On the Validation of Protein Force Fields Based on Structural Criteria.

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, J-coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small. Furthermore, improvements in agreement in one metric were often offset by loss of agreement in another. The work establishes a framework and test set against which protein force fields can be validated. It also highlights the danger of inferring the relative quality of a given force field based on a small range of structural properties or small number of proteins.

Injury patterns and healthcare utilisation by runners of the New York City Marathon.

Objectives: The purpose of this study was to describe injury patterns and healthcare utilisation of marathon runners. Methods: This was a previously reported 16-week prospective observational study of runners training for the New York City Marathon. Runners completed a baseline survey including demographics, running experience and marathon goal. Injury surveys were collected every 4 weeks during training, as well as 1 week before and 1 week after the race. Injury details collected included anatomic location, diagnosis, onset, and treatment received. Results: A total of 1049 runners were enrolled. Injuries were reported by 398 (38.4%) during training and 128 (14.1%) during the marathon. The overall prevalence of injury was 447/1049 (42.6%). Foot, knee and hip injuries were most common during training, whereas knee, thigh and foot injuries were most common during the race. The most frequent tissue type affected was the category of muscle, tendon/fascia and bursa. The prevalence of overuse injuries increased, while acute injuries remained constant throughout training. Hamstring injuries had the highest prevalence of diagnosis with 38/564 injuries (6.7%). Of the 447 runners who reported an injury, 224 (50.1%) received medical care. Physical therapy was the most common medical care received with 115/1037 (11.1%) runners during training and 44/907 (4.9%) postrace. Conclusion: Runners training and participating in a marathon commonly experience injuries, especially of the foot and knee, which often are overuse soft tissue injuries. Half of the injured runners sought out medical care for their injury. Understanding the patterns of injuries affecting marathon runners could help guide future injury prevention efforts.

Trends in inequalities in avoidable hospitalisations across the COVID-19 pandemic: a cohort study of 23.5 million people in England.

OBJECTIVE: To determine whether periods of disruption were associated with increased 'avoidable' hospital admissions and wider social inequalities in England. DESIGN: Observational repeated cross-sectional study. SETTING: England (January 2019 to March 2022). PARTICIPANTS: With the approval of NHS England we used individual-level electronic health records from OpenSAFELY, which covered ~40% of general practices in England (mean monthly population size 23.5 million people). PRIMARY AND SECONDARY OUTCOME MEASURES: We estimated crude and directly age-standardised rates for potentially preventable unplanned hospital admissions: ambulatory care sensitive conditions and urgent emergency sensitive conditions. We considered how trends in these outcomes varied by three measures of social and spatial inequality: neighbourhood socioeconomic deprivation, ethnicity and geographical region. RESULTS: There were large declines in avoidable hospitalisations during the first national lockdown (March to May 2020). Trends increased post-lockdown but never reached 2019 levels. The exception to these trends was for vaccine-preventable ambulatory care sensitive admissions which remained low throughout 2020-2021. While trends were consistent by each measure of inequality, absolute levels of inequalities narrowed across levels of neighbourhood socioeconomic deprivation, Asian ethnicity (compared with white ethnicity) and geographical region (especially in northern regions). CONCLUSIONS: We found no evidence that periods of healthcare disruption from the COVID-19 pandemic resulted in more avoidable hospitalisations. Falling avoidable hospital admissions has coincided with declining inequalities most strongly by level of deprivation, but also for Asian ethnic groups and northern regions of England.

Molecular Insights into the Dynamics of Amyloid Fibril Growth: Elongation and Lateral Assembly of GNNQQNY Protofibrils.

The self-assembly of peptides and proteins into ß-sheet rich amyloid fibrils is linked to both functional and pathological states. In this study, the growth of fibrillar structures of the short peptide GNNQQNY, a fragment from the yeast prion Sup35 protein, was examined. Molecular dynamics simulations were used to study alternative mechanisms of fibril growth, including elongation through binding of monomers as well as fibril self-assembly into larger, more mature structures. It was found that after binding, monomers diffused along preformed fibrils toward the ends, supporting the mechanism of fibril growth via elongation. Lateral assembly of protofibrils was found to occur readily, suggesting that this could be the key to transitioning from isolated fibrils to mature multilayer structures. Overall, the work provides mechanistic insights into the competitive pathways that govern amyloid fibril growth.

Facilitating the structural characterisation of non-canonical amino acids in biomolecular NMR.

Peptides and proteins containing non-canonical amino acids (ncAAs) are a large and important class of biopolymers. They include non-ribosomally synthesised peptides, post-translationally modified proteins, expressed or synthesised proteins containing unnatural amino acids, and peptides and proteins that are chemically modified. Here, we describe a general procedure for generating atomic descriptions required to incorporate ncAAs within popular NMR structure determination software such as CYANA, CNS, Xplor-NIH and ARIA. This procedure is made publicly available via the existing Automated Topology Builder (ATB) server (https://atb.uq.edu.au, last access: 17 February 2023) with all submitted ncAAs stored in a dedicated database. The described procedure also includes a general method for linking of side chains of amino acids from CYANA templates. To ensure compatibility with other systems, atom names comply with IUPAC guidelines. In addition to describing the workflow, 3D models of complex natural products generated by CYANA are presented, including vancomycin. In order to demonstrate the manner in which the templates for ncAAs generated by the ATB can be used in practice, we use a combination of CYANA and CNS to solve the structure of a synthetic peptide designed to disrupt Alzheimer-related protein-protein interactions. Automating the generation of structural templates for ncAAs will extend the utility of NMR spectroscopy to studies of more complex biomolecules, with applications in the rapidly growing fields of synthetic biology and chemical biology. The procedures we outline can also be used to standardise the creation of structural templates for any amino acid and thus have the potential to impact structural biology more generally.

Engineering Transferable Atomic Force Fields: Empirical Optimization of Hydrocarbon Lennard-Jones Interactions by Direct Mapping of Parameter Space.

The utility of atomistic simulations depends on the accuracy of the force field used to represent the potential energy landscape, the consistency with which interaction parameters can be assigned, and the extent to which parameters can be transferred between chemical entities. Here, parameter space mapping, a simple and robust procedure for atom typing (parameter assignment) and parameter optimization, is used to identify a minimal set of parameters capable of simultaneously reproducing the density, heat of vaporization, and solvation free energies for a targeted set of simple hydrocarbons. Using an atom-centered fixed charge model and a 6-12 Lennard-Jones potential, the experimental densities and the heats of vaporization for 22 hydrocarbons (linear, cyclic, and aromatic) could be predicted with high precision: average unsigned error (AUE) of 6.1 kg/m3 and 0.5 kJ/mol, respectively, and R2 values of 0.991 and 0.999, respectively. For the 17 compounds with experimental solvation free energy values in water, the AUE was 1.3 kJ/mol, and the slope and R2 for the line of best fit were 0.968 and 0.991, respectively. A key element in ensuring transferability in this work was minimizing confounding variables by ensuring that the calculation of observables was independent of the precise choice of simulation settings (cutoff, bond constraints, etc.) and the explicit consideration of correlations between parameters.

OFraMP: a fragment-based tool to facilitate the parametrization of large molecules.

An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. OFraMP applied to paclitaxel (ATB ID 35922).

Impact of osmotic stress on production, morphology, and fitness of Beauveria bassiana blastospores.

Beauveria bassiana is a cosmopolitan entomopathogenic fungus that can infect over 1000 insect species. During growth inside the host, B. bassiana transitions from hyphal to yeast-like unicellular growth as blastospores. Blastospores are well suited as an active ingredient in biopesticides due to their ease of production by liquid fermentation. Herein, we investigated the impact of hyperosmotic growth environments mediated by ionic and non-ionic osmolytes on two strains of B. bassiana (ESALQ1432 and GHA) relevant to growth morphology, blastospore production, desiccation tolerance, and insecticidal activity. Polyethylene glycol (PEG200) increased osmotic pressure in submerged cultures leading to decreased blastospore size but higher blastospore yields for one strain. Morphologically, decreased blastospore size was linked to increased osmotic pressure. However, smaller blastospores from PEG200 supplemented cultures after air-drying exhibited delayed germination. Ionic osmolytes (NaCl and KCl) generated the same osmotic pressure (2.5-2.7 MPa) as 20% glucose and boosted blastospore yields (> 2.0 × 109 blastospores mL-1). Fermentation performed in a bench-scale bioreactor consistently promoted high blastospore yields when using NaCl (2.5 MPa) amended media within 3 days. Mealworm larvae (Tenebrio molitor) were similarly susceptible to NaCl-grown blastospores and aerial conidia in a dose-time-dependent manner. Collectively, these results demonstrate the use of hyperosmotic liquid culture media in triggering enhanced yeast-like growth by B. bassiana. Understanding the role of osmotic pressure on blastospore formation and fitness will hasten the development of viable commercial fungal biopesticides. KEY POINTS: • Osmotic pressure plays a critical role in submerged fermentation of B. bassiana. • Ionic/non-ionic osmolytes greatly impact blastospore morphology, fitness, and yield. • Desiccation tolerance and bioefficacy of blastospores are affected by the osmolyte.

Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes.

BACKGROUND: Unplanned hospital readmissions after total joint arthroplasty (TJA) represent potentially serious adverse events and remain a critical measure of hospital quality. Predicting the risk of readmission after TJA may provide patients and clinicians with valuable information for preoperative decision-making. QUESTIONS/PURPOSES: (1) Can nonlinear machine-learning models integrating preoperatively available patient, surgeon, hospital, and county-level information predict 30-day unplanned hospital readmissions in a large cohort of nationwide Medicare beneficiaries undergoing TJA? (2) Which predictors are the most important in predicting 30-day unplanned hospital readmissions? (3) What specific information regarding population-level associations can we obtain from interpreting partial dependency plots (plots describing, given our modeling choice, the potentially nonlinear shape of associations between predictors and readmissions) of the most important predictors of 30-day readmission? METHODS: National Medicare claims data (chosen because this database represents a large proportion of patients undergoing TJA annually) were analyzed for patients undergoing inpatient TJA between October 2016 and September 2018. A total of 679,041 TJAs (239,391 THAs [61.3% women, 91.9% White, 52.6% between 70 and 79 years old] and 439,650 TKAs [63.3% women, 90% White, 55.2% between 70 and 79 years old]) were included. Model features included demographics, county-level social determinants of health, prior-year (365-day) hospital and surgeon TJA procedure volumes, and clinical classification software-refined diagnosis and procedure categories summarizing each patient's Medicare claims 365 days before TJA. Machine-learning models, namely generalized additive models with pairwise interactions (prediction models consisting of both univariate predictions and pairwise interaction terms that allow for nonlinear effects), were trained and evaluated for predictive performance using area under the receiver operating characteristic (AUROC; 1.0 = perfect discrimination, 0.5 = no better than random chance) and precision-recall curves (AUPRC; equivalent to the average positive predictive value, which does not give credit for guessing "no readmission" when this is true most of the time, interpretable relative to the base rate of readmissions) on two holdout samples. All admissions (except the last 2 months' worth) were collected and split randomly 80%/20%. The training cohort was formed with the random 80% sample, which was downsampled (so it included all readmissions and a random, equal number of nonreadmissions). The random 20% sample served as the first test cohort ("random holdout"). The last 2 months of admissions (originally held aside) served as the second test cohort ("2-month holdout"). Finally, feature importances (the degree to which each variable contributed to the predictions) and partial dependency plots were investigated to answer the second and third research questions. RESULTS: For the random holdout sample, model performance values in terms of AUROC and AUPRC were 0.65 and 0.087, respectively, for THA and 0.66 and 0.077, respectively, for TKA. For the 2-month holdout sample, these numbers were 0.66 and 0.087 and 0.65 and 0.075. Thus, our nonlinear models incorporating a wide variety of preoperative features from Medicare claims data could not well-predict the individual likelihood of readmissions (that is, the models performed poorly and are not appropriate for clinical use). The most predictive features (in terms of mean absolute scores) and their partial dependency graphs still confer information about population-level associations with increased risk of readmission, namely with older patient age, low prior 365-day surgeon and hospital TJA procedure volumes, being a man, patient history of cardiac diagnoses and lack of oncologic diagnoses, and higher county-level rates of hospitalizations for ambulatory-care sensitive conditions. Further inspection of partial dependency plots revealed nonlinear population-level associations specifically for surgeon and hospital procedure volumes. The readmission risk for THA and TKA decreased as surgeons performed more procedures in the prior 365 days, up to approximately 75 TJAs (odds ratio [OR] = 1.2 for TKA and 1.3 for THA), but no further risk reduction was observed for higher annual surgeon procedure volumes. For THA, the readmission risk decreased as hospitals performed more procedures, up to approximately 600 TJAs (OR = 1.2), but no further risk reduction was observed for higher annual hospital procedure volumes. CONCLUSION: A large dataset of Medicare claims and machine learning were inadequate to provide a clinically useful individual prediction model for 30-day unplanned readmissions after TKA or THA, suggesting that other factors that are not routinely collected in claims databases are needed for predicting readmissions. Nonlinear population-level associations between low surgeon and hospital procedure volumes and increased readmission risk were identified, including specific volume thresholds above which the readmission risk no longer decreases, which may still be indirectly clinically useful in guiding policy as well as patient decision-making when selecting a hospital or surgeon for treatment. LEVEL OF EVIDENCE: Level III, therapeutic study.

An In-Depth Analysis of Providers and Services of Cancellation in Anesthesia Reveals a Complex Picture after Systemic Analysis.

The variances in operating room (OR) cancellation rates between different service lines and operators within these service lines were assessed by reviewing the electronic medical record (EMR) covering 34,561 cases performed by 199 OR operators in 2018. We assumed that cancellations would differ between different service lines, but the between-operators variance was minimal within the service line. We hypothesized that most variability would be secondary to patient-specific (weekdays, time of year, and national holidays), seasonal and administrative issues. Of 4165 case cancellations, the majority (73.1%) occurred before the patient arrived at the hospital. A total of 60% of all cancellations were within gastroenterology, interventional cardiology, and orthopedics. Cancellation rate variability between surgeons operating within the same service line greatly varied between services from very homogenous to very diverse across providers. The top reasons for cancellation were: date change, canceled by a patient, or "no show". The highest cancellation rates occurred on Mondays and Tuesdays, in January and September, and during weeks associated with national holidays. In summary, cancellation variability must be analyzed at the level of individual specialties, operators, and time variability.

A molecular device for the redox quality control of GroEL/ES substrates.

Hsp60 chaperonins and their Hsp10 cofactors assist protein folding in all living cells, constituting the paradigmatic example of molecular chaperones. Despite extensive investigations of their structure and mechanism, crucial questions regarding how these chaperonins promote folding remain unsolved. Here, we report that the bacterial Hsp60 chaperonin GroEL forms a stable, functionally relevant complex with the chaperedoxin CnoX, a protein combining a chaperone and a redox function. Binding of GroES (Hsp10 cofactor) to GroEL induces CnoX release. Cryoelectron microscopy provided crucial structural information on the GroEL-CnoX complex, showing that CnoX binds GroEL outside the substrate-binding site via a highly conserved C-terminal α-helix. Furthermore, we identified complexes in which CnoX, bound to GroEL, forms mixed disulfides with GroEL substrates, indicating that CnoX likely functions as a redox quality-control plugin for GroEL. Proteins sharing structural features with CnoX exist in eukaryotes, suggesting that Hsp60 molecular plugins have been conserved through evolution.

PyThinFilm: Automated Molecular Dynamics Simulation Protocols for the Generation of Thin Film Morphologies.

The performance of organic optoelectronic devices, such as organic light-emitting diodes (OLEDs) and organic solar cells (OSCs), is intrinsically related to the molecular-scale morphology of the thin films from which they are composed. However, the experimental characterization of morphology at the molecular level is challenging due to the often amorphous or at best semicrystalline nature of these films. Classical molecular modeling techniques, such as molecular dynamics (MD) simulation, are increasingly used to understand the relationship between morphology and the properties of thin-film devices. PyThinFilm (github.com/ATB-UQ/PyThinFilm) is an open-source Python package which allows fully automated MD simulations of thin film growth to be performed using vacuum and/or solution deposition processes. PyThinFilm utilizes the GROMACS simulation package in combination with interaction parameters from the Automated Topology Builder (atb.uq.edu.au). Here, PyThinFilm is described along with an overview of applications in which PyThinFilm has been used to study the thin films of organic semiconductor materials typically used in OLEDs and OSCs.

Pitching mechanics and performance of adult baseball pitchers: A systematic review and meta-analysis for normative data.

OBJECTIVES: The aim of this systematic review was to critically review and synthesise the findings from primary studies on pitching mechanics and performance of healthy adult baseball pitchers. DESIGN: Systematic review with meta-analysis. METHODS: Eight English- and Japanese-language databases were systematically searched from inception to 22nd July 2022. RESULTS: In total, 29 descriptive biomechanical studies were included. Overall study quality was moderate. In subgroup analyses, professional pitchers showed significantly higher stride length, peak shoulder internal rotation velocity, peak shoulder proximal force and ball velocity compared to collegiate pitchers. Conversely, collegiate pitchers were found to have significantly higher peak pelvis rotation velocity. CONCLUSIONS: Available normative data suggested potentially heterogeneous pitching mechanics and performance between professional and collegiate pitchers. However, the findings in this review should be interpreted cautiously. Since statistical heterogeneity was significant within most data sets, more detailed subgroup analyses are required. Additionally, more high-quality studies utilising measurement systems with established reliability are required to obtain accurate data in baseball pitching mechanics and performance.

Ethanol sustains phosphorylated tau protein in the cultured neonatal rat hippocampus: Implications for fetal alcohol spectrum disorders.

Fetal Alcohol Spectrum Disorders (FASDs) are comprised of developmental, behavioral, and cognitive abnormalities caused by prenatal alcohol exposure, affecting an estimated 2%-5% of children and costing $4 billion annually in the United States. While some behavioral therapies help, the neurobiological mechanisms that underpin FASDs need further elucidation for development of effective pharmacotherapeutics. The role of the tau protein in the hippocampus is likely to be involved. Tau catalyzes microtubule polymerization in developing neurons. However, this function can become disrupted by hyperphosphorylation. Many of the cognitive deficits observed in neurodegenerative tauopathies overlap to some degree with what is observed in juvenile developmental disabilities, such as FASDs (e.g., selective memory, executive dysfunction). Thus, tau protein phosphorylation may be one important mechanism of dysfunction in FASDs. The purpose of this study is to provide an empirical basis for a tauopathic characterization of FASDs. To do so, hippocampal slices were extracted from rats at postnatal day 10 (PND10); hippocampal slices were then exposed to 5 days of 50-mM ethanol between 6 days in vitro (DIV) and 11DIV. Immunoblots were taken for Total and p-Tau (Threonine231) at 12DIV and 24DIV. Immunohistochemical fluorescent images were taken for p-Tau (Threonine231) at 12DIV and 24DIV. Separate p-Tau measures were taken for the cornu ammonis 1 (CA1), CA3, and dentate gyrus (DG). Total Tau protein expression remained unchanged between 12DIV and 24DIV regardless of ethanol condition. In the control group, longer DIV was associated with decreased p-Tau. However, in the ethanol-exposed group, p-Tau was sustained across DIV. This is the first study to show that ethanol exposure sustains tau Threonine231 phosphorylation in the perinatal hippocampus regardless of Total Tau expression. These findings could lead to innovative pharmacotherapeutic targets for the treatment of cognitive deficits seen in FASDs.

Understanding the performance differences between solution and vacuum deposited OLEDs: A computational approach.

Solution-processing of organic light-emitting diode films has potential advantages in terms of cost and scalability over vacuum-deposition for large area applications. However, solution processed small molecule films can have lower overall device performance. Here, novel molecular dynamics techniques are developed to enable faster simulation of solvent evaporation that occurs during solution processing and give films of thicknesses relevant to real devices. All-atom molecular dynamics simulations are then used in combination with kinetic Monte Carlo transport modeling to examine how differences in morphology stemming from solution or vacuum film deposition affect charge transport and exciton dynamics in films consisting of light-emitting bis(2-phenylpyridine)(acetylacetonate)iridium(III) [Ir(ppy)2(acac)] guest molecules in a 4,4'-bis(N-carbazolyl)biphenyl host. While the structures of the films deposited from vacuum and solution were found to differ, critically, only minor variations in the transport properties were predicted by the simulations even if trapped solvent was present.

Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.

Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.

Modelling of the dynamic polarizability of macromolecules for single-molecule optical biosensing.

The structural dynamics of macromolecules is important for most microbiological processes, from protein folding to the origins of neurodegenerative disorders. Noninvasive measurements of these dynamics are highly challenging. Recently, optical sensors have been shown to allow noninvasive time-resolved measurements of the dynamic polarizability of single-molecules. Here we introduce a method to efficiently predict the dynamic polarizability from the atomic configuration of a given macromolecule. This provides a means to connect the measured dynamic polarizability to the underlying structure of the molecule, and therefore to connect temporal measurements to structural dynamics. To illustrate the methodology we calculate the change in polarizability as a function of time based on conformations extracted from molecular dynamics simulations and using different conformations of motor proteins solved crystalographically. This allows us to quantify the magnitude of the changes in polarizablity due to thermal and functional motions.