Quantum Brain Dynamics and Virtual Reality.

In this paper we propose a control theory of manipulating holograms in Quantum Brain Dynamics (QBD) involving our subjective experiences, i.e. qualia. We begin with the Lagrangian density in QBD and extend our theory to a hierarchical model involving multiple layers covering the neocortex. We adopt reservoir computing approach or morphological computation to manipulate waveforms of holograms involving our subjective experiences. Numerical simulations performed indicate that the convergence to target waveforms of holograms is realized by external electric fields in QBD in a hierarchy. Our theory can be applied to non-invasive neuronal stimulation of the neocortex and adopted to check whether or not our brain adopts the language of holography. In case the protocol in a brain is discovered and the brain adopts the language of holography, our control theory will be applied to develop virtual reality devices by which our subjective experiences provided by the five senses in the form of qualia are manipulated non-invasively. Then, the information content of qualia might be directly transmitted into our brain without passing through sensory organs.

In Silico Search for Drug Candidates Targeting the PAX8-PPARγ Fusion Protein in Thyroid Cancer.

The PAX8/PPARγ rearrangement, producing the PAX8-PPARγ fusion protein (PPFP), is thought to play an essential role in the oncogenesis of thyroid follicular tumors. To identify PPFP-targeted drug candidates and establish an early standard of care for thyroid tumors, we performed ensemble-docking-based compound screening. Specifically, we investigated the pocket structure that should be adopted to search for a promising ligand compound for the PPFP; the position of the ligand-binding pocket on the PPARγ side of the PPFP is similar to that of PPARγ; however, the shape is slightly different between them due to environmental factors. We developed a method for selecting a PPFP structure with a relevant pocket and high prediction accuracy for ligand binding. This method was validated using PPARγ, whose structure and activity values are known for many compounds. Then, we performed docking calculations to the PPFP for 97 drug or drug-like compounds registered in the DrugBank database with a thiazolidine backbone, which is one of the characteristics of ligands that bind well to PPARγ. Furthermore, the binding affinities of promising ligand candidates were estimated more reliably using the molecular mechanics Poisson-Boltzmann surface area method. Thus, we propose promising drug candidates for the PPFP with a thiazolidine backbone.

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.

Holographic Brain Theory: Super-Radiance, Memory Capacity and Control Theory.

We investigate Quantum Electrodynamics corresponding to the holographic brain theory introduced by Pribram to describe memory in the human brain. First, we derive a super-radiance solution in Quantum Electrodynamics with non-relativistic charged bosons (a model of molecular conformational states of water) for coherent light sources of holograms. Next, we estimate memory capacity of a brain neocortex, and adopt binary holograms to manipulate optical information. Finally, we introduce a control theory to manipulate holograms involving biological water's molecular conformational states. We show how a desired waveform in holography is achieved in a hierarchical model using numerical simulations.

Manipulation of photosynthetic energy transfer by vibrational strong coupling.

Uncovering the mystery of efficient and directional energy transfer in photosynthetic organisms remains a critical challenge in quantum biology. Recent experimental evidence and quantum theory developments indicate the significance of quantum features of molecular vibrations in assisting photosynthetic energy transfer, which provides the possibility of manipulating the process by controlling molecular vibrations. Here, we propose and theoretically demonstrate efficient manipulation of photosynthetic energy transfer by using vibrational strong coupling between the vibrational state of a Fenna-Matthews-Olson (FMO) complex and the vacuum state of an optical cavity. Specifically, based on a full-quantum analytical model to describe the strong coupling effect between the optical cavity and molecular vibration, we realize efficient manipulation of energy transfer efficiency (from 58% to 92%) and energy transfer time (from 20 to 500 ps) in one branch of FMO complex by actively controlling the coupling strength and the quality factor of the optical cavity under both near-resonant and off-resonant conditions, respectively. Our work provides a practical scenario to manipulate photosynthetic energy transfer by externally interfering molecular vibrations via an optical cavity and a comprehensible conceptual framework for researching other similar systems.

Deciphering the Potential of Multidimensional Carbon Materials for Surface-Enhanced Raman Spectroscopy through Density Functional Theory.

Surface-enhanced Raman spectroscopy (SERS) is a potent analytical tool, particularly for molecular identification and structural analysis. Conventional metallic SERS substrates, however, suffer from low reproducibility and compatibility with biological molecules. Recently, metal-free SERS substrates based on chemical enhancement have emerged as a promising alternative with carbon-based materials offering excellent reproducibility and compatibility. Nevertheless, our understanding of carbon materials in SERS remains limited, which hinders their rational design. Here we systematically explore multidimensional carbon materials, including zero-dimensional fullerenes (C60), one-dimensional carbon nanotubes, two-dimensional graphene, and their B-, N-, and O-doped derivatives, for SERS applications. Using density functional theory, we elucidate the nonresonant polarizability-enhanced and resonant charge-transfer-based chemical enhancement mechanisms of these materials by evaluating their static/dynamic polarizability and electron excitation properties. This work provides a critical reference for the future design of carbon-based SERS substrates, opening a new avenue in this field.

Anesthetic Binding Induced Motion of GABAA Receptors Revealed by Coarse-Grained Molecular Dynamics Simulations.

General anesthetics are indispensable in modern medicine because they induce a reversible loss of consciousness and sensation in humans. On the other hand, their molecular mechanisms of action have not yet been elucidated. Several studies have identified the main targets of some general anesthetics. The structures of γ-aminobutyric acid A (GABAA) receptors with the intravenous anesthetics such as propofol and etomidate have recently been determined. Although these anesthetic binding structures provide essential insights into the mechanism of action of anesthetics, the detailed molecular mechanism of how the anesthetic binding affects the Cl- permeability of GABAA receptors remains to be elucidated. In this study, we performed coarse-grained molecular dynamics simulations for GABAA receptors and analyzed the resulting simulation trajectories to investigate the effects of anesthetic binding on the motion of GABAA receptors. The results showed large structural fluctuations in GABAA receptors, correlations of motion between the amino acid residues, large amplitude motion, and autocorrelated slow motion, which were obtained by advanced statistical analyses. In addition, a comparison of the resulting trajectories in the presence or absence of the anesthetic molecules revealed a characteristic pore motion related to the gate-opening motion of GABAA receptors.

End-to-end protein-ligand complex structure generation with diffusion-based generative models.

BACKGROUND: Three-dimensional structures of protein-ligand complexes provide valuable insights into their interactions and are crucial for molecular biological studies and drug design. However, their high-dimensional and multimodal nature hinders end-to-end modeling, and earlier approaches depend inherently on existing protein structures. To overcome these limitations and expand the range of complexes that can be accurately modeled, it is necessary to develop efficient end-to-end methods. RESULTS: We introduce an equivariant diffusion-based generative model that learns the joint distribution of ligand and protein conformations conditioned on the molecular graph of a ligand and the sequence representation of a protein extracted from a pre-trained protein language model. Benchmark results show that this protein structure-free model is capable of generating diverse structures of protein-ligand complexes, including those with correct binding poses. Further analyses indicate that the proposed end-to-end approach is particularly effective when the ligand-bound protein structure is not available. CONCLUSION: The present results demonstrate the effectiveness and generative capability of our end-to-end complex structure modeling framework with diffusion-based generative models. We suppose that this framework will lead to better modeling of protein-ligand complexes, and we expect further improvements and wide applications.

Quantum Chemical Interaction Analysis between SARS-CoV-2 Main Protease and Ensitrelvir Compared with Its Initial Screening Hit.

A non-covalent oral drug targeting SARS-CoV-2 main protease (Mpro), ensitrelvir (Xocova), has been developed using structure-based drug design (SBDD). To elucidate the factors responsible for enhanced inhibitory activities from an in silico screening hit compound to ensitrelvir, we analyzed the interaction energies of the inhibitors with each residue of Mpro using fragment molecular orbital (FMO) calculations. This analysis reveals that functional group conversion for P1' and P1 parts in the inhibitors increases the strength of existing interactions with Mpro and also provides novel interactions for ensitrelvir; the associated changes in the conformation of Mpro induce further interactions for ensitrelvir in other parts, including hydrogen bonds, a halogen bond, and π-orbital interactions. Thus, we illuminate the promising strategies of SBDD for leading ensitrelvir to get higher activity against Mpro by elucidating microscopic interactions through FMO-based analysis. These detailed mechanism findings, including water cross-linkings, will help to design novel inhibitors in SBDD.

Thermal conductivity and conductance of protein in aqueous solution: Effects of geometrical shape.

Considering the importance of elucidating the heat transfer in living cells, we evaluated the thermal conductivity κ and conductance G of hydrated protein through all-atom non-equilibrium molecular dynamics simulation. Extending the computational scheme developed in earlier studies for spherical protein to cylindrical one under the periodic boundary condition, we enabled the theoretical analysis of anisotropic thermal conduction and also discussed the effects of protein size correction on the calculated results. While the present results for myoglobin and green fluorescent protein (GFP) by the spherical model were in fair agreement with previous computational and experimental results, we found that the evaluations for κ and G by the cylindrical model, in particular, those for the longitudinal direction of GFP, were enhanced substantially, but still keeping a consistency with experimental data. We also studied the influence by salt addition of physiological concentration, finding insignificant alteration of thermal conduction of protein in the present case.

Protein-Protein Interaction Modelling with the Fragment Molecular Orbital Method.

Fragment molecular orbital (FMO) method enables ab initio quantum-chemical calculations for biomolecular systems with high accuracy and moderate computational cost. Through this analysis we can evaluate the inter-fragment interaction energies (IFIEs) that provide useful measures for effective interactions between the fragments representing amino-acid residues and ligand molecules. Here I describe how to prepare the input structures and perform the FMO calculations for protein-protein complex system. In addition to the pre-processing, some useful tools for the post-processing analysis are also illustrated.

Interspecies Comparison of Interaction Energies between Photosynthetic Protein RuBisCO and 2CABP Ligand.

Ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO) functions as the initial enzyme in the dark reactions of photosynthesis, catalyzing reactions that extract CO2 from the atmosphere and fix CO2 into organic compounds. RuBisCO is classified into four types (isoforms I-IV) according to sequence-based phylogenetic trees. Given its size, the computational cost of accurate quantum-chemical calculations for functional analysis of RuBisCO is high; however, recent advances in hardware performance and the use of the fragment molecular orbital (FMO) method have enabled the ab initio analyses of RuBisCO. Here, we performed FMO calculations on multiple structural datasets for various complexes with the 2'-carboxylarabinitol 1,5-bisphosphate (2CABP) ligand as a substrate analog and investigated whether phylogenetic relationships based on sequence information are physicochemically relevant as well as whether novel information unobtainable from sequence information can be revealed. We extracted features similar to the phylogenetic relationships found in sequence analysis, and in terms of singular value decomposition, we identified residues that strongly interacted with the ligand and the characteristics of the isoforms for each principal component. These results identified a strong correlation between phylogenetic relationships obtained by sequence analysis and residue interaction energies with the ligand. Notably, some important residues were located far from the ligand, making comparisons among species using only residues proximal to the ligand insufficient.

Inhibition of Melittin Activity Using a Small Molecule with an Indole Ring.

We investigated d-amino acids as potential inhibitors targeting l-peptide toxins. Among the l- and d-amino acids tested, we found that d-tryptophan (d-Trp) acted as an inhibitor of melittin-induced hemolysis. We then evaluated various Trp derivatives and found that 5-chlorotryptamine (5CT) had the largest inhibitory effect on melittin. The indole ring, amino group, and steric hindrance of an inhibitor played important roles in the inhibition of melittin activity. Despite the small size and simple molecular structure of 5CT, its IC50 was approximately 13 µg/mL. Fluorescence quenching, circular dichroism measurements, and size-exclusion chromatography revealed that 5CT interacted with Trp19 in melittin and affected the formation of the melittin tetramer involved in hemolysis. Molecular dynamics simulation of melittin also indicated that the interaction of 5CT with Trp19 in melittin affected the formation of the tetramer.

Protein-ligand binding affinity prediction of cyclin-dependent kinase-2 inhibitors by dynamically averaged fragment molecular orbital-based interaction energy.

Fragment molecular orbital (FMO) method is a powerful computational tool for structure-based drug design, in which protein-ligand interactions can be described by the inter-fragment interaction energy (IFIE) and its pair interaction energy decomposition analysis (PIEDA). Here, we introduced a dynamically averaged (DA) FMO-based approach in which molecular dynamics simulations were used to generate multiple protein-ligand complex structures for FMO calculations. To assess this approach, we examined the correlation between the experimental binding free energies and DA-IFIEs of six CDK2 inhibitors whose net charges are zero. The correlation between the experimental binding free energies and snapshot IFIEs for X-ray crystal structures was R2  = 0.75. Using the DA-IFIEs, the correlation significantly improved to 0.99. When an additional CDK2 inhibitor with net charge of -1 was added, the DA FMO-based scheme with the dispersion energies still achieved R2  = 0.99, whereas R2 decreased to 0.32 employing all the energy terms of PIEDA.

Computational prediction of heteromeric protein complex disassembly order using hybrid Monte Carlo/molecular dynamics simulation.

The physicochemical entities comprising the biological phenomena in the cell form a network of biochemical reactions and the activity of such a network is regulated by multimeric protein complexes. Mass spectroscopy (MS) experiments and multimeric protein docking simulations based on structural bioinformatics techniques have revealed the molecular-level stoichiometry and static configuration of subcomplexes in their bound forms, thus revealing the subcomplex population and formation orders. Meanwhile, these methodologies are not designed to straightforwardly examine the temporal dynamics of multimeric protein assembly and disassembly, essential physicochemical properties to understand the functional expression mechanisms of proteins in the biological environment. To address this problem, we have developed an atomistic simulation in the framework of the hybrid Monte Carlo/molecular dynamics (hMC/MD) method and succeeded in observing the disassembly of a homomeric pentamer of the serum amyloid P component protein in an experimentally consistent order. In this study, we improved the hMC/MD method to examine the disassembly processes of the tryptophan synthase tetramer, a paradigmatic heteromeric protein complex in MS studies. We employed the likelihood-based selection scheme to determine a dissociation-prone subunit pair at every hMC/MD simulation cycle and achieved highly reliable predictions of the disassembly orders without a priori knowledge of the MS experiments and structural bioinformatics simulations. The success rate for the experimentally-observed disassembly order is over 0.9. We similarly succeeded in reliable predictions for three other tetrameric protein complexes. These achievements indicate the potential applicability of our hMC/MD approach as a general-purpose methodology to obtain microscopic and physicochemical insights into multimeric protein complex formation.

Remarked suppression of Aß42 protomer-protomer dissociation reaction elucidated by molecular dynamics simulation.

Multimeric protein complexes are molecular apparatuses to regulate biological systems and often determine their fate. Among proteins forming such molecular assemblies, amyloid proteins have drawn attention over a half-century since amyloid fibril formation of these proteins is supposed to be a common pathogenic cause for neurodegenerative diseases. This process is triggered by the accumulation of fibril-like aggregates, while the microscopic mechanisms are mostly elusive due to technical limitation of experimental methodologies in individually observing each of diverse aggregate species in the aqueous solution. We then addressed this problem by employing atomistic molecular dynamics simulations for the paradigmatic amyloid protein, amyloid-ß (Aß42 ). Seven different dimeric forms of oligomeric Aß42 fibril-like aggregate in aqueous solution, ranging from tetramer to decamer, were considered. We found additive effects of the size of these fibril-like aggregates on their thermodynamic stability and have clarified kinetic suppression of protomer-protomer dissociation reactions at and beyond the point of pentamer dimer formation. This observation was obtained from the specific combination of the Aß42 protomer structure and the physicochemical condition that we here examined, while it is worthwhile to recall that several amyloid fibrils take dimeric forms of their protomers. We could thus conclude that the stable formation of fibril-like protomer dimer should be involved in a turning point where rapid growth of amyloid fibrils is triggered.

Fragment molecular orbital calculations for biomolecules.

Exploring biomolecule behavior, such as proteins and nucleic acids, using quantum mechanical theory can identify many life science phenomena from first principles. Fragment molecular orbital (FMO) calculations of whole single particles of biomolecules can determine the electronic state of the interior and surface of molecules and explore molecular recognition mechanisms based on intermolecular and intramolecular interactions. In this review, we summarized the current state of FMO calculations in drug discovery, virology, and structural biology, as well as recent developments from data science.

Improvement of the Force Field for ß-d-Glucose with Machine Learning.

While the construction of a dependable force field for performing classical molecular dynamics (MD) simulation is crucial for elucidating the structure and function of biomolecular systems, the attempts to do this for glycans are relatively sparse compared to those for proteins and nucleic acids. Currently, the use of GLYCAM06 force field is the most popular, but there have been a number of concerns about its accuracy in the systematic description of structural changes. In the present work, we focus on the improvement of the GLYCAM06 force field for ß-d-glucose, a simple and the most abundant monosaccharide molecule, with the aid of machine learning techniques implemented with the TensorFlow library. Following the pre-sampling over a wide range of configuration space generated by MD simulation, the atomic charge and dihedral angle parameters in the GLYCAM06 force field were re-optimized to accurately reproduce the relative energies of ß-d-glucose obtained by the density functional theory (DFT) calculations according to the structural changes. The validation for the newly proposed force-field parameters was then carried out by verifying that the relative energy errors compared to the DFT value were significantly reduced and that some inconsistencies with experimental (e.g., NMR) results observed in the GLYCAM06 force field were resolved relevantly.

Elucidating microscopic events driven by GTP hydrolysis reaction in the Ras-GAP system with semi-reactive molecular dynamics simulations: the alternative role of a phosphate binding loop for mechanical energy storage.

ATPase and GTPase have been widely found as chemical energy-mechanical work transducers, whereas the physicochemical mechanisms are not satisfactorily understood. We addressed the problem by examining John Ross' conjecture that repulsive Coulomb interaction between ADP/GDP and inorganic phosphate (Pi) does the mechanical work upon the system. We effectively simulated the consequence of a GTP hydrolysis reaction in a complex system of Rat sarcoma (Ras) and GTPase activation protein (GAP) in the framework of classical molecular dynamics by switching force field parameters between the reactant and product systems. We then observed a ca. 5 kcal mol-1 increase of potential energy about the phosphate-binding loop (P-loop) in the Ras protein, indicating that the mechanical work generated via the GTP hydrolysis is converted into the local interaction energy and stored in the P-loop. Interestingly, this local energy storage in the P-loop depends on neither impulsive nor consecutive collisions of GDP and Pi with the P-loop. Instead, GTP-GDP conversion itself does work on the Ras system, elevating the potential energy. These observations encourage us to challenge a conjecture previously given by Ross. We assert that triphosphate nucleotide hydrolyses do mechanical work by producing emergent steric interaction accompanied by relaxation, namely, a shift of the biomolecular system to the non-equilibrium state on the reshaped potential energy landscape. Recalling the universality of the P-loop motif among GTPases and ATPases, the observations that we obtained through this study would progress the physicochemical understanding of the operating principles of GTP/ATP hydrolysis-driven biological nano-machines.

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 (Mpro) 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.