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Owing to the increasing use of computers, computer-aided drug design (CADD) has become an essential component of drug discovery research. In structure-based drug design (SBDD), including inhibitor design and in silico screening of drug target molecules, concordance with wet experimental data is important to provide insights on unique perspectives derived from calculations. Fragment molecular orbital (FMO) method is a quantum chemical method that facilitates precise energy calculations. Fragmentation method makes it possible to apply the quantum chemical method to biological macromolecules for energy calculation based on the electron behavior. Furthermore, interaction energies calculated on a residue-by-residue basis via fragmentation aid in the analysis of interactions between the target and ligand molecule residues and molecular design. In this review, we outline the recent developments in SBDD and FMO methods and highlight the prospects of developing machine learning approaches for large computational data using the FMO method.

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The use of computational methods in drug discovery research has increased substantially in recent years. Computational chemistry techniques, such as quantum chemical calculations and molecular dynamics simulations, continue to be widely used. In this review, we focused on drug discovery-related studies that employ fragment molecular orbital methods. Furthermore, we focused on inhibitor discovery, protein-protein interaction analysis, including antigen-antibody interaction analysis, and integration with molecular dynamics simulations.

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Molecular fragmentation is an effective suite of approaches to reduce the formal computational complexity of quantum chemistry calculations while enhancing their algorithmic parallelisability. However, the practical applicability of fragmentation techniques remains hindered by a dearth of automation and effective metrics to assess the quality of a fragmentation scheme. In this article, we present the Quick Fragmentation via Automated Genetic Search (QFRAGS), a novel automated fragmentation algorithm that uses a genetic optimisation procedure to generate molecular fragments that yield low energy errors when adopted in Many Body Expansions (MBEs). Benchmark testing of QFRAGS on protein systems with less than 500 atoms, using two-body (MBE2) and three-body (MBE3) MBE calculations at the HF/6-31G* level, reveals mean absolute energy errors (MAEE) of 20.6 and 2.2 kJ  mol - 1 , respectively. For larger protein systems exceeding 500 atoms, MAEEs are 181.5 kJ  mol - 1 for MBE2 and 24.3 kJ  mol - 1 for MBE3. Furthermore, when compared to three manual fragmentation schemes on a 40-protein dataset, using both MBE and Fragment Molecular Orbital techniques, QFRAGS achieves comparable or often lower MAEEs. When applied to a 10-lipoglycan/glycolipid dataset, MAEs of 7.9 and 0.3 kJ  mol - 1 were observed at the MBE2 and MBE3 levels, respectively.Scientific Contribution This Article presents the Quick Fragmentation via Automated Genetic Search (QFRAGS), an innovative molecular fragmentation algorithm that significantly improves upon existing molecular fragmentation approaches by specifically addressing their lack of automation and effective fragmentation quality metrics. With an evolutionary optimisation strategy, QFRAGS actively pursues high quality fragments, generating fragmentation schemes that exhibit minimal energy errors on systems with hundreds to thousands of atoms. The advent of QFRAGS represents a significant advancement in molecular fragmentation, greatly improving the accessibility and computational feasibility of accurate quantum chemistry calculations.

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The fragment molecular orbital (FMO) scheme is one of the popular fragmentation-based methods and has the potential advantage of making the circuit shallow for quantum chemical calculations on quantum computers. In this study, we used a GPU-accelerated quantum simulator (cuQuantum) to perform the electron correlation part of the FMO calculation as unitary coupled-cluster singles and doubles (UCCSD) with the variational quantum eigensolver (VQE) for hydrogen-bonded (FH) 3 and (FH) 2 -H 2 O systems with the STO-3G basis set. VQE-UCCSD calculations were performed using both canonical and localized MO sets, and the results were examined from the point of view of size-consistency and orbital-invariance affected by the Trotter error. It was found that the use of localized MO leads to better results, especially for (FH) 2 -H 2 O. The GPU acceleration was substantial for the simulations with larger numbers of qubits, and was about a factor of 6.7-7.7 for 18 qubit systems.

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The Hippo pathway controls organ size and homeostasis and is linked to numerous diseases, including cancer. The transcriptional enhanced associate domain (TEAD) family of transcription factors acts as a receptor for downstream effectors, namely yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), which binds to various transcription factors and is essential for stimulated gene transcription. YAP/TAZ-TEAD facilitates the upregulation of multiple genes involved in evolutionary cell proliferation and survival. TEAD1-4 overexpression has been observed in different cancers in various tissues, making TEAD an attractive target for drug development. The central drug-accessible pocket of TEAD is crucial because it undergoes a post-translational modification called auto-palmitoylation. Crystal structures of the C-terminal TEAD complex with small molecules are available in the Protein Data Bank, aiding structure-based drug design. In this study, we utilized the fragment molecular orbital (FMO) method, molecular dynamics (MD) simulations, shape-based screening, and molecular mechanics-generalized Born surface area (MM-GBSA) calculations for virtual screening, and we identified a novel non-covalent inhibitor-BC-001-with IC50 = 3.7 µM in a reporter assay. Subsequently, we optimized several analogs of BC-001 and found that the optimized compound BC-011 exhibited an IC50 of 72.43 nM. These findings can be used to design effective TEAD modulators with anticancer therapeutic implications.

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Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

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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.

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The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.

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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).

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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.

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It is known that the recommended dietary allowance of selenium (Se) is dangerously close to its tolerable upper intake level. Se is detoxified and excreted in urine as trimethylselenonium ion (TMSe) when the amount ingested exceeds the nutritional level. Recently, we demonstrated that the production of TMSe requires two methyltransferases: thiopurine S-methyltransferase (TPMT) and indolethylamine N-methyltransferase (INMT). In this study, we investigated the substrate recognition mechanisms of INMT and TPMT in the Se-methylation reaction. Examination of the Se-methyltransferase activities of two paralogs of INMT, namely, nicotinamide N-methyltransferase and phenylethanolamine N-methyltransferase, revealed that only INMT exhibited Se-methyltransferase activity. Consistently, molecular dynamics simulations demonstrated that dimethylselenide was preferentially associated with the active center of INMT. Using the fragment molecular orbital method, we identified hydrophobic residues involved in the binding of dimethylselenide to the active center of INMT. The INMT-L164R mutation resulted in a deficiency in Se- and N-methyltransferase activities. Similarly, TPMT-R152, which occupies the same position as INMT-L164, played a crucial role in the Se-methyltransferase activity of TPMT. Our findings suggest that TPMT recognizes negatively charged substrates, whereas INMT recognizes electrically neutral substrates in the hydrophobic active center embedded within the protein. These observations explain the sequential requirement of the two methyltransferases in producing TMSe.

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Energy decomposition analysis is one of the most attractive features of fragment molecular orbital (FMO) calculations from the point of view of practical applications. Here we report some enhancements for PIEDA in the ABINIT-MP program. One is a separation of the dispersion-type stabilization from the electron correlation energy, traditionally referred to as the "dispersion interaction" (DI). Another is an alternative evaluation of the electrostatic (ES) interaction using the restrained electrostatic potential (RESP) charges. The GA:CT stacked base pair and the Trp-Cage miniprotein were used as illustrative examples.

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Tuberculosis (TB), the second leading infectious killer, causes serious public health problems worldwide. To develop novel anti-TB agents, many biochemical studies have targeted the subunit B of DNA gyrase (GyrB), which captures a second DNA segment and responses for ATP hydrolysis. Here, we investigated specific interactions between GyrB residues and existing pyrrolamide derivatives at an electronic level using ab initio fragment molecular orbital (FMO) calculations and designed potent inhibitors against GyrB. The evaluated binding affinities between GyrB and pyrrolamides were confirmed to be consistent with the IC50 values obtained from previous experiments. Thus, we employed the most potent pyrrolamide (compound 1) as a lead compound and proposed novel pyrrolamide derivatives. The specific interactions between GyrB and these derivatives were investigated using molecular mechanic optimizations and FMO calculations. The results revealed that our proposed derivatives had strong hydrogen bonds with Asp79 and Arg141 and exhibited electrostatic interactions with Glu56 and Ile84 of GyrB. In addition, the binding affinity between GyrB and compound 1 was enhanced significantly by the replacement at the R3 site of compound 1. The present results may provide structural concepts for the rational design of potent GyrB inhibitors as anti-TB agents.Communicated by Ramaswamy H. Sarma.

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Alzheimer's disease (AD) is a neurological disease, and its signs and symptoms appear slowly over time. Although current Alzheimer's disease treatments can alleviate symptoms, they cannot prevent the disease from progressing. To accurately diagnose and treat Alzheimer's disease, it is therefore necessary to establish effective methods for diagnosis. Apolipoprotein E4 (ApoE4), the most frequent genetic risk factor for AD, is expressed in more than half of patients with AD, making it an attractive target for AD therapy. We used molecular docking simulations, classical molecular mechanics optimizations, and ab initio fragment molecular orbital (FMO) calculations to investigate the specific interactions between ApoE4 and the naturally occurring compounds found in the plant Moringa Oleifera. According to the FMO calculations, quercetin had the highest binding affinity to ApoE4 among the sixteen compounds because its hydroxyl groups generated strong hydrogen bonds with the ApoE4 residues Trp11, Asp12, Arg15, and Asp130. As a result, we proposed various quercetin derivatives by introducing a hydroxyl group into quercetin and studied their ApoE4 binding properties. The FMO data clearly showed that adding a hydroxyl group to quercetin improved its binding capacity to ApoE4. Furthermore, ApoE4 Trp11, Asp12, Arg15, and Asp130 residues were discovered to be required for significant interactions between ApoE4 and quercetin derivatives. They had a higher ApoE4 binding affinity than our previously proposed epicatechin derivatives. Accordingly, the current results evaluated using the ab initio FMO method will be useful for designing potent ApoE4 inhibitors that can be used as a candidate agent for AD treatment.

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Polo-like kinase 1 (PLK1) plays a pivotal role in cell division regulation and emerges as a promising therapeutic target for cancer treatment. Consequently, the development of small-molecule inhibitors targeting PLK1 has become a focal point in contemporary research. The adenosine triphosphate (ATP)-binding site and the polo-box domain in PLK1 present crucial interaction sites for these inhibitors, aiming to disrupt the protein's function. However, designing potent and selective small-molecule inhibitors can be challenging, requiring a deep understanding of protein-ligand interaction mechanisms at these binding sites. In this context, our study leverages the fragment molecular orbital (FMO) method to explore these site-specific interactions in depth. Using the FMO approach, we used the FMO method to elucidate the molecular mechanisms of small-molecule drugs binding to these sites to design PLK1 inhibitors that are both potent and selective. Our investigation further entailed a comparative analysis of various PLK1 inhibitors, each characterized by distinct structural attributes, helping us gain a better understanding of the relationship between molecular structure and biological activity. The FMO method was particularly effective in identifying key binding features and predicting binding modes for small-molecule ligands. Our research also highlighted specific "hot spot" residues that played a critical role in the selective and robust binding of PLK1. These findings provide valuable insights that can be used to design new and effective PLK1 inhibitors, which can have significant implications for developing anticancer therapeutics.

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The NK1 receptor (NK1R) is a molecular target for both approved and experimental drugs intended for a variety of conditions, including emesis, pain, and cancers. While contemplating modifications to the typical NK1R pharmacophore, we wondered whether the CF3 groups common for many NK1R ligands, could be replaced with some other moiety. Our attention was drawn by the SF5 group, and so we designed, synthesized, and tested ten novel SF5 -containing compounds for NK1R affinity. All analogues exhibit detectable NK1R binding, with the best of them, compound 5 a, (3-bromo-5-(pentafluoro-λ6 -sulfanyl)benzyl acetyl-L-tryptophanate) binding only slightly worse (IC50 =34.3 nM) than the approved NK1R-targeting drug, aprepitant (IC50 =27.7 nM). Molecular docking provided structural explanation of SAR. According to our analysis, the SF5 group in our compounds occupies a position similar to that of one of the CF3 groups of aprepitant as found in the crystal structure. Additionally, we checked whether the docking scoring function or energies derived from Fragment Molecular Orbital quantum chemical calculations may be helpful in explaining and predicting the experimental receptor affinities for our analogues. Both these methods produce moderately good results. Overall, this is the first demonstration of the utility of the SF5 group in the design of NK1R ligands.

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Heparanase-1 (HPSE1) is an endo-ß-d-glucuronidase that is the only mammalian enzyme known to cleave heparan sulfate (HS) of heparan sulfate proteoglycans (HSPG), a key component of the glycocalyx layer of the vascular endothelium matrix. Inhibition of HPSE1 has therapeutic potential for cancer and proteinuric kidney diseases. We previously reported that 2 showed a moderate potency as an HPSE1 inhibitor and an issue of selectivity against exo-ß-d-glucuronidase (GUSß) and glucocerebrosidase (GBA) remained. A structure-based lead optimization of 2 using X-ray co-crystal structure analysis and fragment molecular orbital calculation resulted in 4e, which showed a more than 7-fold increase in HPSE1 inhibitory activity. The subsequent introduction of a methyl group into the 6-hydroxy group of 4e resulted in 18 with reduced inhibitory activities against GUSß and GBA while maintaining the inhibitory activity against HPSE1. The inhibitory activities of 18 against serum HPSE1 in mice were significant and lasted for 4 h at doses of 3, 30, and 100 mg/kg. Compound 18 could be a novel lead compound for HPSE1 inhibitors with improved inhibitory activity against HPSE1 and increased HPSE1 selectivity over GUSß and GBA.

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The ATR pathway plays a crucial role in maintaining genome integrity as the major DNA damage checkpoint. It also attracts attention as a therapeutic target in cancer treatment. The Rad17-RFC2-5 complex loads the Rad9-Hus1-Rad1 (9-1-1) DNA clamp complex onto damaged chromatin to activate the ATR pathway. We previously reported that phosphorylation of a polyanionic C-terminal tail of human Rad17, iVERGE, is essential for the interaction between Rad17 and the 9-1-1 complex. However, the molecular mechanism has remained unclear. Here, we show that iVERGE directly interacts with the Hus1 subunit of the 9-1-1 complex through Rad17-S667 phosphorylation independently of the AAA+ ATPase domains. An exogenous iVERGE peptide interacted with the 9-1-1 complex in vivo. The binding conformation of the iVERGE peptide was analyzed by de novo modeling with docking simulation, simulated annealing-molecular dynamics simulation, and the fragment molecular orbital method. The in silico analyses predicted the association of the iVERGE peptide with the hydrophobic and basic patches on the Hus1 protein, and the corresponding Hus1 mutants were deficient in the interaction with the iVERGE peptide in vivo. The iVERGE peptide occupied the same position as the C-terminus of Saccharomyces cerevisiae RAD24 on MEC3. The interaction energy calculation suggested that the Rad17 KYxxL motif and the iVERGE peptide are the primary and secondary interaction surfaces between the Rad17-RFC2-5 and 9-1-1 complexes. Our data reveal a novel molecular interface, iVERGE, between the Rad17-RFC2-5 and 9-1-1 complexes in vertebrates and implicate that Rad17 utilizes two distinct molecular interfaces to regulate the 9-1-1 complex.

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Alzheimer's disease (AD), one of the most common neurodegenerative diseases, is a major factor contributing to cognitive impairment in older adults. Current therapeutic treatments can only relieve the symptoms of AD, but they cannot stop the progression of the disease because it takes a long time for clinical symptoms to manifest. Therefore, it is essential to develop effective diagnostic strategies for early detection and treatment of AD. As the most common genetic risk factor for AD, apolipoprotein E4 (ApoE4) is present in more than half of patients with AD, and it can be a target protein for AD therapy. We used molecular docking, classical molecular mechanics optimizations, and ab initio fragment molecular orbital (FMO) calculations to investigate the specific interactions between ApoE4 and Cinnamon-derived compounds. Of the 10 compounds, epicatechin was found to have the highest binding affinity to ApoE4 because the hydroxyl groups of epicatechin form strong hydrogen bonds with the Asp130 and Asp12 residues of ApoE4. Therefore, we proposed some epicatechin derivatives by adding a hydroxyl group to epicatechin and studied their interactions with ApoE4. The FMO results indicate that the addition of a hydroxyl group to epicatechin increases its binding affinity to ApoE4. It is also revealed that the Asp130 and Asp12 residues of ApoE4 are important for the binding between ApoE4 and the epicatechin derivatives. These findings will help propose potent inhibitors against ApoE4, leading to a proposal for effective therapeutic candidates for AD.

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The fragment molecular orbital (FMO) method is a fast quantum-mechanics method that divides systems into pieces of fragments and performs ab initio calculations. The method has been expected to improve the accuracy of describing protein-ligand interactions by incorporating electronic effects. In this article, FMO calculation with solvation methods were applied to the affinity prediction at the ATP-binding site of PDHK4. As the ionized aspartic acid lies at the center and is involved in the complex hydrogen bond networks, this system has turned out to be a difficult target to describe by traditional molecular-mechanics method. In the FMO calculation with the polarizable continuum model (PCM) solvation method, a considerable amount of charge (-0.27e) was transferred from the ionized aspartate to the surrounding residues. We found that using FMO with the PCM solvation method was important to increase the correlation, and by incorporating the ligand deformation energy, the correlation was improved to R = 0.81 for whole twelve compounds and R = 0.91 without one outlier compound.

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