Predicting multiple conformations via sequence clustering and AlphaFold2.

AlphaFold2 (ref. 1) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein's biological function often depends on multiple conformational substates2, and disease-causing point mutations often cause population changes within these substates3,4. We demonstrate that clustering a multiple-sequence alignment by sequence similarity enables AlphaFold2 to sample alternative states of known metamorphic proteins with high confidence. Using this method, named AF-Cluster, we investigated the evolutionary distribution of predicted structures for the metamorphic protein KaiB5 and found that predictions of both conformations were distributed in clusters across the KaiB family. We used nuclear magnetic resonance spectroscopy to confirm an AF-Cluster prediction: a cyanobacteria KaiB variant is stabilized in the opposite state compared with the more widely studied variant. To test AF-Cluster's sensitivity to point mutations, we designed and experimentally verified a set of three mutations predicted to flip KaiB from Rhodobacter sphaeroides from the ground to the fold-switched state. Finally, screening for alternative states in protein families without known fold switching identified a putative alternative state for the oxidoreductase Mpt53 in Mycobacterium tuberculosis. Further development of such bioinformatic methods in tandem with experiments will probably have a considerable impact on predicting protein energy landscapes, essential for illuminating biological function.

RNA secondary structure packages evaluated and improved by high-throughput experiments.

Despite the popularity of computer-aided study and design of RNA molecules, little is known about the accuracy of commonly used structure modeling packages in tasks sensitive to ensemble properties of RNA. Here, we demonstrate that the EternaBench dataset, a set of more than 20,000 synthetic RNA constructs designed on the RNA design platform Eterna, provides incisive discriminative power in evaluating current packages in ensemble-oriented structure prediction tasks. We find that CONTRAfold and RNAsoft, packages with parameters derived through statistical learning, achieve consistently higher accuracy than more widely used packages in their standard settings, which derive parameters primarily from thermodynamic experiments. We hypothesized that training a multitask model with the varied data types in EternaBench might improve inference on ensemble-based prediction tasks. Indeed, the resulting model, named EternaFold, demonstrated improved performance that generalizes to diverse external datasets including complete messenger RNAs, viral genomes probed in human cells and synthetic designs modeling mRNA vaccines.

Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular switches.

Internet-based scientific communities promise a means to apply distributed, diverse human intelligence toward previously intractable scientific problems. However, current implementations have not allowed communities to propose experiments to test all emerging hypotheses at scale or to modify hypotheses in response to experiments. We report high-throughput methods for molecular characterization of nucleic acids that enable the large-scale video game­based crowdsourcing of RNA sensor design, followed by high-throughput functional characterization. Iterative design testing of thousands of crowdsourced RNA sensor designs produced near­thermodynamically optimal and reversible RNA switches that act as self-contained molecular sensors and couple five distinct small molecule inputs to three distinct protein binding and fluorogenic outputs. This work suggests a paradigm for widely distributed experimental bioscience.

ICD-11 post-traumatic stress disorder and complex post-traumatic stress disorder in mental health support-seeking former-serving Australian defence force veterans.

BACKGROUND: ICD-11 complex post-traumatic stress disorder is a more severe condition than post-traumatic stress disorder, and recent studies indicate it is more prevalent among military samples. In this study, we tested the psychometric properties of the International Trauma Questionnaire, assessed the relative prevalence rates of post-traumatic stress disorder and complex post-traumatic stress disorder in the sample population and explored relationships between complex post-traumatic stress disorder and post-traumatic stress disorder and a range of risk factors. METHODS: Survey participants (N = 189) were mental health support-seeking former-serving veterans of the Australian Defence Force (ADF) recruited from primary care. Confirmatory factor analysis was used to test the factorial validity of the International Trauma Questionnaire. RESULTS: The latent structure of the International Trauma Questionnaire was best represented by a two-factor second-order model consistent with the ICD-11 model of complex post-traumatic stress disorder. The International Trauma Questionnaire scale scores demonstrated excellent internal reliability. Overall, 9.1% (95% confidence interval = [4.8%, 13.5%]) met diagnostic requirements for post-traumatic stress disorder and an additional 51.4% (95% confidence interval = [44.0%, 58.9%]) met requirements for complex post-traumatic stress disorder. Those meeting diagnostic requirements for complex post-traumatic stress disorder were more likely to have served in the military for 15 years or longer, had a history of more traumatic life events and had the highest levels of depression, anxiety and stress symptoms. CONCLUSION: The International Trauma Questionnaire can effectively distinguish between post-traumatic stress disorder and complex post-traumatic stress disorder within primary care samples of Australian Defence Force veterans. A significantly greater proportion of Australian Defence Force veterans met criteria for complex post-traumatic stress disorder than post-traumatic stress disorder. Australian military mental health services should adopt the International Trauma Questionnaire to routinely screen for complex post-traumatic stress disorder and develop complex post-traumatic stress disorder specific interventions to promote recovery in Australian Defence Force veterans with complex post-traumatic stress disorder.

Theoretical basis for stabilizing messenger RNA through secondary structure design.

RNA hydrolysis presents problems in manufacturing, long-term storage, world-wide delivery and in vivo stability of messenger RNA (mRNA)-based vaccines and therapeutics. A largely unexplored strategy to reduce mRNA hydrolysis is to redesign RNAs to form double-stranded regions, which are protected from in-line cleavage and enzymatic degradation, while coding for the same proteins. The amount of stabilization that this strategy can deliver and the most effective algorithmic approach to achieve stabilization remain poorly understood. Here, we present simple calculations for estimating RNA stability against hydrolysis, and a model that links the average unpaired probability of an mRNA, or AUP, to its overall hydrolysis rate. To characterize the stabilization achievable through structure design, we compare AUP optimization by conventional mRNA design methods to results from more computationally sophisticated algorithms and crowdsourcing through the OpenVaccine challenge on the Eterna platform. We find that rational design on Eterna and the more sophisticated algorithms lead to constructs with low AUP, which we term 'superfolder' mRNAs. These designs exhibit a wide diversity of sequence and structure features that may be desirable for translation, biophysical size, and immunogenicity. Furthermore, their folding is robust to temperature, computer modeling method, choice of flanking untranslated regions, and changes in target protein sequence, as illustrated by rapid redesign of superfolder mRNAs for B.1.351, P.1 and B.1.1.7 variants of the prefusion-stabilized SARS-CoV-2 spike protein. Increases in in vitro mRNA half-life by at least two-fold appear immediately achievable.

RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look.

As the COVID-19 outbreak spreads, there is a growing need for a compilation of conserved RNA genome regions in the SARS-CoV-2 virus along with their structural propensities to guide development of antivirals and diagnostics. Here we present a first look at RNA sequence conservation and structural propensities in the SARS-CoV-2 genome. Using sequence alignments spanning a range of betacoronaviruses, we rank genomic regions by RNA sequence conservation, identifying 79 regions of length at least 15 nt as exactly conserved over SARS-related complete genome sequences available near the beginning of the COVID-19 outbreak. We then confirm the conservation of the majority of these genome regions across 739 SARS-CoV-2 sequences subsequently reported from the COVID-19 outbreak, and we present a curated list of 30 "SARS-related-conserved" regions. We find that known RNA structured elements curated as Rfam families and in prior literature are enriched in these conserved genome regions, and we predict additional conserved, stable secondary structures across the viral genome. We provide 106 "SARS-CoV-2-conserved-structured" regions as potential targets for antivirals that bind to structured RNA. We further provide detailed secondary structure models for the extended 5' UTR, frameshifting stimulation element, and 3' UTR. Lastly, we predict regions of the SARS-CoV-2 viral genome that have low propensity for RNA secondary structure and are conserved within SARS-CoV-2 strains. These 59 "SARS-CoV-2-conserved-unstructured" genomic regions may be most easily accessible by hybridization in primer-based diagnostic strategies.

Fluid flow-induced activation of subcellular AMPK and its interaction with FAK and Src.

AMP-activated protein kinase (AMPK) is a metabolic energy sensor that plays a critical role in cancer cell survival and growth. While the physical microenvironment is believed to influence tumor growth and progression, its role in AMPK regulation remains largely unknown. In the present study, we evaluated AMPK response to mechanical forces and its interaction with other mechano-responsive signaling proteins, FAK and Src. Using genetically encoded biosensors that can detect AMPK activities at different subcellular locations (cytosol, plasma membrane, nucleus, mitochondria, and Golgi apparatus), we observed that AMPK responds to shear stress in a subcellular location-dependent manner in breast cancer cells (MDA-MB-231). While normal epithelial cells (MCF-10A) also similarly responded to shear stress, they are less sensitive to shear stress compared to MDA-MB-231 cells. Inhibition of FAK and Src significantly decreased the basal activity level of AMPK at all five subcellular locations in MDA-MB-231 cells and selectively blocked shear stress-induced AMPK activation. Moreover, testing with cytoskeletal drugs revealed that myosin II might be the critical mediator of shear stress-induced AMPK activation in MDA-MB-231 cells. These findings suggest that breast cancer cells and normal epithelial cells may have different mechanosensitivity in AMPK signaling and that FAK and Src as well as the myosin II-dependent signaling pathway are involved in subcellular AMPK mechanotransduction in breast cancer cells.

Diagnosis of 'possible' mitochondrial disease: an existential crisis.

Primary genetic mitochondrial diseases are often difficult to diagnose, and the term 'possible' mitochondrial disease is used frequently by clinicians when such a diagnosis is suspected. There are now many known phenocopies of mitochondrial disease. Advances in genomic testing have shown that some patients with a clinical phenotype and biochemical abnormalities suggesting mitochondrial disease may have other genetic disorders. In instances when a genetic diagnosis cannot be confirmed, a diagnosis of 'possible' mitochondrial disease may result in harm to patients and their families, creating anxiety, delaying appropriate diagnosis and leading to inappropriate management or care. A categorisation of 'diagnosis uncertain', together with a specific description of the metabolic or genetic abnormalities identified, is preferred when a mitochondrial disease cannot be genetically confirmed.

Mechanotransduction of mitochondrial AMPK and its distinct role in flow-induced breast cancer cell migration.

The biophysical microenvironment of the tumor site has significant impact on breast cancer progression and metastasis. The importance of altered mechanotransduction in cancerous tissue has been documented, yet its role in the regulation of cellular metabolism and the potential link between cellular energy and cell migration remain poorly understood. In this study, we investigated the role of mechanotransduction in AMP-activated protein kinase (AMPK) activation in breast cancer cells in response to interstitial fluid flow (IFF). Additionally, we explored the involvement of AMPK in breast cancer cell migration. IFF was applied to the 3D cell-matrix construct. The subcellular signaling activity of Src, FAK, and AMPK was visualized in real-time using fluorescent resonance energy transfer (FRET). We observed that breast cancer cells (MDA-MB-231) are more sensitive to IFF than normal epithelial cells (MCF-10A). AMPK was activated at the mitochondria of MDA-MB-231 cells by IFF, but not in other subcellular compartments (i.e., cytosol, plasma membrane, and nucleus). The inhibition of FAK or Src abolished flow-induced AMPK activation in the mitochondria of MDA-MB-231 cells. We also observed that global AMPK activation reduced MDA-MB-231 cell migration. Interestingly, specific AMPK inhibition in the mitochondria reduced cell migration and blocked flow-induced cell migration. Our results suggest the linkage of FAK/Src and mitochondria-specific AMPK in mechanotransduction and the differential role of AMPK in breast cancer cell migration depending on its subcellular compartment-specific activation.

Monitoring clinical progression with mitochondrial disease biomarkers.

Mitochondrial disorders are genetically determined metabolic diseases due to a biochemical deficiency of the respiratory chain. Given that multi-system involvement and disease progression are common features of mitochondrial disorders they carry substantial morbidity and mortality. Despite this, no disease-modifying treatments exist with clear clinical benefits, and the current best management of mitochondrial disease is supportive. Several therapeutic strategies for mitochondrial disorders are now at a mature preclinical stage. Some are making the transition into early-phase patient trials, but the lack of validated biomarkers of disease progression presents a challenge when developing new therapies for patients. This update discusses current biomarkers of mitochondrial disease progression including metabolomics, circulating serum markers, exercise physiology, and both structural and functional imaging. We discuss the advantages and disadvantages of each approach, and consider emerging techniques with a potential role in trials of new therapies.

Note: Variational encoding of protein dynamics benefits from maximizing latent autocorrelation.

As deep Variational Auto-Encoder (VAE) frameworks become more widely used for modeling biomolecular simulation data, we emphasize the capability of the VAE architecture to concurrently maximize the time scale of the latent space while inferring a reduced coordinate, which assists in finding slow processes as according to the variational approach to conformational dynamics. We provide evidence that the VDE framework [Hernández et al., Phys. Rev. E 97, 062412 (2018)], which uses this autocorrelation loss along with a time-lagged reconstruction loss, obtains a variationally optimized latent coordinate in comparison with related loss functions. We thus recommend leveraging the autocorrelation of the latent space while training neural network models of biomolecular simulation data to better represent slow processes.

Investigating the role of boundary bricks in DNA brick self-assembly.

In the standard DNA brick set-up, distinct 32-nucleotide strands of single-stranded DNA are each designed to bind specifically to four other such molecules. Experimentally, it has been demonstrated that the overall yield is increased if certain bricks which occur on the outer faces of target structures are merged with adjacent bricks. However, it is not well understood by what mechanism such 'boundary bricks' increase the yield, as they likely influence both the nucleation process and the final stability of the target structure. Here, we use Monte Carlo simulations with a patchy particle model of DNA bricks to investigate the role of boundary bricks in the self-assembly of complex multicomponent target structures. We demonstrate that boundary bricks lower the free-energy barrier to nucleation and that boundary bricks on edges stabilize the final structure. However, boundary bricks are also more prone to aggregation, as they can stabilize partially assembled intermediates. We explore some design strategies that permit us to benefit from the stabilizing role of boundary bricks whilst minimizing their ability to hinder assembly; in particular, we show that maximizing the total number of boundary bricks is not an optimal strategy.

Effects of Al(3+) on Phosphocholine and Phosphoglycerol Containing Solid Supported Lipid Bilayers.

Aluminum has attracted great attention recently as it has been suggested by several studies to be associated with increased risks for Alzheimer's and Parkinson's disease. The toxicity of the trivalent ion is assumed to derive from structural changes induced in lipid bilayers upon binding, though the mechanism of this process is still not well understood. In the present study we elucidate the effect of Al(3+) on supported lipid bilayers (SLBs) using fluorescence microscopy, the quartz crystal microbalance with dissipation (QCM-D) technique, dual-polarization interferometry (DPI), and molecular dynamics (MD) simulations. Results from these techniques show that binding of Al(3+) to SLBs containing negatively charged and neutral phospholipids induces irreversible changes such as domain formation. The measured variations in SLB thickness, birefringence, and density indicate a phase transition from a disordered to a densely packed ordered phase.

Mitochondrial Causes of Epilepsy: Evaluation, Diagnosis, and Treatment.

Mitochondrial disorders are frequently associated with seizures. In this review, the authors discuss the seizure patterns and distinguishing features of mitochondrial epilepsy, alongside the indications for investigating, and how to investigate epilepsy from a mitochondrial perspective. Finally, they discuss management strategies for this complex group of patients.

The practical utility of routine postpartum hemoglobin assessment.

OBJECTIVE: To evaluate a policy of routine versus selective postpartum complete blood count (CBC). STUDY DESIGN: Historic case control design with matched subjects from 1 year periods bracketing the policy change (n = 800). Our primary outcome was postpartum transfusion rate. Univariable and multivariable analyses were performed. Total hospital costs were estimated. RESULTS: Eliminating routine postpartum CBC testing was associated with decreased transfusion rates (5.5% vs 1.8%, P = .007) despite similar transfusion risks. CBC utilization decreased from 59% to 22.2% (P < .0001). No adverse bleeding outcomes occurred. Multivariable modeling suggested that the occurrence of postpartum hemorrhage was the best clinical predictors of transfusion n risk. Tachycardia, oliguria, and symptoms were also effective at identifying transfusion candidates. Elimination of routine CBC was independently associated with a reduced risk of transfusion (odds ratio, 0.30; 95% confidence interval, 0.12-0.72). Annual cost savings were estimated at $58,000. CONCLUSION: Targeted CBC testing results in fewer transfusions, lower costs and improved quality of patient care.

The conformational landscape of fold-switcher KaiB is tuned to the circadian rhythm timescale.

How can a single protein domain encode a conformational landscape with multiple stably-folded states, and how do those states interconvert? Here, we use real-time and relaxation-dispersion NMR to characterize the conformational landscape of the circadian rhythm protein KaiB from Rhodobacter sphaeroides. Unique among known natural metamorphic proteins, this KaiB variant spontaneously interconverts between two monomeric states: the "Ground" and "Fold-switched" (FS) state. KaiB in its FS state interacts with multiple binding partners, including the central KaiC protein, to regulate circadian rhythms. We find that KaiB itself takes hours to interconvert between the Ground and FS state, underscoring the ability of a single sequence to encode the slow process needed for function. We reveal the rate-limiting step between the Ground and FS state is the cis-trans isomerization of three prolines in the fold-switching region by demonstrating interconversion acceleration by the prolyl isomerase CypA. The interconversion proceeds through a "partially disordered" (PD) state, where the C-terminal half becomes disordered while the N-terminal half remains stably folded. We discovered two additional properties of KaiB's landscape. Firstly, the Ground state experiences cold denaturation: at 4°C, the PD state becomes the majorly populated state. Secondly, the Ground state exchanges with a fourth state, the "Enigma" state, on the millisecond timescale. We combine AlphaFold2-based predictions and NMR chemical shift predictions to predict this "Enigma" state is a beta-strand register shift that eases buried charged residues, and support this structure experimentally. These results provide mechanistic insight in how evolution can design a single sequence that achieves specific timing needed for its function.

Compact RNA sensors for increasingly complex functions of multiple inputs.

Designing single molecules that compute general functions of input molecular partners represents a major unsolved challenge in molecular design. Here, we demonstrate that high-throughput, iterative experimental testing of diverse RNA designs crowdsourced from Eterna yields sensors of increasingly complex functions of input oligonucleotide concentrations. After designing single-input RNA sensors with activation ratios beyond our detection limits, we created logic gates, including challenging XOR and XNOR gates, and sensors that respond to the ratio of two inputs. Finally, we describe the OpenTB challenge, which elicited 85-nucleotide sensors that compute a score for diagnosing active tuberculosis, based on the ratio of products of three gene segments. Building on OpenTB design strategies, we created an algorithm Nucleologic that produces similarly compact sensors for the three-gene score based on RNA and DNA. These results open new avenues for diverse applications of compact, single molecule sensors previously limited by design complexity.

Outcomes of observation as therapy for pelvic organ prolapse: a study in the natural history of pelvic organ prolapse.

AIMS: To determine the rate of progression or regression of pelvic organ prolapse (POP) in subjects with symptomatic POP who decline intervention (pessary or surgery) and elect observation. METHODS: Sixty-four patients choosing observation as primary management of symptomatic POP were followed with sequential pelvic organ prolapse quantification (POP-Q) exams. A change in the leading edge value of ±≥2 cm was considered significant. POP-Q exam results, choice of therapy and symptom severity at last visit were recorded. RESULTS: The leading vaginal edge POP-Q exam value at initial exam ranged from -1.5 to 7 cm. Distribution of patients by POP-Q stages on initial exam was: stage I: 1%, stage II: 31%, stage III: 31%, and stage IV: 1.78% (50/64) of patients demonstrated no change in leading edge value from first to last visit on POP-Q exams. Nineteen percent (12/64) demonstrated progression (≥2 cm increase in leading edge); 3% (2/64) demonstrated regression (≥2 cm decrease in leading edge). Median follow-up was 16 months (range 6-91 months). On multivariate analysis, no variable, including length of follow-up, was associated with change in leading edge value (P = 0.09, data not shown). At their last recorded visit, 63% (40/64) of subjects continued observation, 38% (24/64) desired a pessary trial or surgical correction. Those desiring intervention had no greater worsening of prolapse on exam at last follow-up compared with subjects who continued observation. CONCLUSION: The natural history of pelvic organ prolapse is most often one of very minimal change in subjects who decline intervention (pessary or surgery) and choose observation.

Deep learning models for predicting RNA degradation via dual crowdsourcing.

Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ('Stanford OpenVaccine') on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102-130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.