residue CYS797. The findings of this work can substantially shed light on new strategies for developing novel classes of covalent and non-covalent inhibitors with increased specificity and potency.

Fragment Molecular Orbital Calculations with Implicit Solvent Based on the Poisson-Boltzmann Equation: II. Protein and Its Ligand-Binding System Studies

In this study, the electronic properties of bioactive proteins were analyzed using an ab initio fragment molecular orbital (FMO) methodology in solution: coupling with an implicit solvent model based on the Poisson-Boltzmann surface area called as FMO-PBSA. We investigated the solvent effects on practical and heterogeneous targets with uneven exposure to solvents unlike deoxyribonucleic acid analyzed in our recent study. Interfragment interaction energy (IFIE) and its decomposition analyses by FMO-PBSA revealed solvent-screening mechanisms that affect local stability inside ubiquitin protein: the screening suppresses excessiveness in bare charge-charge interactions and enables an intuitive IFIE analysis. The electrostatic character and associated solvation free energy also give consistent results as a whole to previous studies on the explicit solvent model. Moreover, by using the estrogen receptor alpha (ERα) protein bound to ligands, we elucidated the importance of specific interactions that depend on the electric charge and activatability as agonism/antagonism of the ligand while estimating the influences of the implicit solvent on the ligand and helix-12 bindings. The predicted ligand-binding affinities of bioactive compounds to ERα also show a good correlation with their in vitro activities. The FMO-PBSA approach would thus be a promising tool both for biological and pharmaceutical research targeting proteins.

Development of an Analysis Toolkit, AnalysisFMO, to Visualize Interaction Energies Generated by Fragment Molecular Orbital Calculations

In modern praxis, a knowledge-driven design of pharmaceutical compounds relies heavily on protein structure data. Nonetheless, quantification of the interaction between protein and ligand is of great importance in the theoretical evaluation of the ability of a pharmaceutical compound to comply with certain expectations. The FMO (fragment molecular orbital) method is handy in this regard. However, the physical complexity and the number of the interactions within a protein-ligand complex renders analysis of the results somewhat complicated. This situation prompted us to develop the 3D-visualization of interaction energies in protein (3D-VIEP) method; the toolkit AnalysisFMO, which should enable a more efficient and convenient workflow with FMO data generated by quantum-chemical packages such as GAMESS, PAICS, and ABINIT-MP. AnalysisFMO consists of two separate units, RbAnalysisFMO, and the PyMOL plugins. The former can extract interfragment interaction energies (IFIEs) or pair interaction energies (PIEs) from the FMO output files generated by the aforementioned quantum-chemical packages. The PyMOL plugins enable visualization of IFIEs or PIEs in the protein structure in PyMOL. We demonstrate the use of this tool on a lectin protein from Burkholderia cenocepacia in which FMO analysis revealed the existence of a new interaction between Gly84 and fucose. Moreover, we found that second-shell interactions are crucial in forming the sugar binding site. In the case of bilirubin oxidase from Myrothecium verrucaria (MvBO), we predict that interactions between Asp105 and three His residues (His401, His403, and His136) are essential for optimally positioning the His residues to coordinate Cu atoms to form one Type 2 and two Type 3 Cu ions.

Fragment Molecular Orbital Study of the Interaction between Sarco/Endoplasmic Reticulum Ca2+-ATPase and its Inhibitor Thapsigargin toward Anti-Malarial Development

Plasmodium falciparum, the causative agent of malignant malaria, is insensitive to thapsigargin (TG), a well-known inhibitor of the human sarco/endoplasmic reticulum Ca²⁺-ATPase (SERCA). To understand the key factor causing the difference of the sensitivity, the molecular interaction of TG and each SERCA was analyzed by the fragment molecular orbital (FMO) method. While the major component of the interaction energy was the nonpolar interaction, the major difference in the molecular interaction arose from the polar interaction, namely, the hydrogen bonding interaction with a hydroxyl group of TG. Additionally, we successfully confirmed these FMO calculation results by measuring the inhibitory activity of a synthesized TG derivative. Our calculations and experiments indicated that, by replacing the hydroxyl group of TG with another functional group, the sensitivities of TG to human and P. falciparum SERCAs can be reversed. This study provides important information to develop antimalarial compounds targeting P. falciparum SERCA.

Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery

GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.