Influence of structural moieties in squaraine dyes on optoacoustic signal shape and intensity.

Optoacoustic imaging has grown in clinical relevance due to inherent advantages in sensitivity, resolution, and imaging depth, but the development of contrast agents is lacking. This study assesses the influence of structural features of squaraine dyes on optoacoustic activity through computational models, in vitro testing, and in vivo experimentation. The squaraine scaffold was decorated with halogens and side-chain extensions. Extension of side chains and heavy halogenation of squaraines both increased optoacoustic signals individually, although they had a more significant effect in tandem. Density functional theory models suggest that the origin of the increased optoacoustic signal is the increase in transition dipole moment and vibrational entropy, which manifested as increased absorbance in near-infrared region (NIR) wavelengths and decreased fluorescence quantum yield. This study provides insight into the structure-function relationships that will lead guiding principles for optimizing optoacoustic contrast agents. Further developments of squaraines and other agents will further increase the relevance of optoacoustic imaging in a clinical setting.

Erratum: Influence of structural moieties in squaraine dyes on optoacoustic signal shape and intensity.

[This corrects the article PMC11087056.].

Picture book reading improves children's learning understanding.

Mental state reasoning is an integral part of children's teaching and learning understanding. This study investigated whether a picture book reading approach focusing on mental state discourse and contrasting perspectives in a preschool classroom setting would improve children's teaching and learning understanding and school readiness. In total, 104 children from four classrooms aged between 46 and 64 months (53 girls, M = 54.03 months, SD = 3.68) participated in the study. Half of the classrooms were randomly assigned to an experimental group where teachers read picture books rich in mental state discourse and engaged in intensive discussions with children for eight weeks. Children's false belief understanding and teaching and learning understanding were measured before and after the eight-week period. The result revealed that picture book reading improved children's learning understanding with a medium effect size, controlling for demographic variables, children's verbal ability, inhibition, and initial false belief understanding. The experimental group children further demonstrated more advanced school readiness 18 months after the intervention ended in a follow-up study using a teacher questionnaire.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development.

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Preclinical Evaluation of Novel Positron Emission Tomography (PET) Probes for Imaging Leucine-Rich Repeat Kinase 2 (LRRK2).

Parkinson's disease (PD) is one of the most highly debilitating neurodegenerative disorders, which affects millions of people worldwide, and leucine-rich repeat kinase 2 (LRRK2) mutations have been involved in the pathogenesis of PD. Developing a potent LRRK2 positron emission tomography (PET) tracer would allow for in vivo visualization of LRRK2 distribution and expression in PD patients. In this work, we present the facile synthesis of two potent and selective LRRK2 radioligands [11C]3 ([11C]PF-06447475) and [18F]4 ([18F]PF-06455943). Both radioligands exhibited favorable brain uptake and specific bindings in rodent autoradiography and PET imaging studies. More importantly, [18F]4 demonstrated significantly higher brain uptake in the transgenic LRRK2-G2019S mutant and lipopolysaccharide (LPS)-injected mouse models. This work may serve as a roadmap for the future design of potent LRRK2 PET tracers.

Training machine learning potentials for reactive systems: A Colab tutorial on basic models.

In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field - the training of system-specific MLPs for reactive systems - with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self-guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel-based (using Gaussian process regression, GPR) models by fitting the two-dimensional Müller-Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot-SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.

In vivo three-dimensional brain imaging with chemiluminescence probes in Alzheimer's disease models.

Optical three-dimensional (3D) molecular imaging is highly desirable for providing precise distribution of the target-of-interest in disease models. However, such 3D imaging is still far from wide applications in biomedical research; 3D brain optical molecular imaging, in particular, has rarely been reported. In this report, we designed chemiluminescence probes with high quantum yields, relatively long emission wavelengths, and high signal-to-noise ratios to fulfill the requirements for 3D brain imaging in vivo. With assistance from density-function theory (DFT) computation, we designed ADLumin-Xs by locking up the rotation of the double bond via fusing the furan ring to the phenyl ring. Our results showed that ADLumin-5 had a high quantum yield of chemiluminescence and could bind to amyloid beta (Aß). Remarkably, ADLumin-5's radiance intensity in brain areas could reach 4 × 107 photon/s/cm2/sr, which is probably 100-fold higher than most chemiluminescence probes for in vivo imaging. Because of its strong emission, we demonstrated that ADLumin-5 could be used for in vivo 3D brain imaging in transgenic mouse models of Alzheimer's disease.

Public perceptions of the FDA's marketing authorization of Vuse on Twitter/X.

Introduction: On October 12, 2021, the FDA issued its first marketing granted orders for Vuse, the e-cigarette product by R.J. Reynolds Vapor Company. The public perceptions and reactions to the FDA's Vuse authorization are prevalent on social media platforms such as Twitter/X. We aim to understand public perceptions of the FDA's Vuse authorization in the US using Twitter/X data. Methods: Through the Twitter/X streaming API (Application Programming Interface), 3,852 tweets between October 12, 2021, and October 23, 2021, were downloaded using the keyword of Vuse. With the elimination of retweets, irrelevant tweets, and tweets from other countries, the final dataset consisted of 523 relevant tweets from the US. Based on their attitudes toward the FDA authorization on Vuse, these tweets were coded into three major categories: positive, negative, and neutral. These tweets were further manually classified into different categories based on their contents. Results: There was a large peak on Twitter/X mentioning FDA's Vuse authorization on October 13, 2021, just after the authorization was announced. Of the 523 US tweets related to FDA's Vuse authorization, 6.12% (n=32) were positive, 26.77% (n=140) were negative, and 67.11% (n=351) were neutral. In positive tweets, the dominant subcategory was Cessation Claims (n=18, 56.25%). In negative tweets, the topics Health Risk (n=43, 30.71%), Criticize Authorization (n=42, 30.00%), and Big Tobacco (n=40, 38.57%) were the major topics. News (n=271, 77.21%) was the most prevalent topic among neutral tweets. In addition, tweets with a positive attitude tend to have more likes. Discussion: Public perceptions and discussions on Twitter/X regarding the FDA's Vuse authorization in the US showed that Twitter/X users were more likely to show a negative than a positive attitude with a major concern about health risks.

Free Energy Profile Decomposition Analysis for QM/MM Simulations of Enzymatic Reactions.

In enzyme mechanistic studies and mutant design, it is highly desirable to know the individual residue contributions to the reaction free energy and barrier. In this work, we show that such free energy contributions from each residue can be readily obtained by postprocessing ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulation trajectories. Specifically, through a mean force integration along the minimum free energy pathway, one can obtain the electrostatic, polarization, and van der Waals contributions from each residue to the free energy barrier. Separately, a similar analysis procedure allows us to assess the contribution from different collective variables along the reaction coordinate. The chorismate mutase reaction is used to demonstrate the utilization of these two trajectory analysis tools.

Nature of Hops, Coordinates, and Detailed Balance for Nonadiabatic Simulations in the Condensed Phase.

Photoinduced processes play a crucial role in a multitude of important molecular phenomena. Accurately modeling these processes in an environment other than a vacuum requires a detailed description of the electronic states involved as well as how energy flows are coupled to the surroundings. Nonadiabatic effects must also be included in order to describe the exchange of energy between electronic and nuclear degrees of freedom correctly. In this work, we revisit the ring-opening reaction 1,3-cylohexadiene (CHD) in a solvent environment. Using our newly developed Interface for Non-Adiabatic Quantum mechanics/molecular mechanics in Solvent (INAQS) we trace the evolution of the reaction via hybrid quantum mechanics/molecular mechanics (QM/MM) surface hopping with a focus on the solvent's participation in the nonadiabatic relaxation process and the long-time approach to equilibrium. We explicitly include the MM solvent contribution to the nonadiabatic coupling vector─enabling an accurate approach to equilibrium at long times─and find that in highly multidimensional systems gradients can have little or nothing to do with the nonadiabatic couplings.

Bridging semiempirical and ab initio QM/MM potentials by Gaussian process regression and its sparse variants for free energy simulation.

Free energy simulations that employ combined quantum mechanical and molecular mechanical (QM/MM) potentials at ab initio QM (AI) levels are computationally highly demanding. Here, we present a machine-learning-facilitated approach for obtaining AI/MM-quality free energy profiles at the cost of efficient semiempirical QM/MM (SE/MM) methods. Specifically, we use Gaussian process regression (GPR) to learn the potential energy corrections needed for an SE/MM level to match an AI/MM target along the minimum free energy path (MFEP). Force modification using gradients of the GPR potential allows us to improve configurational sampling and update the MFEP. To adaptively train our model, we further employ the sparse variational GP (SVGP) and streaming sparse GPR (SSGPR) methods, which efficiently incorporate previous sample information without significantly increasing the training data size. We applied the QM-(SS)GPR/MM method to the solution-phase SN2 Menshutkin reaction, NH3+CH3Cl→CH3NH3++Cl-, using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. For 4000 configurations sampled along the MFEP, the iteratively optimized AM1-SSGPR-4/MM model reduces the energy error in AM1/MM from 18.2 to 4.4 kcal/mol. Although not explicitly fitting forces, our method also reduces the key internal force errors from 25.5 to 11.1 kcal/mol/Å and from 30.2 to 10.3 kcal/mol/Å for the N-C and C-Cl bonds, respectively. Compared to the uncorrected simulations, the AM1-SSGPR-4/MM method lowers the predicted free energy barrier from 28.7 to 11.7 kcal/mol and decreases the reaction free energy from -12.4 to -41.9 kcal/mol, bringing these results into closer agreement with their AI/MM and experimental benchmarks.

Visualization of Electron Density Changes Along Chemical Reaction Pathways.

We propose a simple procedure for visualizing the electron density changes (EDC) during a chemical reaction, which is based on a mapping of rectangular grid points for a stationary structure into (distorted) positions around atoms of another stationary structure. Specifically, during a small step along the minimum energy pathway (MEP), the displacement of each grid point is obtained as a linear combination of the motion of all atoms, with the contribution from each atom scaled by the corresponding Hirshfeld weight. For several reactions (identity SN2, Claisen rearrangement, Diels-Alder reaction, [3+2] cycloaddition, and phenylethyl mercaptan attack on pericosine A), our EDC plots showed an expected reduction of electron densities around severed bonds (or those with the bond-order lowered), with the opposite observed for newly-formed or enhanced chemical bonds. The EDC plots were also shown for copper triflate catalyzed N2O fragmentation, where the N-O bond weakening initially occurred on a singlet surface, but continued on a triplet surface after reaching the minimum-energy crossing point (MECP) between the two potential energy surfaces.

Interactions of arylhydroxylamines and alkylaldoximes with a rhodium porphyrin.

Heme enzymes are involved in the binding and metabolism of hydroxylamine (RNHOH) and aldoxime (RCH=NOH) compounds (R = H, alkyl, aryl). We report the synthesis and X-ray crystal structure of a metalloporphyrin in complex with an arylhydroxylamine, namely that of (TPP)Rh(PhNHOH)(C6H4Cl) (TPP = tetraphenylpophryinato dianion). The crystal structure reveals, in addition to N-binding of PhNHOH to Rh, the presence of an intramolecular H-bond between the hydroxylamine -OH proton and a porphyrin N-atom. Results from density functional theory (DFT) calculations support the presence of this intramolecular H-bond in this global minimum structure, and a natural bond order (NBO) analysis reveals that this H-bond comprises a donor π N=C (porphyrin) to acceptor σ* O-H (hydroxylamine) interaction of 2.32 kcal/mol. While DFT calculations predict the presence of similar intramolecular H-bond interactions in the related aldoxime complexes (TPP)Rh(RCH=NOH)(C6H4Cl) in their global minima structures, the X-ray crystal structure obtained for the (TPP)Rh(CH3(CH2)2CH=NOH)(C6H4Cl) complex is consistent with the local (non-global) minima conformation that does not have this intramolecular H-bond interaction.

In Vivo Three-dimensional Brain Imaging with Chemiluminescence Probes in Alzheimer's Disease Models.

Optical three-dimensional (3D) molecular imaging is highly desirable for providing precise distribution of the target-of-interest in disease models. However, such 3D imaging is still far from wide applications in biomedical research; 3D brain optical molecular imaging, in particular, has rarely been reported. In this report, we designed chemiluminescence probes with high quantum yields (QY), relatively long emission wavelengths, and high signal-to-noise ratios (SNRs) to fulfill the requirements for 3D brain imaging in vivo. With assistance from density-function theory (DFT) computation, we designed ADLumin-Xs by locking up the rotation of the double-bond via fusing the furan ring to the phenyl ring. Our results showed that ADLumin-5 had a high quantum yield of chemiluminescence and could bind to amyloid beta (Aß). Remarkably, ADLumin-5's radiance intensity in brain areas could reach 4×107 photon/s/cm2/sr, which is probably 100-fold higher than most chemiluminescence probes for in vivo imaging. Because of its strong emission, we demonstrated that ADLumin-5 could be used for in vivo 3D brain imaging in transgenic mouse models of Alzheimer's disease (AD).

Perspective: Reference-Potential Methods for the Study of Thermodynamic Properties in Chemical Processes: Theory, Applications, and Pitfalls.

In silico investigations of enzymatic reactions and chemical reactions in condensed phases often suffer from formidable computational costs due to a large number of degrees of freedom and enormous important volume in phase space. Usually, accuracy must be compromised to trade for efficiency by lowering the reliability of the Hamiltonians employed or reducing the sampling time. Reference-potential methods (RPMs) offer an alternative approach to reaching high accuracy of simulation without much loss of efficiency. In this Perspective, we summarize the idea of RPMs and showcase some recent applications. Most importantly, the pitfalls of these methods are also discussed, and remedies to these pitfalls are presented.

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations.

Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol-1 Å-1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol-1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.

Analytical gradients and derivative couplings for the TDDFT-1D method.

We derive and implement analytic gradients and derivative couplings for time-dependent density functional theory plus one double (TDDFT-1D) which is a semiempirical configuration interaction method whereby the Hamiltonian is diagonalized in a basis of all singly excited configurations and one doubly excited configuration as constructed from a set of reference Kohn-Sham orbitals. We validate the implementation by comparing against finite difference values. Furthermore, we show that our implementation can locate both optimized geometries and minimum-energy crossing points along conical seams of S1/S0 surfaces for a set of test cases.

Computational Investigation of Substituent Effects on the Fluorescence Wavelengths of Oxyluciferin Analogs.

Oxyluciferin, which is the light emitter for firefly bioluminescence, has been subjected to extensive chemical modifications to tune its emission wavelength and quantum yield. However, the exact mechanisms for various electron-donating and withdrawing groups to perturb the photophysical properties of oxyluciferin analogs are still not fully understood. To elucidate the substituent effects on the fluorescence wavelength of oxyluciferin analogs, we applied the absolutely localized molecular orbitals (ALMO)-based frontier orbital analysis to assess various types of interactions (i.e. permanent electrostatics/exchange repulsion, polarization, occupied-occupied orbital mixing, virtual-virtual orbital mixing, and charge-transfer) between the oxyluciferin and substituent orbitals. We suggested two distinct mechanisms that can lead to red-shifted oxyluciferin emission wavelength, a design objective that can help increase the tissue penetration of bioluminescence emission. Within the first mechanism, an electron-donating group (such as an amino or dimethylamino group) can contribute its highest occupied molecular orbital (HOMO) to an out-of-phase combination with oxyluciferin's HOMO, thus raising the HOMO energy of the substituted analog and narrowing its HOMO-LUMO gap. Alternatively, an electron-withdrawing group (such as a nitro or cyano group) can participate in an in-phase virtual-virtual orbital mixing of fragment LUMOs, thus lowering the LUMO energy of the substituted analog. Such an ALMO-based frontier orbital analysis is expected to lead to intuitive principles for designing analogs of not only the oxyluciferin molecule, but also many other functional dyes.

Analytic high-order energy derivatives for metal nanoparticle-mediated infrared and Raman scattering spectra within the framework of quantum mechanics/molecular mechanics model with induced charges and dipoles.

This work is devoted to deriving and implementing analytic second- and third-order energy derivatives with respect to the nuclear coordinates and external electric field within the framework of the hybrid quantum mechanics/molecular mechanics method with induced charges and dipoles (QM/DIM). Using these analytic energy derivatives, one can efficiently compute the harmonic vibrational frequencies, infrared (IR) and Raman scattering (RS) spectra of the molecule in the proximity of noble metal clusters/nanoparticles. The validity and accuracy of these analytic implementations are demonstrated by the comparison of results obtained by the finite-difference method and the analytic approaches and by the full QM and QM/DIM calculations. The complexes formed by pyridine and two sizes of gold clusters (Au18 and Au32) at varying intersystem distances of 3, 4, and 5 Å are used as the test systems, and Raman spectra of 4,4'-bipyridine in the proximity of Au2057 and Ag2057 metal nanoparticles (MNP) are calculated by the QM/DIM method and compared with experimental results as well. We find that the QM/DIM model can well reproduce the IR spectra obtained from full QM calculations for all the configurations, while although it properly enhances some of the vibrational modes, it artificially overestimates RS spectral intensities of several modes for the systems with very short intersystem distance. We show that this could be improved, however, by incorporating the hyperpolarizability of the gold metal cluster in the evaluation of RS intensities. Additionally, we address the potential impact of charge migration between the adsorbate and MNPs.

Facilitating ab initio QM/MM free energy simulations by Gaussian process regression with derivative observations.

In combined quantum mechanical and molecular mechanical (QM/MM) free energy simulations, how to synthesize the accuracy of ab initio (AI) methods with the speed of semiempirical (SE) methods for a cost-effective QM treatment remains a long-standing challenge. In this work, we present a machine-learning-facilitated method for obtaining AI/MM-quality free energy profiles through efficient SE/MM simulations. In particular, we use Gaussian process regression (GPR) to learn the energy and force corrections needed for SE/MM to match with AI/MM results during molecular dynamics simulations. Force matching is enabled in our model by including energy derivatives into the observational targets through the extended-kernel formalism. We demonstrate the effectiveness of this method on the solution-phase SN2 Menshutkin reaction using AM1/MM and B3LYP/6-31+G(d,p)/MM as the base and target levels, respectively. Trained on only 80 configurations sampled along the minimum free energy path (MFEP), the resulting GPR model reduces the average energy error in AM1/MM from 18.2 to 5.8 kcal mol-1 for the 4000-sample testing set with the average force error on the QM atoms decreased from 14.6 to 3.7 kcal mol-1 Å-1. Free energy sampling with the GPR corrections applied (AM1-GPR/MM) produces a free energy barrier of 14.4 kcal mol-1 and a reaction free energy of -34.1 kcal mol-1, in closer agreement with the AI/MM benchmarks and experimental results.