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Highlights d Crystallographic structure of the human eIF4A1,AMPPNP,RocA,polypurine RNA complex d Direct base recognition by RocA induces polypurine RNA selectivity on eIF4A1 d Natural amino acid substitutions found in Aglaia eIF4As provide self-resistance to RocA

Recent developments in the fragment molecular orbital (FMO) method for theoretical formulation, implementation, and application to nano and biomolecular systems are reviewed. The FMO method has enabled ab initio quantum-mechanical calculations for large molecular systems such as protein-ligand complexes at a reasonable computational cost in a parallelized way. There have been a wealth of application outcomes from the FMO method in the fields of biochemistry, medicinal chemistry and nanotechnology, in which the electron correlation effects play vital roles. With the aid of the advances in high-performance computing, the FMO method promises larger, faster, and more accurate simulations of biomolecular and related systems, including the descriptions of dynamical behaviors in solvent environments. The current status and future prospects of the FMO scheme are addressed in these contexts.

We have theoretically examined the relative binding affinities (RBA) of typical ligands, 17beta-estradiol (EST), 17alpha-estradiol (ESTA), genistein (GEN), raloxifene (RAL), 4-hydroxytamoxifen (OHT), tamoxifen (TAM), clomifene (CLO), 4-hydroxyclomifene (OHC), diethylstilbestrol (DES), bisphenol A (BISA), and bisphenol F (BISF), to the alpha-subtype of the human estrogen receptor ligand-binding domain (hERalpha LBD), by calculating their binding energies. The ab initio fragment molecular orbital (FMO) method, which we have recently proposed for the calculations of macromolecules such as proteins, was applied at the HF/STO-3G level. The receptor protein was primarily modeled by 50 amino acid residues surrounding the ligand. The number of atoms in these model complexes is about 850, including hydrogen atoms. For the complexes with EST, RAL, OHT, and DES, the binding energies were calculated again with the entire ERalphaLBD consisting of 241 residues or about 4000 atoms. No significant difference was found in the calculated binding energies between the model and the real protein complexes. This indicates that the binding between the protein and its ligands is well characterized by the model protein with the 50 residues. The calculated binding energies relative to EST were very well correlated with the experimental RBA (the correlation coefficient r=0.837) for the ligands studied in this work. We also found that the charge transfer between ER and ligands is significant on ER-ligand binding. To our knowledge, this is the first achievement of ab initio quantum mechanical calculations of large molecules such as the entire ERalphaLBD protein.

We have developed a visualized cluster analysis of protein-ligand interaction (VISCANA) that analyzes the pattern of the interaction of the receptor and ligand on the basis of quantum theory for virtual ligand screening. Kitaura et al. (Chem. Phys. Lett. 1999, 312, 319-324.) have proposed an ab initio fragment molecular orbital (FMO) method by which large molecules such as proteins can be easily treated with chemical accuracy. In the FMO method, a total energy of the molecule is evaluated by summation of fragment energies and interfragment interaction energies (IFIEs). In this paper, we have proposed a cluster analysis using the dissimilarity that is defined as the squared Euclidean distance between IFIEs of two ligands. Although the result of an ordered table by clustering is still a massive collection of numbers, we combine a clustering method with a graphical representation of the IFIEs by representing each data point with colors that quantitatively and qualitatively reflect the IFIEs. We applied VISCANA to a docking study of pharmacophores of the human estrogen receptor alpha ligand-binding domain (57 amino acid residues). By using VISCANA, we could classify even structurally different ligands into functionally similar clusters according to the interaction pattern of a ligand and amino acid residues of the receptor protein. In addition, VISCANA could estimate the correct docking conformation by analyzing patterns of the receptor-ligand interactions of some conformations through the docking calculation.

RNA-based therapeutics is a promising approach for curing intractable diseases by manipulating various cellular functions. For eliciting RNA (i.e., mRNA and siRNA) functions successfully, the RNA in the extracellular space must be protected and it must be delivered to the cytoplasm. In this study, the development of a self-degradable lipid-like material that functions to accelerate the collapse of lipid nanoparticles (LNPs) and the release of RNA into cytoplasm is reported. The self-degradability is based on a unique reaction "Hydrolysis accelerated by intra-Particle Enrichment of Reactant (HyPER)." In this reaction, a disulfide bond and a phenyl ester are essential structural components: concentrated hydrophobic thiols that are produced by the cleavage of the disulfide bonds in the LNPs drive an intraparticle nucleophilic attack to the phenyl ester linker, which results in further degradation. An oleic acid-scaffold lipid-like material that mounts all of these units (ssPalmO-Phe) shows superior transfection efficiency to nondegradable or conventional materials. The insertion of the aromatic ring is unexpectedly revealed to contribute to the enhancement of endosomal escape. Since the intracellular trafficking is a sequential process that includes cellular uptake, endosomal escape, the release of mRNA, and translation, the improvement in each process synergistically enhances the gene expression.

The worldwide spread of COVID-19 (new coronavirus found in 2019) is an emergent issue to be tackled. In fact, a great amount of works in various fields have been made in a rather short period. Here, we report a fragment molecular orbital (FMO) based interaction analysis on a complex between the SARS-CoV-2 main protease (Mpro) and its peptide-like inhibitor N3 (PDB ID: 6LU7). The target inhibitor molecule was segmented into five fragments in order to capture site specific interactions with amino acid residues of the protease. The interaction energies were decomposed into several contributions, and then the characteristics of hydrogen bonding and dispersion stabilization were made clear. Furthermore, the hydration effect was incorporated by the Poisson–Boltzmann (PB) scheme. From the present FMO study, His41, His163, His164, and Glu166 were found to be the most important amino acid residues of Mpro in interacting with the inhibitor, mainly due to hydrogen bonding. A guideline for optimizations of the inhibitor molecule was suggested as well based on the FMO analysis.

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