New challenges for photopharmacology and computational modeling.

Mous et al. recently reported the molecular mechanism of chloride transport through a light-activated pumping rhodopsin, a key process involved in a range of cellular functions. Their results open exciting new challenges for photopharmacology and computational modeling that should be addressed in the coming years.

Approaching the catalytic mechanism of protein lysine methyltransferases by biochemical and simulation techniques.

Protein lysine methyltransferases (PKMTs) transfer up to three methyl groups to the side chains of lysine residues in proteins and fulfill important regulatory functions by controlling protein stability, localization and protein/protein interactions. The methylation reactions are highly regulated, and aberrant methylation of proteins is associated with several types of diseases including neurologic disorders, cardiovascular diseases, and various types of cancer. This review describes novel insights into the catalytic machinery of various PKMTs achieved by the combined application of biochemical experiments and simulation approaches during the last years, focusing on clinically relevant and well-studied enzymes of this group like DOT1L, SMYD1-3, SET7/9, G9a/GLP, SETD2, SUV420H2, NSD1/2, different MLLs and EZH2. Biochemical experiments have unraveled many mechanistic features of PKMTs concerning their substrate and product specificity, processivity and the effects of somatic mutations observed in PKMTs in cancer cells. Structural data additionally provided information about the substrate recognition, enzyme-substrate complex formation, and allowed for simulations of the substrate peptide interaction and mechanism of PKMTs with atomistic resolution by molecular dynamics and hybrid quantum mechanics/molecular mechanics methods. These simulation technologies uncovered important mechanistic details of the PKMT reaction mechanism including the processes responsible for the deprotonation of the target lysine residue, essential conformational changes of the PKMT upon substrate binding, but also rationalized regulatory principles like PKMT autoinhibition. Further developments are discussed that could bring us closer to a mechanistic understanding of catalysis of this important class of enzymes in the near future. The results described here illustrate the power of the investigation of enzyme mechanisms by the combined application of biochemical experiments and simulation technologies.

New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics.

Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.

Conservation laws and the foundations of quantum mechanics.

In a recent paper, [Y. Aharonov, S. Popescu, D. Rohrlich, Proc. Natl. Acad. Sci. U.S.A.118 e1921529118 (2021)], it was argued that while the standard definition of conservation laws in quantum mechanics, which is of a statistical character, is perfectly valid, it misses essential features of nature and it can and must be revisited to address the issue of conservation/nonconservation in individual cases. Specifically, in the above paper, an experiment was presented in which it can be proven that in some individual cases, energy is not conserved, despite being conserved statistically. It was felt however that this is worrisome and that something must be wrong if there are individual instances in which conservation does not hold, even though this is not required by the standard conservation law. Here, we revisit that experiment and show that although its results are correct, there is a way to circumvent them and ensure individual case conservation in that situation. The solution is however quite unusual, challenging one of the basic assumptions of quantum mechanics, namely that any quantum state can be prepared, and it involves a time-holistic, double nonconservation effect. Our results bring light on the role of the preparation stage of the initial state of a particle and on the interplay of conservation laws and frames of reference. We also conjecture that when such a full analysis of any conservation experiment is performed, conservation is obeyed in every individual case.

Mutation-induced shift of the photosystem II active site reveals insight into conserved water channels.

Photosystem II (PSII) is the water-plastoquinone photo-oxidoreductase central to oxygenic photosynthesis. PSII has been extensively studied for its ability to catalyze light-driven water oxidation at a Mn4CaO5 cluster called the oxygen-evolving complex (OEC). Despite these efforts, the complete reaction mechanism for water oxidation by PSII is still heavily debated. Previous mutagenesis studies have investigated the roles of conserved amino acids, but these studies have lacked a direct structural basis that would allow for a more meaningful interpretation. Here, we report a 2.14-Å resolution cryo-EM structure of a PSII complex containing the substitution Asp170Glu on the D1 subunit. This mutation directly perturbs a bridging carboxylate ligand of the OEC, which alters the spectroscopic properties of the OEC without fully abolishing water oxidation. The structure reveals that the mutation shifts the position of the OEC within the active site without markedly distorting the Mn4CaO5 cluster metal-metal geometry, instead shifting the OEC as a rigid body. This shift disturbs the hydrogen-bonding network of structured waters near the OEC, causing disorder in the conserved water channels. This mutation-induced disorder appears consistent with previous FTIR spectroscopic data. We further show using quantum mechanics/molecular mechanics methods that the mutation-induced structural changes can affect the magnetic properties of the OEC by altering the axes of the Jahn-Teller distortion of the Mn(III) ion coordinated to D1-170. These results offer new perspectives on the conserved water channels, the rigid body property of the OEC, and the role of D1-Asp170 in the enzymatic water oxidation mechanism.

Chemical diversity in angiosperms - monoterpene synthases control complex reactions that provide the precursors for ecologically and commercially important monoterpenoids.

Monoterpene synthases (MTSs) catalyze the first committed step in the biosynthesis of monoterpenoids, a class of specialized metabolites with particularly high chemical diversity in angiosperms. In addition to accomplishing a rate enhancement, these enzymes manage the formation and turnover of highly reactive carbocation intermediates formed from a prenyl diphosphate substrate. At each step along the reaction path, a cationic intermediate can be subject to cyclization, migration of a proton, hydride, or alkyl group, or quenching to terminate the sequence. However, enzymatic control of ligand folding, stabilization of specific intermediates, and defined quenching chemistry can maintain the specificity for forming a signature product. This review article will discuss our current understanding of how angiosperm MTSs control the reaction environment. Such knowledge allows inferences about the origin and regulation of chemical diversity, which is pertinent for appreciating the role of monoterpenoids in plant ecology but also for aiding commercial efforts that harness the accumulation of these specialized metabolites for the food, cosmetic, and pharmaceutical industries.

A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

Interference of nuclear wavepackets in a pair of proton transfer reactions.

Quantum mechanics revolutionized chemists' understanding of molecular structure. In contrast, the kinetics of molecular reactions in solution are well described by classical, statistical theories. To reveal how the dynamics of chemical systems transition from quantum to classical, we study femtosecond proton transfer in a symmetric molecule with two identical reactant sites that are spatially apart. With the reaction launched from a superposition of two local basis states, we hypothesize that the ensuing motions of the electrons and nuclei will proceed, conceptually, in lockstep as a superposition of probability amplitudes until decoherence collapses the system to a product. Using ultrafast spectroscopy, we observe that the initial superposition state affects the reaction kinetics by an interference mechanism. With the aid of a quantum dynamics model, we propose how the evolution of nuclear wavepackets manifests the unusual intersite quantum correlations during the reaction.

Accurate prediction of dynamic protein-ligand binding using P-score ranking.

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

Optical and supramolecular properties of cyclometalated palladium nanostructures with tumor targeting properties in living mice: a quantum mechanics interpretation.

The recent discovery that metallophilic interactions between cyclometalated palladium supramolecular nanostructures - with efficient tumour accumulation rate in a skin melanoma model - maintain excellent photodynamic properties even in a hypoxic microenvironment has inspired the present study focused on the theoretical predictions of optical properties of the bis-cyclometalated palladium compound in different contexts. More specifically, structural and UV/Vis absorption properties of both monomeric and dimeric forms of this anticancer drug are well reproduced with a Time-Dependent Density Functional Theoretical (TD-DFT) approach based on Exchange-Correlation (XC) hybrid functionals in conjunction with conductor-like and polarization solvation effects. A further novelty is represented by a fine investigation of the supramolecular interactions between the different subunits of the drug via dispersion force correction and Quantum Theory of Atoms in Molecules (QTAIM). This contribution while supporting the photoexcitation properties derived in laboratory following the self-assembly of monomeric units when passing from dimethyl sulfoxide (DMSO) to a H2O/DMSO mixture at 298K, shed some light on the nature of the chemical interactions modulating the formation of nano-size aggregates.

When Tautomers Matter: UV-Vis Absorption Spectra of Hypoxanthine in Aqueous Solution from Fully Atomistic Simulations.

The UV-Vis spectrum of the solvated purine derivative Hypoxanthine (HYX) is investigated using the Quantum Mechanics/Fluctuating Charges (QM/FQ) multiscale approach combined with a sampling of configurations through atomistic Molecular Dynamics (MD) simulations. Keto 1H7H and 1H9H tautomeric forms of HYX are the most stable in aqueous solution and form different stable complexes with the surrounding water molecules, ultimately affecting the electronic absorption spectra. The final simulated spectrum resulting from the combination of the individual spectra of tautomers agrees very well with most of the characteristics in the measured spectrum. The importance of considering the effect of the solute tautomers and, in parallel, the contribution of the different solvent arrangements around the solute when modeling spectral properties, is highlighted. In addition, the high quality of the computed spectra leads to suggesting an alternative way for acquiring tautomeric populations from combined computational/experimental spectra.

COSMOPharm: Drug-Polymer Compatibility of Pharmaceutical Amorphous Solid Dispersions from COSMO-SAC.

The quantum mechanics-aided COSMO-SAC activity coefficient model is applied and systematically examined for predicting the thermodynamic compatibility of drugs and polymers. The drug-polymer compatibility is a key aspect in the rational selection of optimal polymeric carriers for pharmaceutical amorphous solid dispersions (ASD) that enhance drug bioavailability. The drug-polymer compatibility is evaluated in terms of both solubility and miscibility, calculated using standard thermodynamic equilibrium relations based on the activity coefficients predicted by COSMO-SAC. As inherent to COSMO-SAC, our approach relies only on quantum-mechanically derived σ-profiles of the considered molecular species and involves no parameter fitting to experimental data. All σ-profiles used were determined in this work, with those of the polymers being derived from their shorter oligomers by replicating the properties of their central monomer unit(s). Quantitatively, COSMO-SAC achieved an overall average absolute deviation of 13% in weight fraction drug solubility predictions compared to experimental data. Qualitatively, COSMO-SAC correctly categorized different polymer types in terms of their compatibility with drugs and provided meaningful estimations of the amorphous-amorphous phase separation. Furthermore, we analyzed the sensitivity of the COSMO-SAC results for ASD to different model configurations and σ-profiles of polymers. In general, while the free volume and dispersion terms exerted a limited effect on predictions, the structures of oligomers used to produce σ-profiles of polymers appeared to be more important, especially in the case of strongly interacting polymers. Explanations for these observations are provided. COSMO-SAC proved to be an efficient method for compatibility prediction and polymer screening in ASD, particularly in terms of its performance-cost ratio, as it relies only on first-principles calculations for the considered molecular species. The open-source nature of both COSMO-SAC and the Python-based tool COSMOPharm, developed in this work for predicting the API-polymer thermodynamic compatibility, invites interested readers to explore and utilize this method for further research or assistance in the design of pharmaceutical formulations.

Computational discovery of dual potential inhibitors of SARS-CoV-2 spike/ACE2 and Mpro: 3D-pharmacophore, docking-based virtual screening, quantum mechanics and molecular dynamics.

To find drugs against COVID-19, caused by the SARS-CoV-2, promising targets include the fusion of the viral spike with the human angiotensin-converting enzyme 2 (ACE2) as well as the main protease (Mpro). These proteins are responsible for viral entry and replication, respectively. We combined several state-of-the-art computational methods, including, protein-ligand interaction fingerprint, 3D-pharmacophores, molecular-docking, MM-GBSA, DFT, and MD simulations to explore two databases: ChEMBL and NANPDB to identify molecules that could both block spike/ACE2 fusion and inhibit Mpro. A total of 1,690,649 compounds from the two databases were screened using the pharmacophore model obtained from PLIF analysis. Five recent complexes of Mpro co-crystallized with different ligands were used to generate the pharmacophore model, allowing 4,829 compounds that passed this prefilter. These were then submitted to molecular docking against Mpro. The 5% top-ranked docking hits from docking result having scores < -8.32 kcal mol-1 were selected and then docked against spike/ACE2. Only four compounds: ChEMBL244958, ChEMBL266531, ChEMBL3680003, and 1-methoxy-3-indolymethyl glucosinolate (4) displayed binding energies < - 8.21 kcal mol-1 (for the native ligand) were considered as putative dual-target inhibitors. Furthermore, predictive ADMET, MM-GBSA and DFT/6-311G(d,p) were performed on these compounds and compared with those of well-known antivirals. DFT calculations showed that ChEMBL244958 and compound 4 had significant predicted reactivity values. Molecular dynamics simulations of the docked complexes were run for 100 ns and used to validate the stability docked poses and to confirm that these hits are putative dual binders of the spike/ACE2 and the Mpro.

A proof of Specker's principle.

Specker's principle, the condition that pairwise orthogonal propositions must be jointly orthogonal (or rather, the 'exclusivity principle' that follows from it), has been much investigated recently within the programme of finding physical principles to characterize quantum mechanics. Specker's principle, however, largely appears to lack a physical justification. In this paper, I present a proof of Specker's principle from three assumptions (made suitably precise): the existence of 'maximal entanglement', the existence of 'non-maximal measurements' and no-signalling. I discuss these three assumptions and describe canonical examples of non-Specker sets of propositions satisfying any two of them. These examples display analogies with various approaches to the interpretation of quantum mechanics, including retrocausation. I also discuss connections with the work of Popescu & Rohrlich. The core of the proof (and the main example violating no-signalling) is illustrated by a variant of Specker's tale of the seer of Nineveh, with which I open the paper. This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'.

On conservation laws in quantum mechanics.

We raise fundamental questions about the very meaning of conservation laws in quantum mechanics, and we argue that the standard way of defining conservation laws, while perfectly valid as far as it goes, misses essential features of nature and has to be revisited and extended.

Computational simulations uncover enantioselective metabolism of chiral triazole fungicides by human CYP450 enzymes: A case study of tebuconazole.

Tebuconazole (TEB), a prominent chiral triazole fungicide, has been extensively utilized for plant pathogen control globally. Despite experimental evidence of TEB metabolism in mammals, the enantioselectivity in the biotransformation of R- and S-TEB enantiomers by specific CYP450s remains elusive. In this work, integrated in silico simulations were employed to unveil the binding interactions and enantioselective metabolic fate of TEB enantiomers within human CYP1A2, 2B6, 2E1, and 3A4. Molecular dynamics (MD) simulations clearly delineated the binding specificity of R- and S-TEB to the four CYP450s, crucially determining their differences in metabolic activity and enantioselectivity. The primary driving force for robust ligand binding was identified as van der Waals interactions with CYP450s, particularly involving the hydrophobic residues. Mechanistic insights derived from quantum mechanics/molecular mechanics (QM/MM) calculations established C2-methyl hydroxylation as the predominant route of R-/S-TEB metabolism, while C6-hydroxylation and triazol epoxidation were deemed kinetically infeasible pathways. Specifically, the resulting hydroxy-R-TEB metabolite primarily originates from R-TEB biotransformation by 1A2, 2E1 and 3A4, whereas hydroxy-S-TEB is preferentially produced by 2B6. These findings significantly contribute to our comprehension of the binding specificity and enantioselective metabolic fate of chiral TEB by CYP450s, potentially informing further research on human health risk assessment associated with TEB exposure.

A High-Finesse Suspended Interferometric Sensor for Macroscopic Quantum Mechanics with Femtometre Sensitivity.

We present an interferometric sensor for investigating macroscopic quantum mechanics on a table-top scale. The sensor consists of a pair of suspended optical cavities with finesse over 350,000 comprising 10 g fused silica mirrors. The interferometer is suspended by a four-stage, light, in-vacuum suspension with three common stages, which allows for us to suppress common-mode motion at low frequency. The seismic noise is further suppressed by an active isolation scheme, which reduces the input motion to the suspension point by up to an order of magnitude starting from 0.7 Hz. In the current room-temperature operation, we achieve a peak sensitivity of 0.5 fm/Hz in the acoustic frequency band, limited by a combination of readout noise and suspension thermal noise. Additional improvements of the readout electronics and suspension parameters will enable us to reach the quantum radiation pressure noise. Such a sensor can eventually be utilized for demonstrating macroscopic entanglement and for testing semi-classical and quantum gravity models.

Ab Initio Manganese Kα and Kß Energy Eigenvalues, Shake-Off Probabilities, Auger Rates, with Convergence Tests.

This work presents ab initio calculations for the Kα spectrum of manganese (Z = 25, [Ar]3d54s2), a highly complex system due to the five open orbitals in the 3d shell. The spectrum is composed of the canonical diagram line [1s]→[2p] and shake-off satellite lines [1snl]→[2pnl] (nl∈{2s,2p,3s,3p,3d,4s}), where square brackets denote a hole state. The multiconfiguration Dirac-Hartree-Fock method with the active set approach provides the initial and final atomic wavefunctions. Results are presented as energy eigenvalue spectra for the diagram and satellite transitions. The calculated wavefunctions include over one hundred million configuration state functions and over 280,000 independent transition energies for the seven sets of spectra considered. Shake-off probabilities and Auger transition rates determine satellite intensities. The number of configuration state functions ensures highly-converged wavefunctions. Several measures of convergence demonstrate convergence in the calculated parameters. We obtain convergence of the transition energies in all eight transitions to within 0.06 eV and shake-off probabilities to within 4.5%.

The Role of Hydrogen Bonding in the Raman Spectral Signals of Caffeine in Aqueous Solution.

The identification and quantification of caffeine is a common need in the food and pharmaceutical industries and lately also in the field of environmental science. For that purpose, Raman spectroscopy has been used as an analytical technique, but the interpretation of the spectra requires reliable and accurate computational protocols, especially as regards the Resonance Raman (RR) variant. Herein, caffeine solutions are sampled using Molecular Dynamics simulations. Upon quantification of the strength of the non-covalent intermolecular interactions such as hydrogen bonding between caffeine and water, UV-Vis, Raman, and RR spectra are computed. The results provide general insights into the hydrogen bonding role in mediating the Raman spectral signals of caffeine in aqueous solution. Also, by analyzing the dependence of RR enhancement on the absorption spectrum of caffeine, it is proposed that the sensitivity of the RR technique could be exploited at excitation wavelengths moderately far from 266 nm, yet achieving very low detection limits in the quantification caffeine content.

Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal-Chelator Complexes.

Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal-chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug's efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal-chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal-chelator complexes, with a direct impact for rational drug design of this important class of drugs.