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.

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies.

BACKGROUND: RNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class predictors, reflecting the disease class, can be constructed for known tissue types using the gene expression measurements extracted from cancer patients. One challenge of current cancer predictors is that they often have suboptimal performance estimates when integrating molecular datasets generated from different labs. Often, the quality of the data is variable, procured differently, and contains unwanted noise hampering the ability of a predictive model to extract useful information. Data preprocessing methods can be applied in attempts to reduce these systematic variations and harmonize the datasets before they are used to build a machine learning model for resolving tissue of origins. RESULTS: We aimed to investigate the impact of data preprocessing steps-focusing on normalization, batch effect correction, and data scaling-through trial and comparison. Our goal was to improve the cross-study predictions of tissue of origin for common cancers on large-scale RNA-Seq datasets derived from thousands of patients and over a dozen tumor types. The results showed that the choice of data preprocessing operations affected the performance of the associated classifier models constructed for tissue of origin predictions in cancer. CONCLUSION: By using TCGA as a training set and applying data preprocessing methods, we demonstrated that batch effect correction improved performance measured by weighted F1-score in resolving tissue of origin against an independent GTEx test dataset. On the other hand, the use of data preprocessing operations worsened classification performance when the independent test dataset was aggregated from separate studies in ICGC and GEO. Therefore, based on our findings with these publicly available large-scale RNA-Seq datasets, the application of data preprocessing techniques to a machine learning pipeline is not always appropriate.

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

[This corrects the article PMC11087056.].

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.

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.

RAS-dependent RAF-MAPK hyperactivation by pathogenic RIT1 is a therapeutic target in Noonan syndrome-associated cardiac hypertrophy.

RIT1 is a RAS guanosine triphosphatase (GTPase) that regulates different aspects of signal transduction and is mutated in lung cancer, leukemia, and in the germline of individuals with Noonan syndrome. Pathogenic RIT1 proteins promote mitogen-activated protein kinase (MAPK) hyperactivation; however, this mechanism remains poorly understood. Here, we show that RAF kinases are direct effectors of membrane-bound mutant RIT1 necessary for MAPK activation. We identify critical residues in RIT1 that facilitate interaction with membrane lipids and show that these are necessary for association with RAF kinases and MAPK activation. Although mutant RIT1 binds to RAF kinases directly, it fails to activate MAPK signaling in the absence of classical RAS proteins. Consistent with aberrant RAF/MAPK activation as a driver of disease, we show that pathway inhibition alleviates cardiac hypertrophy in a mouse model of RIT1 mutant Noonan syndrome. These data shed light on the function of pathogenic RIT1 and identify avenues for therapeutic intervention.

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).

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.

Assessment of cholesterol homeostasis in the living human brain.

Alterations in brain cholesterol homeostasis have been broadly implicated in neurological disorders. Notwithstanding the complexity by which cholesterol biology is governed in the mammalian brain, excess neuronal cholesterol is primarily eliminated by metabolic clearance via cytochrome P450 46A1 (CYP46A1). No methods are currently available for visualizing cholesterol metabolism in the living human brain; therefore, a noninvasive technology that quantitatively measures the extent of brain cholesterol metabolism via CYP46A1 could broadly affect disease diagnosis and treatment options using targeted therapies. Here, we describe the development and testing of a CYP46A1-targeted positron emission tomography (PET) tracer, 18F-CHL-2205 (18F-Cholestify). Our data show that PET imaging readouts correlate with CYP46A1 protein expression and with the extent to which cholesterol is metabolized in the brain, as assessed by cross-species postmortem analyses of specimens from rodents, nonhuman primates, and humans. Proof of concept of in vivo efficacy is provided in the well-established 3xTg-AD murine model of Alzheimer's disease (AD), where we show that the probe is sensitive to differences in brain cholesterol metabolism between 3xTg-AD mice and control animals. Furthermore, our clinical observations point toward a considerably higher baseline brain cholesterol clearance via CYP46A1 in women, as compared to age-matched men. These findings illustrate the vast potential of assessing brain cholesterol metabolism using PET and establish PET as a sensitive tool for noninvasive assessment of brain cholesterol homeostasis in the clinic.

Epitope alteration by small molecules and applications in drug discovery.

Small molecules and antibodies are normally considered separately in drug discovery, except in the case of covalent conjugates. We unexpectedly discovered several small molecules that could inhibit or enhance antibody-epitope interactions which opens new possibilities in drug discovery and therapeutic modulation of auto-antibodies. We first discovered a small molecule, CRANAD-17, that enhanced the binding of an antibody to amyloid beta (Aß), one of the major hallmarks of Alzheimer's disease, by stable triplex formation. Next, we found several small molecules that altered antibody-epitope interactions of tau and PD-L1 proteins, demonstrating the generality of this phenomenon. We report a new screening technology for ligand discovery, screening platform based on epitope alteration for drug discovery (SPEED), which is label-free for both the antibody and small molecule. SPEED, applied to an Aß antibody, led to the discovery of a small molecule, GNF5837, that inhibits Aß aggregation and another, obatoclax, that binds Aß plaques and can serve as a fluorescent reporter in brain slices of AD mice. We also found a small molecule that altered the binding between Aß and auto-antibodies from AD patient serum. SPEED reveals the sensitivity of antibody-epitope interactions to perturbation by small molecules and will have multiple applications in biotechnology and drug discovery.

Accelerating Ab Initio Quantum Mechanical and Molecular Mechanical (QM/MM) Molecular Dynamics Simulations with Multiple Time Step Integration and a Recalibrated Semiempirical QM/MM Hamiltonian.

Molecular dynamics (MD) simulations employing ab initio quantum mechanical and molecular mechanical (ai-QM/MM) potentials are considered to be the state of the art, but the high computational cost associated with the ai-QM calculations remains a theoretical challenge for their routine application. Here, we present a modified protocol of the multiple time step (MTS) method for accelerating ai-QM/MM MD simulations of condensed-phase reactions. Within a previous MTS protocol [Nam J. Chem. Theory Comput. 2014, 10, 4175], reference forces are evaluated using a low-level (semiempirical QM/MM) Hamiltonian and employed at inner time steps to propagate the nuclear motions. Correction forces, which arise from the force differences between high-level (ai-QM/MM) and low-level Hamiltonians, are applied at outer time steps, where the MTS algorithm allows the time-reversible integration of the correction forces. To increase the outer step size, which is bound by the highest-frequency component in the correction forces, the semiempirical QM Hamiltonian is recalibrated in this work to minimize the magnitude of the correction forces. The remaining high-frequency modes, which are mainly bond stretches involving hydrogen atoms, are then removed from the correction forces. When combined with a Langevin or SIN(R) thermostat, the modified MTS-QM/MM scheme remains robust with an up to 8 (with Langevin) or 10 fs (with SIN(R)) outer time step (with 1 fs inner time steps) for the chorismate mutase system. This leads to an over 5-fold speedup over standard ai-QM/MM simulations, without sacrificing the accuracy in the predicted free energy profile of the reaction.

A novel monoacylglycerol lipase-targeted 18F-labeled probe for positron emission tomography imaging of brown adipose tissue in the energy network.

Monoacylglycerol lipase (MAGL) constitutes a serine hydrolase that orchestrates endocannabinoid homeostasis and exerts its function by catalyzing the degradation of 2-arachidonoylglycerol (2-AG) to arachidonic acid (AA). As such, selective inhibition of MAGL represents a potential therapeutic and diagnostic approach to various pathologies including neurodegenerative disorders, metabolic diseases and cancers. Based on a unique 4-piperidinyl azetidine diamide scaffold, we developed a reversible and peripheral-specific radiofluorinated MAGL PET ligand [18F]FEPAD. Pharmacokinetics and binding studies on [18F]FEPAD revealed its outstanding specificity and selectivity towards MAGL in brown adipose tissue (BAT) - a tissue that is known to be metabolically active. We employed [18F]FEPAD in PET studies to assess the abundancy of MAGL in BAT deposits of mice and found a remarkable degree of specific tracer binding in the BAT, which was confirmed by post-mortem tissue analysis. Given the negative regulation of endocannabinoids on the metabolic BAT activity, our study supports the concept that dysregulation of MAGL is likely linked to metabolic disorders. Further, we now provide a suitable imaging tool that allows non-invasive assessment of MAGL in BAT deposits, thereby paving the way for detailed mechanistic studies on the role of BAT in endocannabinoid system (ECS)-related pathologies.

The seventh international RASopathies symposium: Pathways to a cure-expanding knowledge, enhancing research, and therapeutic discovery.

RASopathies are a group of genetic disorders that are caused by genes that affect the canonical Ras/mitogen-activated protein kinase (MAPK) signaling pathway. Despite tremendous progress in understanding the molecular consequences of these genetic anomalies, little movement has been made in translating these findings to the clinic. This year, the seventh International RASopathies Symposium focused on expanding the research knowledge that we have gained over the years to enhance new discoveries in the field, ones that we hope can lead to effective therapeutic treatments. Indeed, for the first time, research efforts are finally being translated to the clinic, with compassionate use of Ras/MAPK pathway inhibitors for the treatment of RASopathies. This biannual meeting, organized by the RASopathies Network, brought together basic scientists, clinicians, clinician scientists, patients, advocates, and their families, as well as representatives from pharmaceutical companies and the National Institutes of Health. A history of RASopathy gene discovery, identification of new disease genes, and the latest research, both at the bench and in the clinic, were discussed.

Imaging Autotaxin In Vivo with 18F-Labeled Positron Emission Tomography Ligands.

Autotaxin (ATX) is a secreted phosphodiesterase that has been implicated in a remarkably wide array of pathologies, especially in fibrosis and cancer. While ATX inhibitors have entered the clinical arena, a validated probe for positron emission tomography (PET) is currently lacking. With the aim to develop a suitable ATX-targeted PET radioligand, we have synthesized a focused library of fluorinated imidazo[1,2-a]pyridine derivatives, determined their inhibition constants, and confirmed their binding mode by crystallographic analysis. Based on their promising in vitro properties, compounds 9c, 9f, 9h, and 9j were radiofluorinated. Also, a deuterated analog of [18F]9j, designated as [18F]ATX-1905 ([18F]20), was designed and proved to be highly stable against in vivo radiodefluorination compared with [18F]9c, [18F]9f, [18F]9h, and [18F]9j. These results along with in vitro and in vivo studies toward ATX in a mouse model of LPS-induced liver injury suggest that [18F]ATX-1905 is a suitable PET probe for the non-invasive quantification of ATX.

Novel Reversible-Binding PET Ligands for Imaging Monoacylglycerol Lipase Based on the Piperazinyl Azetidine Scaffold.

Monoacylglycerol lipase (MAGL) is a 33 kDa serine protease primarily responsible for hydrolyzing 2-arachidonoylglycerol into the proinflammatory eicosanoid precursor arachidonic acid in the central nervous system. Inhibition of MAGL constitutes an attractive therapeutic concept for treating psychiatric disorders and neurodegenerative diseases. Herein, we present the design and synthesis of multiple reversible MAGL inhibitor candidates based on a piperazinyl azetidine scaffold. Compounds 10 and 15 were identified as the best-performing reversible MAGL inhibitors by pharmacological evaluations, thus channeling their radiolabeling with fluorine-18 in high radiochemical yields and favorable molar activity. Furthermore, evaluation of [18F]10 and [18F]15 ([18F]MAGL-2102) by autoradiography and positron emission tomography (PET) imaging in rodents and nonhuman primates demonstrated favorable brain uptakes, heterogeneous radioactivity distribution, good specific binding, and adequate brain kinetics, and [18F]15 demonstrated a better performance. In conclusion, [18F]15 was found to be a suitable PET radioligand for the visualization of MAGL, harboring potential for the successful translation into humans.

Development of a triazolobenzodiazepine-based PET probe for subtype-selective vasopressin 1A receptor imaging.

OBJECTIVES: To enable non-invasive real-time quantification of vasopressin 1A (V1A) receptors in peripheral organs, we sought to develop a suitable PET probe that would allow specific and selective V1A receptor imaging in vitro and in vivo. METHODS: We synthesized a high-affinity and -selectivity ligand, designated compound 17. The target structure was labeled with carbon-11 and tested for its utility as a V1A-targeted PET tracer by cell uptake studies, autoradiography, in vivo PET imaging and ex vivo biodistribution experiments. RESULTS: Compound 17 (PF-184563) and the respective precursor for radiolabeling were synthesized in an overall yield of 49% (over 7 steps) and 40% (over 8 steps), respectively. An inhibitory constant of 0.9 nM towards the V1A receptors was measured, while excellent selectivity over the related V1B, V2 and OT receptor (IC50 >10,000 nM) were obtained. Cell uptake studies revealed considerable V1A binding, which was significantly reduced in the presence of V1A antagonists. Conversely, there was no significant blockade in the presence of V1B and V2 antagonists. In vitro autoradiography and PET imaging studies in rodents indicated specific tracer binding mainly in the liver. Further, the pancreas, spleen and the heart exhibited specific binding of [11C]17 ([11C]PF-184563) by ex vivo biodistribution experiments. CONCLUSION: We have developed the first V1A-targeted PET ligand that is suitable for subtype-selective receptor imaging in peripheral organs including the liver, heart, pancreas and spleen. Our findings suggest that [11C]PF-184563 can be a valuable tool to study the role of V1A receptors in liver diseases, as well as in cardiovascular pathologies.

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions.

Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (ai-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the ai-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the ai-QM/MM and semiempirical (se) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the ai-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol-1 Å-1, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference ai-QM/MM results at only a fraction of the computational cost.

Development of a highly-specific 18F-labeled irreversible positron emission tomography tracer for monoacylglycerol lipase mapping.

As a serine hydrolase, monoacylglycerol lipase (MAGL) is principally responsible for the metabolism of 2-arachidonoylglycerol (2-AG) in the central nervous system (CNS), leading to the formation of arachidonic acid (AA). Dysfunction of MAGL has been associated with multiple CNS disorders and symptoms, including neuroinflammation, cognitive impairment, epileptogenesis, nociception and neurodegenerative diseases. Inhibition of MAGL provides a promising therapeutic direction for the treatment of these conditions, and a MAGL positron emission tomography (PET) probe would greatly facilitate preclinical and clinical development of MAGL inhibitors. Herein, we design and synthesize a small library of fluoropyridyl-containing MAGL inhibitor candidates. Pharmacological evaluation of these candidates by activity-based protein profiling identified 14 as a lead compound, which was then radiolabeled with fluorine-18 via a facile SNAr reaction to form 2-[18F]fluoropyridine scaffold. Good blood-brain barrier permeability and high in vivo specific binding was demonstrated for radioligand [18F]14 (also named as [18F]MAGL-1902). This work may serve as a roadmap for clinical translation and further design of potent 18F-labeled MAGL PET tracers.