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.

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.

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.

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.

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.

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.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics.

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured "superfolder" mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Deep learning models for predicting RNA degradation via dual crowdsourcing.

Messenger RNA-based medicines 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 6043 102-130-nucleotide diverse 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 (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales.