(5S)-5-Benzylswainsonines as potent and selective inhibitors of Golgi α-mannosidase II: synthesis, enzyme evaluation and molecular modelling

Development of novel anti-cancer therapeutics based on Golgi α-mannosidase II (GMII) inhibition is considerably impeded by an undesired co-inhibition of lysosomal α-mannosidase leading to severe side-effects. In this contribution, we describe a fully stereoselective synthesis of (5S)-5-[4-(halo)benzyl]swainsonines as highly potent and selective inhibitors of GMII. The synthesis starts from a previously reported aldehyde readily available from l-ribose, and the key features include an intramolecular reductive amination with substrate-controlled stereoselectivity and a late-stage derivatisation of the benzyl group via ipso-substitution. These novel swainsonine analogues were found to be nanomolar inhibitors of the Golgi-type α-mannosidase AMAN-2 (Ki = 23-75 nM) with excellent selectivity (selectivity index = 205-870) over the lysosomal-type Jack bean α-mannosidase. Finally, molecular docking and pKa calculations were performed to provide more insight into the structure of the inhibitor:enzyme complexes, and a pair interaction energy analysis (FMO-PIEDA) was carried out to rationalise the observed potency and selectivity of the inhibitors.

Massively Parallel Computational Chemistry with the Super Instruction Architecture and ACES4

The task of developing high-performing parallel software must be made easier and more cost-effective in order to fully exploit existing and emerging large-scale computer systems for the advancement of science. The Super Instruction Architecture (SIA) is a parallel programming platform geared toward applications that need to manage large amounts of data stored in potentially sparse multidimensional arrays during calculations. The SIA platform was originally designed for the quantum chemistry software package ACESIII. More recently, the SIA was reimplemented to overcome the limitations in the original ACESIII program. It has now been successfully employed in the new ACES4 quantum chemistry software package. This paper describes the SIA and ACES4 and illustrates their capabilities with some difficult quantum chemistry open-shell coupled-cluster benchmark calculations.

Many-Body Expansion-Based Quantum Mechanical Force Field for Cyclotrimethylene Trinitramine under High Pressure

RDX undergoes pressures of approximately 30-50 GPa during detonation, leading to significant changes in intermolecular interactions. Accurately describing these interactions is crucial for understanding the energy transfer in the detonation process. To address this, this work introduces a many-body expansion-based quantum mechanical force field (MB-QMFF) to accurately describe RDX's intermolecular interactions under high pressures. Using MB-QMFF, we evaluated various density functionals and found that the M062X functional with GD3 dispersion correction provided the highest accuracy. Regarding intermolecular forces, two-body interactions were the most significant, with three-body interactions being negligible. Additionally, we investigated intermolecular energy variations at different densities (or pressures). The results clearly demonstrate an accurate description of intermolecular interactions by the MB-QMFF scheme. Therefore, we believe that the MB-QMFF scheme can serve as a foundation for the development of RDX-specific force fields and pave the way for future studies on the detonation process of RDX.

Quantum-centric high performance computing for quantum chemistry

High performance computing (HPC) is renowned for its capacity to tackle complex problems. Meanwhile, quantum computing (QC) provides a potential way to accurately and efficiently solve quantum chemistry problems. The emerging field of quantum-centric high performance computing (QCHPC), which merges these two powerful technologies, is anticipated to enhance computational capabilities for solving challenging problems in quantum chemistry. The implementation of QCHPC for quantum chemistry requires interdisciplinary research and collaboration across multiple fields, including quantum chemistry, quantum physics, computer science and so on. This perspective provides an introduction to the quantum algorithms that are suitable for deployment in QCHPC, focusing on conceptual insights rather than technical details. Parallel strategies to implement these algorithms on quantum-centric supercomputers are discussed. We also summarize high performance quantum emulating simulators, which are considered a viable tool to explore QCHPC. We conclude with challenges and outlooks in this field.

Enhancement of energy decomposition analysis in fragment molecular orbital calculations

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.

Towards tailoring hydrophobic interaction with uranyl(VI) oxygen for C-H activation

Bovine serum albumin (BSA) has a uranyl(VI) binding hotspot where uranium is tightly bound by three carboxylates. Uranyl oxygen is "soaked" into the hydrophobic core of BSA. Isopropyl hydrogen of Val is trapped near UO22+ and upon photoexcitation, C-H bond cleavage is initiated. A unique hydrophobic contact with "yl"-oxygen, as observed here, can be used to induce C-H activation.

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.

Prediction of Binding Pose and Affinity of Nelfinavir, a SARS-CoV-2 Main Protease Repositioned Drug, by Combining Docking, Molecular Dynamics, and Fragment Molecular Orbital Calculations

A novel in silico drug design procedure is described targeting the Main protease (Mpro) of the SARS-CoV-2 virus. The procedure combines molecular docking, molecular dynamics (MD), and fragment molecular orbital (FMO) calculations. The binding structure and properties of Mpro were predicted for Nelfinavir (NFV), which had been identified as a candidate compound through drug repositioning, targeting Mpro. Several poses of the Mpro and NFV complexes were generated by docking, from which four docking poses were selected by scoring with FMO energy. Then, each pose was subjected to MD simulation, 100 snapshot structures were sampled from each of the generated MD trajectories, and the structures were evaluated by FMO calculations to rank the pose based on binding energy. Several residues were found to be important in ligand recognition, including Glu47, Asp48, Glu166, Asp187, and Gln189, all of which interacted strongly with NFV. Asn142 is presumably regarded to form hydrogen bonds or CH/π interaction with NFV; however, in the present calculation, their interactions were transient. Moreover, the tert-butyl group of NFV had no interaction with Mpro. Identifying such strong and weak interactions provides candidates for maintaining and substituting ligand functional groups and important suggestions for drug discovery using drug repositioning. Besides the interaction between NFV and the amino acid residues of Mpro, the desolvation effect of the binding pocket also affected the ranking order. A similar procedure of drug design was applied to Lopinavir, and the calculated interaction energy and experimental inhibitory activity value trends were consistent. Our approach provides a new guideline for structure-based drug design starting from a candidate compound whose complex crystal structure has not been obtained.

Fragment molecular orbital-based variational quantum eigensolver for quantum chemistry in the age of quantum computing

Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQE) was proposed to address these issues, its scalability remains limited. Many efforts, including new ansätze and Hamiltonian modifications, have been made to overcome these challenges. In this work, we introduced the novel Fragment Molecular Orbital/Variational Quantum Eigensolver (FMO/VQE) algorithm. This method combines the fragment molecular orbital (FMO) approach with VQE and efficiently utilizes qubits for quantum chemistry simulations. Employing the UCCSD ansatz, the FMO/VQE achieved an absolute error of just 0.053 mHa with 8 qubits in a [Formula: see text] system using the STO-3G basis set, and an error of 1.376 mHa with 16 qubits in a [Formula: see text] system with the 6-31G basis set. These results indicated a significant advancement in scalability over conventional VQE, maintaining accuracy with fewer qubits. Therefore, our FMO/VQE method exemplifies how integrating fragment-based quantum chemistry with quantum algorithms can enhance scalability, facilitating more complex molecular simulations and aligning with quantum computing advancements.

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.

High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques

Structure-based drug design (SBDD) is rapidly evolving to be a fundamental tool for faster and more cost-effective methods of lead drug discovery. SBDD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This "virtual screening" technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of different computational techniques to determine which potential candidates are likely to bind with high affinity and efficacy. It is proposed that high-throughput SBDD (HT-SBDD) will significantly enrich the success rate of HTS methods, which currently fluctuates around ~1%. In this chapter, we focus on the theory and utility of high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics techniques. We also offer a comparative review of the benefits and limitations of traditional methods against more recent SBDD advances. As HT-SBDD is computationally intensive, we will also cover the important role high-performance computing (HPC) clusters play in the future of computational drug discovery.

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.

Leveraging the Fragment Molecular Orbital Method to Explore the PLK1 Kinase Binding Site and Polo-Box Domain for Potent Small-Molecule Drug Design

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.

Structure-based lead optimization to improve potency and selectivity of a novel tetrahydroimidazo[1,2-a]pyridine-5-carboxylic acid series of heparanase-1 inhibitor

Heparanase-1 (HPSE1) is an endo-β-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-β-d-glucuronidase (GUSβ) 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 GUSβ 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 GUSβ and GBA.

Toward an extreme-scale electronic structure system

Electronic structure calculations have the potential to predict key matter transformations for applications of strategic technological importance, from drug discovery to material science and catalysis. However, a predictive physicochemical characterization of these processes often requires accurate quantum chemical modeling of complex molecular systems with hundreds to thousands of atoms. Due to the computationally demanding nature of electronic structure calculations and the complexity of modern high-performance computing hardware, quantum chemistry software has historically failed to operate at such large molecular scales with accuracy and speed that are useful in practice. In this paper, novel algorithms and software are presented that enable extreme-scale quantum chemistry capabilities with particular emphasis on exascale calculations. This includes the development and application of the multi-Graphics Processing Unit (GPU) library LibCChem 2.0 as part of the General Atomic and Molecular Electronic Structure System package and of the standalone Extreme-scale Electronic Structure System (EXESS), designed from the ground up for scaling on thousands of GPUs to perform high-performance accurate quantum chemistry calculations at unprecedented speed and molecular scales. Among various results, we report that the EXESS implementation enables Hartree-Fock/cc-pVDZ plus RI-MP2/cc-pVDZ/cc-pVDZ-RIFIT calculations on an ionic liquid system with 623 016 electrons and 146 592 atoms in less than 45 min using 27 600 GPUs on the Summit supercomputer with a 94.6% parallel efficiency.

Structural and Computational Analyses of the Unique Interactions of Opicapone in the Binding Pocket of Catechol O-Methyltransferase: A Crystallographic Study and Fragment Molecular Orbital Analyses

A third-generation inhibitor of catechol O-methyltransferase (COMT), opicapone (1), has the 3-nitrocatechol scaffold as do the second-generation inhibitors such as entacapone (2) and tolcapone (3), but only 1 can sustainably inhibit COMT activity making it suitable for a once-daily regimen. These improvements should be attributed to the optimized sidechain moiety (oxidopyridyloxadiazolyl group) of 1 substituted at the 5-position of the 3-nitrocatechol ring. We analyzed the role of the sidechain moiety by solving the crystal structures of COMT/S-adenosylmethionine (SAM)/Mg/1 and COMT/S-adenosylhomocysteine (SAH)/Mg/1 complexes. Fragment molecular orbital (FMO) calculations elucidated that the dispersion interaction between the sidechains of Leu 198 and Met 201 on the β6β7-loop and the oxidopyridine ring of 1 were unique and important in both complexes. In contrast, the catechol binding site made a remarkable difference in the sidechain conformation of Lys 144. The ε-amino group of Lys 144 was outside of the catalytic pocket and was replaced by a water molecule in the COMT/SAH/Mg/1 complex. No nitrocatechol inhibitor has ever been reported to make a complex with COMT and SAH. Thus, the conformational change of Lys 144 found in the COMT/SAH/Mg/1 complex is the first crystallographic evidence that supports the role of Lys 144 as a catalytic base to take out a proton ion from the reaction site to the outside of the enzyme. The fact that 1 generated a complex with SAH and COMT also suggests that 1 could inhibit COMT twofold, as a typical substrate mimic competitive inhibitor and as a product-inhibition enhancer.

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.

Predicting Kinetics and Dynamics of Spin-Dependent Processes

ConspectusPredicting mechanisms and rates of nonadiabatic spin-dependent processes including photoinduced intersystem crossings, thermally activated spin-forbidden reactions, and spin crossovers in metal centers is a very active field of research. These processes play critical roles in transition-metal-based and metalloenzymatic catalysis, molecular magnets, light-harvesting materials, organic light-emitting diodes, photosensitizers for photodynamic therapy, and many other applications. Therefore, accurate modeling of spin-dependent processes in complex systems and on different time scales is important for many problems in chemistry, biochemistry, and materials sciences.Nonadiabatic statistical theory (NAST) and nonadiabatic molecular dynamics (NAMD) are two complementary approaches to modeling the kinetics and dynamics of spin-dependent processes. NAST predicts the probabilities and rate constants of nonradiative transitions between electronic states with different spin multiplicities using molecular properties at only few critical points on the potential energy surfaces (PESs), including the reactant minimum and the minimum energy crossing point (MECP) between two spin states. This makes it possible to obtain molecular properties for NAST calculations using accurate but often computationally expensive electronic structure methods, which is critical for predicting the rate constants of spin-dependent processes. Alternatively, NAST can be used to study spin-dependent processes in very large complex molecular systems using less computationally expensive electronic structure methods. The nuclear quantum effects, such as zero-point vibrational energy, tunneling, and interference between reaction paths can be easily incorporated. However, the statistical and local nature of NAST makes it more suitable for large systems and slow kinetics. In contrast, NAMD explores entire PESs of interacting electronic states, making it ideal for modeling fast barrierless spin-dependent processes. Because the knowledge of large portions of PESs is often needed, the simulations require a very large number of electronic structure calculations, which limits the NAMD applicability to relatively small molecular systems and ultrafast kinetics.In this Account, we discuss our contribution to the development of the NAST and NAMD approaches for predicting the rates and mechanism of spin-dependent processes. First, we briefly describe our NAST and NAMD implementations. The NAST implementation is an extension of the transition state theory to the processes involving two crossing potential energy surfaces of different spin multiplicities. The NAMD approach includes the trajectory surface hopping (TSH) and ab initio multiple spawning (AIMS) methods. Second, we discuss several applications of NAST and NAMD to model spin-dependent processes in different systems. The NAST applicability to large complex systems is demonstrated by the studies of the spin-forbidden isomerization of the active sites of metal-sulfur proteins. Our implementation of the MECP search algorithm within the fully ab initio fragment molecular orbital method allows applying NAST to systems with thousands of atoms, such as the solvated protein rubredoxin. Applications of NAMD to ultrafast spin-dependent processes are represented by the generalized AIMS simulations utilizing the fast GPU-based TeraChem electronic structure program to gain insight into the complex photoexcited state relaxation in 2-cyclopentenone.

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.

Molecular Modeling Insights into the Structure and Behavior of Integrins: A Review

Integrins are heterodimeric glycoproteins crucial to the physiology and pathology of many biological functions. As adhesion molecules, they mediate immune cell trafficking, migration, and immunological synapse formation during inflammation and cancer. The recognition of the vital roles of integrins in various diseases revealed their therapeutic potential. Despite the great effort in the last thirty years, up to now, only seven integrin-based drugs have entered the market. Recent progress in deciphering integrin functions, signaling, and interactions with ligands, along with advancement in rational drug design strategies, provide an opportunity to exploit their therapeutic potential and discover novel agents. This review will discuss the molecular modeling methods used in determining integrins' dynamic properties and in providing information toward understanding their properties and function at the atomic level. Then, we will survey the relevant contributions and the current understanding of integrin structure, activation, the binding of essential ligands, and the role of molecular modeling methods in the rational design of antagonists. We will emphasize the role played by molecular modeling methods in progress in these areas and the designing of integrin antagonists.

An efficient electrostatic embedding QM/MM method using periodic boundary conditions based on particle-mesh Ewald sums and electrostatic potential fitted charge operators

Hybrid quantum mechanics/molecular mechanics (QM/MM) models are successful at describing the properties and reactivity of biological macromolecules. Combining ab initio QM/MM methods and periodic boundary conditions (PBC) is currently the optimal approach for modeling chemical processes in an infinite environment, but frequently, these models are too time-consuming for general applicability to biological systems in a solution. Here, we define a simple and efficient electrostatic embedding QM/MM model in PBC, combining the benefits of electrostatic potential fitted atomic charges and particle-mesh Ewald sums, which can efficiently treat systems of an arbitrary size at a reasonable computational cost. To illustrate this, we apply our scheme to extract the lowest singlet excitation energies from a model for Arabidopsis thaliana cryptochrome 1 containing circa 93 000 atoms, accurately reproducing the experimental absorption maximum.

An Embedded Fragment Method for Molecules in Strong Magnetic Fields

An extension of the embedded fragment method for calculations on molecular clusters is presented, which includes strong external magnetic fields. The approach is flexible, allowing for calculations at the Hartree-Fock, current-density-functional theory, Møller-Plesset perturbation theory, and coupled-cluster levels using London atomic orbitals. For systems consisting of discrete molecular subunits, calculations using London atomic orbitals can be performed in a computationally tractable manner for systems beyond the reach of conventional calculations, even those accelerated by resolution-of-the-identity or Cholesky decomposition methods. To assess the applicability of the approach, applications to water clusters are presented, showing how strong magnetic fields enhance binding within the clusters. However, our calculations suggest that, contrary to previous suggestions in the literature, this enhanced binding may not be directly attributable to strengthening of hydrogen bonding. Instead, these results suggest that this arises for larger field strengths as a response of the system to the presence of the external field, which induces a charge density build up between the monomer units. The approach is embarrassingly parallel and its computational tractability is demonstrated for clusters of up to 103 water molecules in triple-ζ basis sets, which would correspond to conventional calculations with more than 12 000 basis functions.

Boosting the Full Potential of PyMOL with Structural Biology Plugins

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses.

1,4-Dideoxy-1,4-imino-D- and L-lyxitol-based inhibitors bind to Golgi α-mannosidase II in different protonation forms

The development of effective inhibitors of Golgi α-mannosidase II (GMII, E.C.3.2.1.114) with minimal off-target effects on phylogenetically-related lysosomal α-mannosidase (LMan, E.C.3.2.1.24) is a complex task due to the complicated structural and chemical properties of their active sites. The pKa values (and also protonation forms in some cases) of several ionizable amino acids, such as Asp, Glu, His or Arg of enzymes, can be changed upon the binding of the inhibitor. Moreover, GMII and LMan work under different pH conditions. The pKa calculations on large enzyme-inhibitor complexes and FMO-PIEDA energy decomposition analysis were performed on the structures of selected inhibitors obtained from docking and hybrid QM/MM calculations. Based on the calculations, the roles of the amino group incorporated in the ring of the imino-D-lyxitol inhibitors and some ionizable amino acids of Golgi-type (Asp270-Asp340-Asp341 of Drosophila melanogaster α-mannosidase dGMII) and lysosomal-type enzymes (His209-Asp267-Asp268 of Canavalia ensiformis α-mannosidase, JBMan) were explained in connection with the observed inhibitory properties. The pyrrolidine ring of the imino-D-lyxitols prefers at the active site of dGMII the neutral form while in JBMan the protonated form, whereas that of imino-L-lyxitols prefers the protonation form in both enzymes. The calculations indicate that the binding mechanism of inhibitors to the active-site of α-mannosidases is dependent on the inhibitor structure and could be used to design new selective inhibitors of GMII. A series of novel synthetic N-substituted imino-D-lyxitols were evaluated with four enzymes from the glycoside hydrolase GH38 family (two of Golgi-type, Drosophila melanogaster GMIIb and Caenorhabditis elegans AMAN-2, and two of lysosomal-type, Drosophila melanogaster LManII and Canavalia ensiformis JBMan, enzymes). The most potent structures [N-9-amidinononyl and N-2-(1-naphthyl)ethyl derivatives] inhibited GMIIb (Ki = 40 nM) and AMAN-2 (Ki = 150 nM) with a weak selectivity index (SI) toward Golgi-type enzymes of IC50(LManII)/IC50(GMIIb) = 35 or IC50(JBMan)/IC50(AMAN-2) = 86. On the other hand, weaker micromolar inhibitors, such as N-2-naphthylmethyl or 4-iodobenzyl derivatives [IC50(GMIIb) = 2.4 μM and IC50 (AMAN-2) = 7.6 μM], showed a significant SI in the range from 111 to 812.

Interaction between the Human OX2 Orexin Receptor and Suvorexant and Some of Its Analogues: SAPT (DFT) Interaction Energy Decomposition Analysis

In this study, the interaction energy (E_int) of suvorexant (as an orexin receptor antagonist) and some of its analogues with the important residues of the human OX2 orexin receptor, determined by molecular docking, is calculated using the symmetry-adapted perturbation theory-density functional theory (SAPT (DFT)) method. Also, the important residues with the dominant interaction with each ligand are determined based on the obtained SAPT (DFT) interaction energies. To analyze the interaction of the receptor with each ligand, the decomposition of E_int to its constituent components including electrostatic (E_ele), exchange (E_ex), induction (E_ind), and exchange-induction (E_ex-ind), dispersion (E_disp), and exchange-dispersion (E_ex-disp) is performed. The change of interaction energy components with the replacement of the benzoxazole part of suvorexant by pyrimidine containing different functional groups, thieno pyrimidine, and furo pyrimidine is also investigated, separately. It is found that the change in E_int, due to these replacements, is controlled more by the variation of the electrostatic interaction energy component of E_int than by the other interaction energy components. A linear correlation (R² = 0.91) is found for the variation of E_int versus experimental ligand-binding affinities. Also, the existence of the linear correlation for the variation of the interaction energy components with experimental ligand-binding affinities is investigated. The variation of the electrostatic component versus experimental ligand-binding affinities shows a more linear correlation compared to the other interaction energy components.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

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).

A double exponential coupled cluster theory in the fragment molecular orbital framework

Fragmentation-based methods enable electronic structure calculations for large chemical systems through partitioning them into smaller fragments. Here, we have developed and benchmarked a dual exponential operator-based coupled cluster theory to account for high-rank electronic correlation of large chemical systems within the fragment molecular orbital (FMO) framework. Upon partitioning the molecular system into several fragments, the zeroth order reference determinants for each fragment and fragment pair are constructed in a self-consistent manner with two-body FMO expansion. The dynamical correlation is induced through a dual exponential ansatz with a set of fragment-specific rank-one and rank-two operators that act on the individual reference determinants. While the single and double excitations for each fragment are included through the conventional rank-one and rank-two cluster operators, the triple excitation space is spanned via the contraction between the cluster operators and a set of rank-two scattering operators over a few optimized fragment-specific occupied and virtual orbitals. Thus, the high-rank dynamical correlation effects within the FMO framework are computed with rank-one and rank-two parametrization of the wave operator, leading to significant reduction in the number of variables and associated computational scaling over the conventional methods. Through a series of pilot numerical applications on various covalent and non-covalently bonded systems, we have shown the quantitative accuracy of the proposed methodology compared to canonical, as well as FMO-based coupled-cluster single double triple. The accuracy of the proposed method is shown to be systematically improvable upon increasing the number of contractible occupied and virtual molecular orbitals employed to simulate triple excitations.

The GTP responsiveness of PI5P4Kβ evolved from a compromised trade-off between activity and specificity

Unlike most kinases, phosphatidylinositol 5-phosphate 4-kinase β (PI5P4Kβ) utilizes GTP as a physiological phosphate donor and regulates cell growth under stress (i.e., GTP-dependent stress resilience). However, the genesis and evolution of its GTP responsiveness remain unknown. Here, we reveal that PI5P4Kβ has acquired GTP preference by generating a short dual-nucleotide-recognizing motif called the guanine efficient association (GEA) motif. Comparison of nucleobase recognition with 660 kinases and 128 G proteins has uncovered that most kinases and PI5P4Kβ use their main-chain atoms for adenine recognition, while the side-chain atoms are required for guanine recognition. Mutational analysis of the GEA motif revealed that the acquisition of GTP reactivity is accompanied by an extended activity toward inosine triphosphate (ITP) and xanthosine triphosphate (XTP). Along with the evolutionary analysis data that point to strong negative selection of the GEA motif, these results suggest that the GTP responsiveness of PI5P4Kβ has evolved from a compromised trade-off between activity and specificity, underpinning the development of the GTP-dependent stress resilience.

Local electronic structure analysis by ab initio elongation method: A benchmark using DNA block polymers

The ab initio elongation (ELG) method based on a polymerization concept is a feasible way to perform linear-scaling electronic structure calculations for huge aperiodic molecules while maintaining computational accuracy. In the method, the electronic structures are sequentially elongated by repeating (1) the conversion of canonical molecular orbitals (CMOs) to region-localized MOs (RLMOs), that is, active RLMOs localized onto a region close to an attacking monomer or frozen RLMOs localized onto the remaining region, and the subsequent (2) partial self-consistent-field calculations for an interaction space composed of the active RLMOs and the attacking monomer. For each ELG process, one can obtain local CMOs for the interaction space and the corresponding local orbital energies. Local site information, such as the local highest-occupied/lowest-unoccupied MOs, can be acquired with linear-scaling efficiency by correctly including electronic effects from the frozen region. In this study, we performed a local electronic structure analysis using the ELG method for various DNA block polymers with different sequential patterns. This benchmark aimed to confirm the effectiveness of the method toward the efficient detection of a singular local electronic structure in unknown systems as a future practical application. We discussed the high-throughput efficiency of our method and proposed a strategy to detect singular electronic structures by combining with a machine learning technique.

Quantum computational study of chloride attack on chloromethane for chemical accuracy and quantum noise effects with UCCSD and k-UpCCGSD ansatzes

Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and entanglement, to perform computations. Quantum mechanical (QM) methods are candidates for various applications and can provide accurate absolute energy calculations in structure-based methods. QM methods are powerful tools for describing reaction pathways and their potential energy surfaces (PES). In this study, we applied quantum computing to describe the PES of the bimolecular nucleophilic substitution (S_N2) reaction between chloromethane and chloride ions. We performed noiseless and noise simulations using quantum algorithms and compared the accuracy and noise effects of the ansatzes. In noiseless simulations, the results from UCCSD and k-UpCCGSD are similar to those of full configurational interaction (FCI) with the same active space, which indicates that quantum algorithms can describe the PES of the S_N2 reaction. In noise simulations, UCCSD is more susceptible to quantum noise than k-UpCCGSD. Therefore, k-UpCCGSD can serve as an alternative to UCCSD to reduce quantum noisy effects in the noisy intermediate-scale quantum era, and k-UpCCGSD is sufficient to describe the PES of the S_N2 reaction in this work. The results showed the applicability of quantum computing to the S_N2 reaction pathway and provided valuable information for structure-based molecular simulations with quantum computing.

Identification of Novel Natural Product Inhibitors against Matrix Metalloproteinase 9 Using Quantum Mechanical Fragment Molecular Orbital-Based Virtual Screening Methods

Matrix metalloproteinases (MMPs) are calcium-dependent zinc-containing endopeptidases involved in multiple cellular processes. Among the MMP isoforms, MMP-9 regulates cancer invasion, rheumatoid arthritis, and osteoarthritis by degrading extracellular matrix proteins present in the tumor microenvironment and cartilage and promoting angiogenesis. Here, we identified two potent natural product inhibitors of the non-catalytic hemopexin domain of MMP-9 using a novel quantum mechanical fragment molecular orbital (FMO)-based virtual screening workflow. The workflow integrates qualitative pharmacophore modeling, quantitative binding affinity prediction, and a raw material search of natural product inhibitors with the BMDMS-NP library. In binding affinity prediction, we made a scoring function with the FMO method and applied the function to two protein targets (acetylcholinesterase and fibroblast growth factor 1 receptor) from DUD-E benchmark sets. In the two targets, the FMO method outperformed the Glide docking score and MM/PBSA methods. By applying this workflow to MMP-9, we proposed two potent natural product inhibitors (laetanine 9 and genkwanin 10) that interact with hotspot residues of the hemopexin domain of MMP-9. Laetanine 9 and genkwanin 10 bind to MMP-9 with a dissociation constant (KD) of 21.6 and 0.614 μM, respectively. Overall, we present laetanine 9 and genkwanin 10 for MMP-9 and demonstrate that the novel FMO-based workflow with a quantum mechanical approach is promising to discover potent natural product inhibitors of MMP-9, satisfying the pharmacophore model and good binding affinity.

The catalytic activity and adsorption in faujasite and ZSM-5 zeolites: the role of differential stabilization and charge delocalization

Adsorption and chemical reactions occurring on industrially important ZSM-5 and faujasite zeolite catalysts are investigated with the quantum-mechanical fragment molecular orbital method combined with periodic boundary conditions. Suitable fragmentation patterns are devised and tested providing important case studies of computing real materials with fragmentation methods. A good accuracy is demonstrated in comparison to full calculations, and a good agreement with the available experimental data is obtained. The full production cycle of p-xylene on faujasite zeolite is mapped. The catalytic role of the zeolite in the dehydration reaction, analyzed with the partition analysis, is attributed to the delocalization of the negative charge over the zeolite. On the other hand, an increase of the barrier in the Diels-Alder reaction by the zeolite is attributed to the preferential stabilization of the reactants over the transition state as demonstrated by the guest-zeolite interaction energy.

Quantum algorithms for electronic structures: basis sets and boundary conditions

The advantages of quantum computers are believed to significantly change the research paradigm of chemical and materials sciences, where computational characterization and theoretical design play an increasingly important role. It is especially desirable to solve the electronic structure problem, a central problem in chemistry and materials science, efficiently and accurately with well-designed quantum algorithms. Various quantum electronic-structure algorithms have been proposed in the literature. In this article, we briefly review recent progress in this direction with a special emphasis on the basis sets and boundary conditions. Compared to classical electronic structure calculations, there are new considerations in choosing a basis set in quantum algorithms. For example, the effect of the basis set on the circuit complexity is very important in quantum algorithm design. Electronic structure calculations should be performed with an appropriate boundary condition. Simply using a wave function ansatz designed for molecular systems in a material system with a periodic boundary condition may lead to significant errors. Artificial boundary conditions can be used to partition a large system into smaller fragments to save quantum resources. The basis sets and boundary conditions are expected to play a crucial role in electronic structure calculations on future quantum computers, especially for realistic systems.

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.

Inclusion of More Physics Leads to Less Data: Learning the Interaction Energy as a Function of Electron Deformation Density with Limited Training Data

Machine learning (ML) approaches to predicting quantum mechanical (QM) properties have made great strides toward achieving the computational chemist's holy grail of structure-based property prediction. In contrast to direct ML methods, which encode a molecule with only structural information, in this work, we show that QM descriptors improve ML predictions of dimer interaction energy, both in terms of accuracy and data efficiency, by incorporating electronic information into the descriptor. We present the electron deformation density interaction energy machine learning (EDDIE-ML) model, which predicts the interaction energy as a function of Hartree-Fock electron deformation density. We compare its performance with leading direct ML schemes and modern DFT methods for the prediction of interaction energies for dimers of varying charge type, size, and intermolecular separation. Under a low-data regime, EDDIE-ML outperforms other direct ML schemes and is the only model readily transferrable to larger, more complex systems including base pair trimers and porous cages. The underlying physical connection between the density and interaction energy enables EDDIE-ML to reach an accuracy comparable to modern DFT functionals in fewer training data points compared to other ML methods.

Evaluation of protein descriptors in computer-aided rational protein engineering tasks and its application in property prediction in SARS-CoV-2 spike glycoprotein

The importance of protein engineering in the research and development of biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided protein engineering can markedly reduce the experimental effort in identifying optimal sequences that satisfy the desired properties from a large number of possible protein sequences. To develop general protein descriptors for computer-aided protein engineering tasks, we devised new protein descriptors, one sequence-based descriptor (PCgrades), and three structure-based descriptors (PCspairs, 3D-SPIEs_5.4 Å, and 3D-SPIEs_8Å). While the PCgrades and PCspairs include general and statistical information in physicochemical properties in single and pairwise amino acids respectively, the 3D-SPIEs include specific and quantum–mechanical information with parameterized quantum mechanical calculations (FMO2-DFTB3/D/PCM). To evaluate the protein descriptors, we made prediction models with the new descriptors and previously developed descriptors for diverse protein datasets including protein expression and binding affinity change in SARS-CoV-2 spike glycoprotein. As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance (R2=0.783 for protein expression and R2=0.711 for binding affinity). As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance. Similar approaches with those descriptors would be promising and useful if the prediction models are trained with sufficient quantitative experimental data from high-throughput assays for industrial enzymes or protein drugs.

Specific interactions between the alkaline protease of P. aeruginosa and its natural peptide inhibitor: ab initio molecular simulations

Alkaline protease aeruginolysin (APR) is an important virulence factor in the evasion of the immune system by Pseudomonas aeruginosa (P. aeruginosa). The P. aeruginosa genome also encodes the highly potent and specific APR peptide inhibitor (APRin). However, the structural reason for the significant inhibition has not been revealed. Using ab initio molecular simulations, we here investigated the specific interactions between APR and APRin to elucidate which amino acid residues of APRin and APR contribute strongest to the inhibition. Since APR has a Zn²⁺ ion at the ligand-binding site and histidine and glutamic acid residues are coordinated with Zn²⁺, it is essential to precisely describe these coordination bonds to elucidate the specific interactions between APR and APRin. Therefore, we employed the ab initio fragment molecular orbital method to investigate the specific interactions at an electronic level. The results revealed that Ser1 and Ser2 at the N-terminus of APRin significantly contribute to the binding between APRin and APR. In particular, Ser1 binds strongly to Zn²⁺ as well as to the sidechains of His176(Hid), His180(Hid), and His186(Hid) in APR. This is the main reason for the strong interaction between APR and APRin. The results also elucidated significant contributions of the positively charged Arg83 and Arg90 residues of APRin to the binding with APR. These findings may provide information useful for the design of novel small agents as potent APR inhibitors.

Third-Order Many-Body Expansion of OSV-MP2 Wave Function for Low-Order Scaling Analytical Gradient Computation

We present a many-body expansion (MBE) formulation and implementation for efficient computation of analytical energy gradients from the orbital-specific-virtual second-order Møllet-Plesset perturbation theory (OSV-MP2) based on our earlier work (Zhou et al. J. Chem. Theory Comput. 2020, 16, 196-210). The third-order MBE(3) expansion of OSV-MP2 amplitudes and density matrices was developed to adopt the orbital-specific clustering and long-range termination schemes, which avoids term-by-term differentiations of the MBE energy bodies. We achieve better efficiency by exploiting the algorithmic sparsity that allows us to prune out insignificant fitting integrals and OSV relaxations. With these approximations, the present implementation is benchmarked on a range of molecules that show an economic scaling in the linear and quadratic regimes for computing MBE(3)-OSV-MP2 amplitude and gradient equations, respectively, and yields normal accuracy comparable to the original OSV-MP2 results. The MPI-3-based parallelism through shared memory one-sided communication is further developed for improving parallel scalability and memory accessibility by sorting the MBE(3) orbital clusters into independent tasks that are distributed on multiple processes across many nodes, supporting both global and local data locations in which selected MBE(3)-OSV-MP2 intermediates of different sizes are distinguished and accordingly placed. The accuracy and efficiency level of our MBE(3)-OSV-MP2 analytical gradient implementation is finally illustrated in two applications: we show that the subtle coordination structure differences of mechanically interlocked Cu-catenane complexes can be distinguished when tuning ligand lengths; and the porphycene molecular dynamics reveals the emergence of the vibrational signature arising from softened N-H stretching associated with hydrogen transfer, using an MP2 level of electron correlation and classical nuclei for the first time.

Proposal of selective inhibitor for bacterial zinc metalloprotease: Molecular mechanics and ab initio molecular orbital calculations

The zinc metalloprotease pseudolysin (PLN) secreted from Pseudomonas aeruginosa degrades extracellular proteins to produce bacterial nutrition, and various types of PLN inhibitors have been developed to suppress the bacterial growth. However, as the structure of the ligand-binding pocket of PLN has large similarities to those of human matrix metalloproteinases (MMPs) and other human zinc metalloprotease, there is a risk that PLN inhibitors also inhibit human zinc proteases. In this study, we propose a novel agent that may bind stronger to PLN than to MMPs. The compound is proposed based on the specific molecular interactions between existing agents and PLN/MMP metalloproteases evaluated by the present molecular simulations. First, we confirmed that the binding energies of PLN agents evaluated using the ab initio fragment molecular orbital method were comparable to the IC₅₀ values obtained through previous experiments. In addition, the specific molecular interactions between these agents and MMP-9 were investigated to elucidate the fact that some of the agents bind weaker to MMP than PLN. Based on the results, we proposed a novel agent having a succinimide group introduce by a hydroxamic acid group and investigated its binding properties with PLN and MMP. The results may provide useful information for the development of potent inhibitors for PLN with few potential side effects in human bodies.

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.

Ab initio multi-level layered elongation method and its application to local interaction analysis between DNA bulge and ligand molecules

A multi-level layered elongation method was developed for efficiently analyzing the electronic states of local structures in large bio/nano-systems at the full ab initio level of theory. The original elongation method developed during the last three decades in our group has focused on the system in one direction from one terminal to the other terminal to sequentially construct the electronic states of a polymer, called a theoretical synthesis of polymers. In this study, an important region termed the central (C) part is targeted in a large polymer and the remainder are terminal (T) parts. The electronic structures along with polymer elongation are calculated repeatedly from both end T parts to the C central part at the same time. The important C part is treated with large basis sets (high level) and the other regions are treated with small basis sets (low level) in the ab initio theoretical framework. The electronic structures besides the C part can be reused for other systems with different structures at the C part, which renders the method computationally efficient. This multi-level layered elongation method was applied to the investigation on DNA single bulge recognition of small molecules (ligands). The reliability and validity of our approach were examined in comparison with the results obtained by direct calculations using a conventional quantum chemical method for the entire system. Furthermore, stabilization energies by the formation of the complex of bulge DNA and a ligand were estimated with basis set superposition error corrections incorporated into the elongation method.

Hot Spot Analysis of YAP-TEAD Protein-Protein Interaction Using the Fragment Molecular Orbital Method and Its Application for Inhibitor Discovery

The Hippo pathway is an important signaling pathway modulating growth control and cancer cell proliferation. Dysregulation of the Hippo pathway is a common feature of several types of cancer cells. The modulation of the interaction between yes-associated protein (YAP) and transcriptional enhancer associated domain (TEAD) in the Hippo pathway is considered an attractive target for cancer therapeutic development, although the inhibition of PPI is a challenging task. In order to investigate the hot spots of the YAP and TEAD1 interacting complex, an ab initio Fragment Molecular Orbital (FMO) method was introduced. With the hot spots, pharmacophores for the inhibitor design were constructed, then virtual screening was performed to an in-house library. Next, we performed molecular docking simulations and FMO calculations for screening results to study the binding modes and affinities between PPI inhibitors and TEAD1. As a result of the virtual screening, three compounds were selected as virtual hit compounds. In order to confirm their biological activities, cellular (luciferase activity, proximity ligation assay and wound healing assay in A375 cells, qRT-PCR in HEK 293T cells) and biophysical assays (surface plasmon resonance assays) were performed. Based on the findings of the study, we propose a novel PPI inhibitor BY03 and demonstrate a profitable strategy to analyze YAP-TEAD PPI and discover novel PPI inhibitors.

pyProGA-A PyMOL plugin for protein residue network analysis

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.

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.

Trends in application of advancing computational approaches in GPCR ligand discovery

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs are integral membrane proteins that play key roles in various cellular signaling processes. Therefore, GPCR signaling pathways are closely associated with numerous diseases, including cancer and several neurological, immunological, and hematological disorders. Computer-aided drug design (CADD) can expedite the process of GPCR drug discovery and potentially reduce the actual cost of research and development. Increasing knowledge of biological structures, as well as improvements on computer power and algorithms, have led to unprecedented use of CADD for the discovery of novel GPCR modulators. Similarly, machine learning approaches are now widely applied in various fields of drug target research. This review briefly summarizes the application of rising CADD methodologies, as well as novel machine learning techniques, in GPCR structural studies and bioligand discovery in the past few years. Recent novel computational strategies and feasible workflows are updated, and representative cases addressing challenging issues on olfactory receptors, biased agonism, and drug-induced cardiotoxic effects are highlighted to provide insights into future GPCR drug discovery.

Intermolecular Interaction Analyses on SARS-CoV-2 Spike Protein Receptor Binding Domain and Human Angiotensin-Converting Enzyme 2 Receptor-Blocking Antibody/Peptide Using Fragment Molecular Orbital Calculation

The spike glycoprotein (S-protein) mediates SARS-CoV-2 entry via intermolecular interaction with human angiotensin-converting enzyme 2. The receptor binding domain (RBD) of the S-protein has been considered critical for this interaction and acts as the target of numerous neutralizing antibodies and antiviral peptides. This study used the fragment molecular orbital method to analyze the interactions between the RBD and antibodies/peptides and extracted crucial residues that can be used as epitopes. The interactions evaluated as interfragment interaction energy values between the RBD and 12 antibodies/peptides showed a fairly good correlation with the experimental activity pIC50 (R2 = 0.540). Nine residues (T415, K417, Y421, F456, A475, F486, N487, N501, and Y505) were confirmed as being crucial. Pair interaction energy decomposition analyses showed that hydrogen bonds, electrostatic interactions, and π-orbital interactions are important. Our results provide essential information for understanding SARS-CoV-2-antibody/peptide binding and may play roles in future antibody/antiviral drug design.