Identification of HLA-E Binding Mycobacterium tuberculosis-Derived Epitopes through Improved Prediction Models.

Tuberculosis (TB) remains one of the deadliest infectious diseases worldwide, posing great social and economic burden to affected countries. Novel vaccine approaches are needed to increase protective immunity against the causative agent Mycobacterium tuberculosis (Mtb) and to reduce the development of active TB disease in latently infected individuals. Donor-unrestricted T cell responses represent such novel potential vaccine targets. HLA-E-restricted T cell responses have been shown to play an important role in protection against TB and other infections, and recent studies have demonstrated that these cells can be primed in vitro. However, the identification of novel pathogen-derived HLA-E binding peptides presented by infected target cells has been limited by the lack of accurate prediction algorithms for HLA-E binding. In this study, we developed an improved HLA-E binding peptide prediction algorithm and implemented it to identify (to our knowledge) novel Mtb-derived peptides with capacity to induce CD8+ T cell activation and that were recognized by specific HLA-E-restricted T cells in Mycobacterium-exposed humans. Altogether, we present a novel algorithm for the identification of pathogen- or self-derived HLA-E-presented peptides.

Positive allosteric modulation of the mu-opioid receptor produces analgesia with reduced side effects.

Positive allosteric modulators (PAMs) of the mu-opioid receptor (MOR) have been hypothesized as potentially safer analgesics than traditional opioid drugs. This is based on the idea that PAMs will promote the action of endogenous opioid peptides while preserving their temporal and spatial release patterns and so have an improved therapeutic index. However, this hypothesis has never been tested. Here, we show that a mu-PAM, BMS-986122, enhances the ability of the endogenous opioid Methionine-enkephalin (Met-Enk) to stimulate G protein activity in mouse brain homogenates without activity on its own and to enhance G protein activation to a greater extent than ß-arrestin recruitment in Chinese hamster ovary (CHO) cells expressing human mu-opioid receptors. Moreover, BMS-986122 increases the potency of Met-Enk to inhibit GABA release in the periaqueductal gray, an important site for antinociception. We describe in vivo experiments demonstrating that the mu-PAM produces antinociception in mouse models of acute noxious heat pain as well as inflammatory pain. These effects are blocked by MOR antagonists and are consistent with the hypothesis that in vivo mu-PAMs enhance the activity of endogenous opioid peptides. Because BMS-986122 does not bind to the orthosteric site and has no inherent agonist action at endogenously expressed levels of MOR, it produces a reduced level of morphine-like side effects of constipation, reward as measured by conditioned place preference, and respiratory depression. These data provide a rationale for the further exploration of the action and safety of mu-PAMs as an innovative approach to pain management.

NPP-669, a Novel Broad-Spectrum Antiviral Therapeutic with Excellent Cellular Uptake, Antiviral Potency, Oral Bioavailability, Preclinical Efficacy, and a Promising Safety Margin.

DNA viruses are responsible for many diseases in humans. Current treatments are often limited by toxicity, as in the case of cidofovir (CDV, Vistide), a compound used against cytomegalovirus (CMV) and adenovirus (AdV) infections. CDV is a polar molecule with poor bioavailability, and its overall clinical utility is limited by the high occurrence of acute nephrotoxicity. To circumvent these disadvantages, we designed nine CDV prodrug analogues. The prodrugs modulate the polarity of CDV with a long sulfonyl alkyl chain attached to one of the phosphono oxygens. We added capping groups to the end of the alkyl chain to minimize ß-oxidation and focus the metabolism on the phosphoester hydrolysis, thereby tuning the rate of this reaction by altering the alkyl chain length. With these modifications, the prodrugs have excellent aqueous solubility, optimized metabolic stability, increased cellular permeability, and rapid intracellular conversion to the pharmacologically active diphosphate form (CDV-PP). The prodrugs exhibited significantly enhanced antiviral potency against a wide range of DNA viruses in infected human foreskin fibroblasts. Single-dose intravenous and oral pharmacokinetic experiments showed that the compounds maintained plasma and target tissue levels of CDV well above the EC50 for 24 h. These experiments identified a novel lead candidate, NPP-669. NPP-669 demonstrated efficacy against CMV infections in mice and AdV infections in hamsters following oral (p.o.) dosing at a dose of 1 mg/kg BID and 0.1 mg/kg QD, respectively. We further showed that NPP-669 at 30 mg/kg QD did not exhibit histological signs of toxicity in mice or hamsters. These data suggest that NPP-669 is a promising lead candidate for a broad-spectrum antiviral compound.

Serverless Prediction of Peptide Properties with Recurrent Neural Networks.

We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solubility predictor, MahLooL, outperforms the current state-of-the-art methods for short peptides. These models are implemented as a static website without the use of a dedicated server or cloud computing. Web-based models like this allow for accessible and effective reproducibility. Most existing approaches rely on third-party servers that typically require upkeep and maintenance. Our predictive models do not require servers, require no installation of dependencies, and work across a range of devices. The specific architecture is bidirectional recurrent neural networks. This serverless approach is a demonstration of edge machine learning that removes the dependence on cloud providers. The code and models are accessible at https://github.com/ur-whitelab/peptide-dashboard.

Neural potentials of proteins extrapolate beyond training data.

We evaluate neural network (NN) coarse-grained (CG) force fields compared to traditional CG molecular mechanics force fields. We conclude that NN force fields are able to extrapolate and sample from unseen regions of the free energy surface when trained with limited data. Our results come from 88 NN force fields trained on different combinations of clustered free energy surfaces from four protein mapped trajectories. We used a statistical measure named total variation similarity to assess the agreement between reference free energy surfaces from mapped atomistic simulations and CG simulations from trained NN force fields. Our conclusions support the hypothesis that NN CG force fields trained with samples from one region of the proteins' free energy surface can, indeed, extrapolate to unseen regions. Additionally, the force matching error was found to only be weakly correlated with a force field's ability to reconstruct the correct free energy surface.

Investigating Active Learning and Meta-Learning for Iterative Peptide Design.

Often the development of novel functional peptides is not amenable to high throughput or purely computational screening methods. Peptides must be synthesized one at a time in a process that does not generate large amounts of data. One way this method can be improved is by ensuring that each experiment provides the best improvement in both peptide properties and predictive modeling accuracy. Here, we study the effectiveness of active learning, optimizing experiment order, and meta-learning, transferring knowledge between contexts, to reduce the number of experiments necessary to build a predictive model. We present a multitask benchmark database of peptides designed to advance these methods for experimental design. Each task is a binary classification of peptides represented as a sequence string. We find neither active learning method tested to be better than random choice. The meta-learning method Reptile was found to improve the average accuracy across data sets. Combining meta-learning with active learning offers inconsistent benefits.

Synthesis and structure-activity relationships of thieno[2,3-d]pyrimidines as atypical protein kinase C inhibitors to control retinal vascular permeability and cytokine-induced edema.

Studies demonstrate that small molecule targeting of atypical protein kinase C (aPKC) may provide an effective means to control vascular permeability, prevent edema, and reduce inflammation providing novel and important alternatives to anti-VEGF therapies for certain blinding eye diseases. Based on a literature tricyclic thieno[2,3-d]pyrimidine lead (1), an ATP-competitive inhibitor of the aPKC iota (ι) and aPKC zeta (ζ) isoforms, we have synthesized a small series of compounds in 1-2 steps from a readily available chloro intermediate. A single pyridine congener was also made using 2D NMR to assign regiochemistry. Within the parent pyrimidine series, a range of potencies was observed against aPKCζ whereas the pyridine congener was inactive. Selected compounds were also tested for their effect toward VEGF-induced permeability in BREC cells. The most potent of these (7l) was further assayed against the aPKCι isoform and showed a favorable selectivity profile against a panel of 31 kinases, including kinases from the AGC superfamily, with a focus on PKC isoforms and kinases previously shown to affect permeability. Further testing of 7l in a luciferase assay in HEK293 cells showed an ability to prevent TNF-α induced NFκB activation while not having any effect on cell survival. Intravitreal administration of 7l to the eye yielded a complete reduction in permeability in a test to determine whether the compound could block VEGF- and TNFα-induced permeability across the retinal vasculature in a rat model. The compound in mice displayed good microsomal stability and in plasma moderate exposure (AUC and Cmax), low clearance, a long half-life and high oral bioavailability. With IV dosing, higher levels were observed in the brain and eye relative to plasma, with highest levels in the eye by either IV or PO dosing. With a slow oral absorption profile, 7l accumulates in the eye to maintain a high concentration after dosing with higher levels than in plasma. Compound 7l may represent a class of aPKC inhibitors for further investigation.

Is preservation of symmetry necessary for coarse-graining?

There is a need for theory on how to group atoms in a molecule to define a coarse-grained (CG) mapping. This article investigates the importance of preserving symmetry of the underlying molecular graph of a given molecule when choosing a CG mapping. 26 CG models of seven alkanes with three different CG techniques were examined. We unexpectedly find preserving symmetry has no consistent effect on CG model accuracy regardless of CG method or comparison metric.

Multiscale Kinetic Modeling Reveals an Ensemble of Cl-/H+ Exchange Pathways in ClC-ec1 Antiporter.

Despite several years of research, the ion exchange mechanisms in chloride/proton antiporters and many other coupled transporters are not yet understood at the molecular level. Here, we present a novel approach to kinetic modeling and apply it to ion exchange in ClC-ec1. Our multiscale kinetic model is developed by (1) calculating the state-to-state rate coefficients with reactive and polarizable molecular dynamics simulations, (2) optimizing these rates in a global kinetic network, and (3) predicting new electrophysiological results. The model shows that the robust Cl:H exchange ratio (2.2:1) can indeed arise from kinetic coupling without large protein conformational changes, indicating a possible facile evolutionary connection to chloride channels. The E148 amino acid residue is shown to couple chloride and proton transport through protonation-dependent blockage of the central anion binding site and an anion-dependent pKa value, which influences proton transport. The results demonstrate how an ensemble of different exchange pathways, as opposed to a single series of transitions, culminates in the macroscopic observables of the antiporter, such as transport rates, chloride/proton stoichiometry, and pH dependence.

Encoding and selecting coarse-grain mapping operators with hierarchical graphs.

Coarse-grained (CG) molecular dynamics (MD) can simulate systems inaccessible to fine-grained (FG) MD simulations. A CG simulation decreases the degrees of freedom by mapping atoms from an FG representation into agglomerate CG particles. The FG to CG mapping is not unique. Research into systematic selection of these mappings is challenging due to their combinatorial growth with respect to the number of atoms in a molecule. Here we present a method of reducing the total count of mappings by imposing molecular topology and symmetry constraints. The count reduction is illustrated by considering all mappings for nearly 50 000 molecules. The resulting number of mapping operators is still large, so we introduce a novel hierarchical graphical approach which encodes multiple CG mapping operators. The encoding method is demonstrated for methanol and a 14-mer peptide. With the test cases, we show how the encoding can be used for automated selection of reasonable CG mapping operators.

Communication: Improved ab initio molecular dynamics by minimally biasing with experimental data.

Accounting for electrons and nuclei simultaneously is a powerful capability of ab initio molecular dynamics (AIMD). However, AIMD is often unable to accurately reproduce properties of systems such as water due to inaccuracies in the underlying electronic density functionals. This shortcoming is often addressed by added empirical corrections and/or increasing the simulation temperature. We present here a maximum-entropy approach to directly incorporate limited experimental data via a minimal bias. Biased AIMD simulations of water and an excess proton in water are shown to give significantly improved properties both for observables which were biased to match experimental data and for unbiased observables. This approach also yields new physical insight into inaccuracies in the underlying density functional theory as utilized in the unbiased AIMD.

Analysis of international competition and training in men's field hockey by global positioning system and inertial sensor technology.

This study assessed the relative demands of elite field hockey training and competition to determine whether familiar exercise prescription strategies provide an appropriate training stimulus. Sixteen elite male field hockey players (age, 25 ± 4 years; body mass, 70.9 ± 6.6 kg; and maximal oxygen consumption, 61.0 ± 2.1 ml·kg·min [mean ± SD]) participated in the study. Seventy-five elite level competition and 37 training analyses from 8 games and 4 training sessions were obtained. Training duration was longer than competition and covered a greater total distance (109 ± 2.5 vs. 74 ± 0.3 minutes and 7318 ± 221 vs. 5868 ± 75 m; p < 0.001 in both). The distance covered sprinting and running at high intensity was not different between training and competition (114 ± 6 vs. 116 ± 9 m when sprinting and 457 ± 6 vs. 448 ± 7 m for high-intensity running). More high-intensity accelerations were performed during training than in competition (37 ± 3 vs. 20 ± 2). Despite having lower predicted aerobic capacity and covering less distance in competition than in some previous studies, these data support the suggestion that it is high-intensity activity that differentiates international level competition and further suggests that international players can replicate the intensity of competition during small-sided games.

Biologically inspired stealth peptide-capped gold nanoparticles.

Introduction into the human body makes most nanoparticle systems susceptible to aggregation via nonspecific protein binding. Here, we developed a peptide-capped gold nanoparticle platform that withstands aggregation in undiluted human serum at 37 °C for 24 h. This biocompatible and natural system is based on mimicking human proteins which are enriched in negatively charged glutamic acid and positively charged lysine residues on their surface. The multifunctional EKEKEKE-PPPPC-Am peptide sequence consists of a stealth glutamic acid/lysine portion combined with a surface anchoring linker containing four prolines and a cysteine. Particle stability was measured via optical spectroscopy and dynamic light scattering in single protein, high salt, and undiluted human serum solutions. In vitro cell experiments demonstrate EKEKEKE-PPPPC-Am capped gold nanoparticles effectively minimize nonspecific cell uptake by nonphagocytic bovine aortic endothelial cells and phagocytic murine macrophage RAW 264.7 cells. Cytotoxicity studies show that peptide-capped gold nanoparticles do not affect cell viability. Finally, the peptide EKEKEKE-PPPPC-Am was extended with cyclic RGD to demonstrate specific cell targeting and stealth without using poly(ethylene glycol). Adding the functional peptide via peptide sequence extension avoids complex conjugation chemistries that are used for connection to synthetic materials. Inductively coupled plasma mass spectroscopy results indicate high aortic bovine endothelial cell uptake of c[RGDfE(SGG-KEKEKE-PPPPC-Am)] capped gold nanoparticles and low uptake of the control scrambled sequence c[RDGfE(SGG-KEKEKE-PPPPC-Am)] capped gold nanoparticles.

cis-1-Oxo-heterocyclyl-4-amido cyclohexane derivatives as NPY5 receptor antagonists.

The NPY5 receptor binding and pharmacokinetic properties of a novel series of cis-1-oxo-heterocyclyl-4-amido-cyclohexane derivatives are described.

Learning peptide properties with positive examples only.

Deep learning can create accurate predictive models by exploiting existing large-scale experimental data, and guide the design of molecules. However, a major barrier is the requirement of both positive and negative examples in the classical supervised learning frameworks. Notably, most peptide databases come with missing information and low number of observations on negative examples, as such sequences are hard to obtain using high-throughput screening methods. To address this challenge, we solely exploit the limited known positive examples in a semi-supervised setting, and discover peptide sequences that are likely to map to certain antimicrobial properties via positive-unlabeled learning (PU). In particular, we use the two learning strategies of adapting base classifier and reliable negative identification to build deep learning models for inferring solubility, hemolysis, binding against SHP-2, and non-fouling activity of peptides, given their sequence. We evaluate the predictive performance of our PU learning method and show that by only using the positive data, it can achieve competitive performance when compared with the classical positive-negative (PN) classification approach, where there is access to both positive and negative examples.

Correction: Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.

[This corrects the article DOI: 10.1039/D3DD00217A.].

Predicting small molecules solubility on endpoint devices using deep ensemble neural networks.

Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. Data-driven approaches, such as deep learning, offer improved accuracy and computational efficiency but typically lack uncertainty quantification. Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods. In this work, we addressed these problems with a deep learning model with predictive uncertainty that runs on a static website (without a server). This approach moves computing needs onto the website visitor without requiring installation, removing the need to pay for and maintain servers. Our model achieves satisfactory results in solubility prediction. Furthermore, we demonstrate how to create molecular property prediction models that balance uncertainty and ease of use. The code is available at https://github.com/ur-whitelab/mol.dev, and the model is useable at https://mol.dev.

Augmenting large language models with chemistry tools.

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. Our agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chromophore. Our evaluation, including both LLM and expert assessments, demonstrates ChemCrow's effectiveness in automating a diverse set of chemical tasks. Our work not only aids expert chemists and lowers barriers for non-experts but also fosters scientific advancement by bridging the gap between experimental and computational chemistry.

Purpose reflection benefits minoritized students' motivation and well-being in STEM.

Students from groups historically excluded from STEM face heightened challenges to thriving and advancing in STEM. Prompting students to reflect on these challenges in light of their purpose can yield benefits by helping students see how their STEM work connects to fundamental motives. We conducted a randomized, controlled trial to test potential benefits of reflecting on purpose-their "why" for pursuing their degrees. This multimethod study included 466 STEM students (232 women; 237 Black/Latinx/Native students). Participants wrote about their challenges in STEM, with half randomly assigned to consider these in light of their purpose. Purpose reflection fostered benefits to beliefs and attitudes about the major, authentic belonging, and stress appraisals. Effects were robust across race and gender identities or larger for minoritized students. Structural and cultural shifts to recognize students' purpose in STEM can provide a clearer pathway for students to advance.

Low-dose M.tb infection but not BCG or MTBVAC vaccination enhances heterologous antibody titres in non-human primates.

Introduction: Mycobacteria are known to exert a range of heterologous effects on the immune system. The mycobacteria-based Freund's Complete Adjuvant is a potent non-specific stimulator of the immune response used in immunization protocols promoting antibody production, and Mycobacterium bovis Bacille Calmette Guérin (BCG) vaccination has been linked with decreased morbidity and mortality beyond the specific protection it provides against tuberculosis (TB) in some populations and age groups. The role of heterologous antibodies in this phenomenon, if any, remains unclear and under-studied. Methods: We set out to evaluate antibody responses to a range of unrelated pathogens following infection with Mycobacterium tuberculosis (M.tb) and vaccination with BCG or a candidate TB vaccine, MTBVAC, in non-human primates. Results: We demonstrate a significant increase in the titer of antibodies against SARS-CoV-2, cytomegalovirus, Epstein-Barr virus, tetanus toxoid, and respiratory syncytial virus antigens following low-dose aerosol infection with M.tb. The magnitude of some of these responses correlated with TB disease severity. However, vaccination with BCG administered by the intradermal, intravenous or aerosol routes, or intradermal delivery of MTBVAC, did not increase antibody responses against unrelated pathogens. Discussion: Our findings suggest that it is unlikely that heterologous antibodies contribute to the non-specific effects of these vaccines. The apparent dysregulation of B cell responses associated with TB disease warrants further investigation, with potential implications for risk of B cell cancers and novel therapeutic strategies.