Antibody agonists trigger immune receptor signaling through local exclusion of receptor-type protein tyrosine phosphatases.

Antibodies can block immune receptor engagement or trigger the receptor machinery to initiate signaling. We hypothesized that antibody agonists trigger signaling by sterically excluding large receptor-type protein tyrosine phosphatases (RPTPs) such as CD45 from sites of receptor engagement. An agonist targeting the costimulatory receptor CD28 produced signals that depended on antibody immobilization and were sensitive to the sizes of the receptor, the RPTPs, and the antibody itself. Although both the agonist and a non-agonistic anti-CD28 antibody locally excluded CD45, the agonistic antibody was more effective. An anti-PD-1 antibody that bound membrane proximally excluded CD45, triggered Src homology 2 domain-containing phosphatase 2 recruitment, and suppressed systemic lupus erythematosus and delayed-type hypersensitivity in experimental models. Paradoxically, nivolumab and pembrolizumab, anti-PD-1-blocking antibodies used clinically, also excluded CD45 and were agonistic in certain settings. Reducing these agonistic effects using antibody engineering improved PD-1 blockade. These findings establish a framework for developing new and improved therapies for autoimmunity and cancer.

Modulating Phase Behavior in Fatty Acid-Modified Elastin-like Polypeptides (FAMEs): Insights into the Impact of Lipid Length on Thermodynamics and Kinetics of Phase Separation.

Although post-translational lipidation is prevalent in eukaryotes, its impact on the liquid-liquid phase separation of disordered proteins is still poorly understood. Here, we examined the thermodynamic phase boundaries and kinetics of aqueous two-phase system (ATPS) formation for a library of elastin-like polypeptides modified with saturated fatty acids of different chain lengths. By systematically altering the physicochemical properties of the attached lipids, we were able to correlate the molecular properties of lipids to changes in the thermodynamic phase boundaries and the kinetic stability of droplets formed by these proteins. We discovered that increasing the chain length lowers the phase separation temperature in a sigmoidal manner due to alterations in the unfavorable interactions between protein and water and changes in the entropy of phase separation. Our kinetic studies unveiled remarkable sensitivity to lipid length, which we propose is due to the temperature-dependent interactions between lipids and the protein. Strikingly, we found that the addition of just a single methylene group is sufficient to allow tuning of these interactions as a function of temperature, with proteins modified with C7-C9 lipids exhibiting non-Arrhenius dependence in their phase separation, a behavior that is absent for both shorter and longer fatty acids. This work advances our theoretical understanding of protein-lipid interactions and opens avenues for the rational design of lipidated proteins in biomedical paradigms, where precise control over the phase separation is pivotal.

Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison.

BACKGROUND: Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions. OBJECTIVE: Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question "To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents' health outcomes over time?" METHODS: We conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes. RESULTS: We show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P<.001 in every case) when compared with the alternate approaches. For Norfolk, Virginia (n=31 census tracts), our approach estimated, on average, the percentage of individuals with high blood pressure in the census tract within 1.49% (SD 0.85%), the percentage of individuals with diabetes in the census tract within 1.63% (SD 0.59%), and the percentage of individuals who had >2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%). CONCLUSIONS: We propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents' health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics.

Temperature-Responsive Nano-Biomaterials from Genetically Encoded Farnesylated Disordered Proteins.

Despite broad interest in understanding the biological implications of protein farnesylation in regulating different facets of cell biology, the use of this post-translational modification to develop protein-based materials and therapies remains underexplored. The progress has been slow due to the lack of accessible methodologies to generate farnesylated proteins with broad physicochemical diversities rapidly. This limitation, in turn, has hindered the empirical elucidation of farnesylated proteins' sequence-structure-function rules. To address this gap, we genetically engineered prokaryotes to develop operationally simple, high-yield biosynthetic routes to produce farnesylated proteins and revealed determinants of their emergent material properties (nano-aggregation and phase-behavior) using scattering, calorimetry, and microscopy. These outcomes foster the development of farnesylated proteins as recombinant therapeutics or biomaterials with molecularly programmable assembly.

Adaptive Recombinant Nanoworms from Genetically Encodable Star Amphiphiles.

Recombinant nanoworms are promising candidates for materials and biomedical applications ranging from the templated synthesis of nanomaterials to multivalent display of bioactive peptides and targeted delivery of theranostic agents. However, molecular design principles to synthesize these assemblies (which are thermodynamically favorable only in a narrow region of the phase diagram) remain unclear. To advance the identification of design principles for the programmable assembly of proteins into well-defined nanoworms and to broaden their stability regimes, we were inspired by the ability of topologically engineered synthetic macromolecules to acess rare mesophases. To test this design principle in biomacromolecular assemblies, we used post-translational modifications (PTMs) to generate lipidated proteins with precise topological and compositional asymmetry. Using an integrated experimental and computational approach, we show that the material properties (thermoresponse and nanoscale assembly) of these hybrid amphiphiles are modulated by their amphiphilic architecture. Importantly, we demonstrate that the judicious choice of amphiphilic architecture can be used to program the assembly of proteins into adaptive nanoworms, which undergo a morphological transition (sphere-to-nanoworms) in response to temperature stimuli.

Physiology of the Weight-Reduced State: A Report from a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

Preventing regain of lost weight is the most difficult challenge in the treatment of obesity. The National Institute of Diabetes and Digestive and Kidney Diseases convened a workshop, "The Physiology of the Weight-Reduced State," on June 3 to 4, 2019, in order to explore the physiologic mechanisms of appetitive and metabolic adaptation that take place in the weight-reduced state and counter an individual's efforts to maintain reduced weight following weight loss.

BCAA Supplementation in Mice with Diet-induced Obesity Alters the Metabolome Without Impairing Glucose Homeostasis.

Circulating branched chain amino acid (BCAA) levels are elevated in obese humans and genetically obese rodents. However, the relationship of BCAAs to insulin resistance in diet-induced obese mice, a commonly used model to study glucose homeostasis, is still ill-defined. Here we examined how high-fat high-sucrose (HFHS) or high-fat diet (HFD) feeding, with or without BCAA supplementation in water, alters the metabolome in serum/plasma and tissues in mice and whether raising circulating BCAA levels worsens insulin resistance and glucose intolerance. Neither HFHS nor HFD feeding raised circulating BCAA levels in insulin-resistant diet-induced obese mice. BCAA supplementation raised circulating BCAA and branched-chain α-keto acid levels and C5-OH/C3-DC acylcarnitines (AC) in muscle from mice fed an HFHS diet or HFD, but did not worsen insulin resistance. A set of short- and long-chain acyl CoAs were elevated by diet alone in muscle, liver, and white adipose tissue (WAT), but not increased further by BCAA supplementation. HFD feeding reduced valine and leucine oxidation in WAT but not in muscle. BCAA supplementation markedly increased valine oxidation in muscle from HFD-fed mice, while leucine oxidation was unaffected by diet or BCAA treatment. Here we establish an extensive metabolome database showing tissue-specific changes in mice on 2 different HFDs, with or without BCAA supplementation. We conclude that mildly elevating circulating BCAAs and a subset of ACs by BCAA supplementation does not worsen insulin resistance or glucose tolerance in mice. This work highlights major differences in the effects of BCAAs on glucose homeostasis in diet-induced obese mice versus data reported in obese rats and in humans.

Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study.

BACKGROUND: Forecasting methods rely on trends and averages of prior observations to forecast COVID-19 case counts. COVID-19 forecasts have received much media attention, and numerous platforms have been created to inform the public. However, forecasting effectiveness varies by geographic scope and is affected by changing assumptions in behaviors and preventative measures in response to the pandemic. Due to time requirements for developing a COVID-19 vaccine, evidence is needed to inform short-term forecasting method selection at county, health district, and state levels. OBJECTIVE: COVID-19 forecasts keep the public informed and contribute to public policy. As such, proper understanding of forecasting purposes and outcomes is needed to advance knowledge of health statistics for policy makers and the public. Using publicly available real-time data provided online, we aimed to evaluate the performance of seven forecasting methods utilized to forecast cumulative COVID-19 case counts. Forecasts were evaluated based on how well they forecast 1, 3, and 7 days forward when utilizing 1-, 3-, 7-, or all prior-day cumulative case counts during early virus onset. This study provides an objective evaluation of the forecasting methods to identify forecasting model assumptions that contribute to lower error in forecasting COVID-19 cumulative case growth. This information benefits professionals, decision makers, and the public relying on the data provided by short-term case count estimates at varied geographic levels. METHODS: We created 1-, 3-, and 7-day forecasts at the county, health district, and state levels using (1) a naïve approach, (2) Holt-Winters (HW) exponential smoothing, (3) a growth rate approach, (4) a moving average (MA) approach, (5) an autoregressive (AR) approach, (6) an autoregressive moving average (ARMA) approach, and (7) an autoregressive integrated moving average (ARIMA) approach. Forecasts relied on Virginia's 3464 historical county-level cumulative case counts from March 7 to April 22, 2020, as reported by The New York Times. Statistically significant results were identified using 95% CIs of median absolute error (MdAE) and median absolute percentage error (MdAPE) metrics of the resulting 216,698 forecasts. RESULTS: The next-day MA forecast with 3-day look-back length obtained the lowest MdAE (median 0.67, 95% CI 0.49-0.84, P<.001) and statistically significantly differed from 39 out of 59 alternatives (66%) to 53 out of 59 alternatives (90%) at each geographic level at a significance level of .01. For short-range forecasting, methods assuming stationary means of prior days' counts outperformed methods with assumptions of weak stationarity or nonstationarity means. MdAPE results revealed statistically significant differences across geographic levels. CONCLUSIONS: For short-range COVID-19 cumulative case count forecasting at the county, health district, and state levels during early onset, the following were found: (1) the MA method was effective for forecasting 1-, 3-, and 7-day cumulative case counts; (2) exponential growth was not the best representation of case growth during early virus onset when the public was aware of the virus; and (3) geographic resolution was a factor in the selection of forecasting methods.

Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods.

The coronavirus disease 2019 (COVID-19) spread rapidly across the world since its appearance in December 2019. This data set creates one-, three-, and seven-day forecasts of the COVID-19 pandemic's cumulative case counts at the county, health district, and state geographic levels for the state of Virginia. Forecasts are created over the first 46 days of reported COVID-19 cases using the cumulative case count data provided by The New York Times as of April 22, 2020. From this historical data, one-, three-, seven, and all-days prior to the forecast start date are used to generate the forecasts. Forecasts are created using: (1) a Naïve approach; (2) Holt-Winters exponential smoothing (HW); (3) growth rate (Growth); (4) moving average (MA); (5) autoregressive (AR); (6) autoregressive moving average (ARMA); and (7) autoregressive integrated moving average (ARIMA). Median Absolute Error (MdAE) and Median Absolute Percentage Error (MdAPE) metrics are created with each forecast to evaluate the forecast with respect to existing historical data. These error metrics are aggregated to provide a means for assessing which combination of forecast method, forecast length, and lookback length are best fits, based on lowest aggregated error at each geographic level. The data set is comprised of an R-Project file, four R source code files, all 1,329,404 generated short-range forecasts, MdAE and MdAPE error metric data for each forecast, copies of the input files, and the generated comparison tables. All code and data files are provided to provide transparency and facilitate replicability and reproducibility. This package opens directly in RStudio through the R Project file. The R Project file removes the need to set path locations for the folders contained within the data set to simplify setup requirements. This data set provides two avenues for reproducing results: 1) Use the provided code to generate the forecasts from scratch and then run the analyses; or 2) Load the saved forecast data and run the analyses on the stored data. Code annotations provide the instructions needed to accomplish both routes. This data can be used to generate the same set of forecasts and error metrics for any US state by altering the state parameter within the source code. Users can also generate health district forecasts for any other state, by providing a file which maps each county within a state to its respective health-district. The source code can be connected to the most up-to-date version of The New York Times COVID-19 dataset allows for the generation of forecasts up to the most recently reported data to facilitate near real-time forecasting.

A content analysis-based approach to explore simulation verification and identify its current challenges.

Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a corpus of 4,047 peer-reviewed Modeling and Simulation (M&S) publications dealing with a wide range of studies of simulation verification from 1963 to 2015. We group the selected papers by decade of publication to provide insights and explore the corpus from four perspectives: (i) the positioning of prominent concepts across the corpus as a whole; (ii) a comparison of the prominence of verification, validation, and Verification and Validation (V&V) as separate concepts; (iii) the positioning of the concepts specifically associated with verification; and (iv) an evaluation of verification's defining characteristics within each decade. Our analysis reveals unique characterizations of verification in each decade. The insights gathered helped to identify and discuss three categories of verification challenges as avenues of future research, awareness, and understanding for researchers, students, and practitioners. These categories include conveying confidence and maintaining ease of use; techniques' coverage abilities for handling increasing simulation complexities; and new ways to provide error feedback to model users.

Post-Translational Modification Mimicry for Programmable Assembly of Elastin-Based Protein Polymers.

Post-translational modification (PTM) of protein polymers is emerging as a powerful bioinspired strategy to create protein-based hybrid materials with molecularly encoded assembly and function for applications in nanobiotechnology and medicine. While these modifications can be accomplished by harnessing native biological machinery (i.e., enzymes), the evolutionarily programmed specificity of these enzymes (recognition of select substrates and the limited repertoire of ligation chemistries catalyzed by these enzymes) can limit the type and linkage of PTMs appended to proteins. One approach to overcome this limitation is to leverage advances in site-selective biomolecular modification to prepare synthetic mimics of naturally occurring PTMs that are absent in nature. As a proof of concept, we used scalable bio-orthogonal reactions to prepare synthetic mimics of lipidated proteins with tunable assembly and disassembly. Additionally, we demonstrated that our PTM mimicry regulates the stimuli-responsive phase behavior of intrinsically disordered biopolymers, modulates their self-assembly at the nanoscale, and can be used for programmable disassembly of these materials in acidic environments. Synthetic PTM mimicry opens a path to encode new assembly and disassembly capabilities into hybrid materials that cannot be produced via biosynthesis.

Perspective: Dietary Biomarkers of Intake and Exposure-Exploration with Omics Approaches.

While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research.

NIH workshop on human milk composition: summary and visions.

Nationally representative data from mother-child dyads that capture human milk composition (HMC) and associated health outcomes are important for advancing the evidence to inform federal nutrition and related health programs, policies, and consumer information across the governments in the United States and Canada as well as in nongovernment sectors. In response to identified gaps in knowledge, the National Institute of Diabetes and Digestive and Kidney Diseases of the NIH sponsored the "Workshop on Human Milk Composition-Biological, Environmental, Nutritional, and Methodological Considerations" held 16-17 November 2017 in Bethesda, Maryland. Through presentations and discussions, the workshop aimed to 1) share knowledge on the scientific need for data on HMC; 2) explore the current understanding of factors affecting HMC; 3) identify methodological challenges in human milk (HM) collection, storage, and analysis; and 4) develop a vision for a research program to develop an HMC data repository and database. The 4 workshop sessions included 1) perspectives from both federal agencies and nonfederal academic experts, articulating scientific needs for data on HMC that could lead to new research findings and programmatic advances to support public health; 2) information about the factors that influence lactation and/or HMC; 3) considerations for data quality, including addressing sampling strategies and the complexities in standardizing collection, storage, and analyses of HM; and 4) insights on how existing research programs and databases can inform potential visions for HMC initiatives. The general consensus from the workshop is that the limited scope of HM research initiatives has led to a lack of robust estimates of the composition and volume of HM consumed and, consequently, missed opportunities to improve maternal and infant health.

A report of activities related to the Dietary Reference Intakes from the Joint Canada-US Dietary Reference Intakes Working Group.

The governments of the United States and Canada have jointly undertaken the development of the Dietary Reference Intakes (DRIs) since the mid-1990s. The Federal DRI committees from each country work collaboratively to identify DRI needs, prioritize nutrient reviews, advance work to resolve methodological issues that is necessary for new reviews, and sponsor DRI-related committees through the National Academies of Sciences, Engineering and Medicine. In recent years, the Joint Canada-US DRI Working Group, consisting of members from both Federal DRI committees, developed an open and transparent nomination process for prioritizing nutrients for DRI review, by which sodium, the omega-3 (n-3) fatty acids, vitamin E, and magnesium were identified. In addition, discussions during the nutrient nomination process prompted the Federal DRI committees to address previously identified issues related to the use of chronic disease endpoints when setting DRIs. The development of guiding principles for setting DRIs based on chronic disease risk reduction will be applied for the first time during the DRI review of sodium and potassium. In summary, the US and Canadian governments have worked collaboratively to adapt our approach to prioritizing nutrients for DRI review and to broaden the scope of the DRIs to better incorporate the concept of chronic disease risk reduction in order to improve public health.

Considerations for best practices in studies of fiber or other dietary components and the intestinal microbiome.

A 2-day workshop organized by the National Institutes of Health and U.S. Department of Agriculture included 16 presentations focused on the role of diet in alterations of the gastrointestinal microbiome, primarily that of the colon. Although thousands of research projects have been funded by U.S. federal agencies to study the intestinal microbiome of humans and a variety of animal models, only a minority addresses dietary effects, and a small subset is described in sufficient detail to allow reproduction of a study. Whereas there are standards being developed for many aspects of microbiome studies, such as sample collection, nucleic acid extraction, data handling, etc., none has been proposed for the dietary component; thus this workshop focused on the latter specific point. It is important to foster rigor in design and reproducibility of published studies to maintain high quality and enable designs that can be compared in systematic reviews. Speakers addressed the influence of the structure of the fermentable carbohydrate on the microbiota and the variables to consider in design of studies using animals, in vitro models, and human subjects. For all types of studies, strengths and weaknesses of various designs were highlighted, and for human studies, comparisons between controlled feeding and observational designs were discussed. Because of the lack of published, best-diet formulations for specific research questions, the main recommendation is to describe dietary ingredients and treatments in as much detail as possible to allow reproduction by other scientists.

Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter.

In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists' emotions when visiting a city's tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and activities like concerts, impact tourists' emotions. In addition, our analysis suggests that tourists experience greater levels of enjoyment in places such as observatories rather than zoos. Finally, we find that local and international visitors tend to convey negative sentiment when visiting more than one attraction in a day whereas the opposite holds for out of state visitors.

Scientific rigor and credibility in the nutrition research landscape.

Scientific progress depends on the quality and credibility of research methods. As discourse on rigor, transparency, and reproducibility joins the cacophony of nutrition information and misinformation in mass media, buttressing the real and perceived reliability of nutrition science is more important than ever. This broad topic was the focus of a 2016 plenary session, "Scientific Rigor and Competing Interests in the Nutrition Research Landscape." This article summarizes and expands on this session in an effort to increase understanding and dialogue with regard to factors that limit the real and perceived reliability of nutrition science and steps that can be taken to mitigate those factors. The end goal is to both earn and merit greater trust in nutrition science by both the scientific community and the general public. The authors offer suggestions in each of the domains of education and training, communications, research conduct, and procedures and policies to help achieve this goal. The authors emphasize the need for adequate funding to support these efforts toward greater rigor and transparency, which will be resource demanding and may require either increased research funding or the recognition that a greater proportion of research funding may need to be allocated to these tasks.

A common mechanism by which type 2A von Willebrand disease mutations enhance ADAMTS13 proteolysis revealed with a von Willebrand factor A2 domain FRET construct.

Rheological forces in the blood trigger the unfolding of von Willebrand factor (VWF) and its A2 domain, exposing the scissile bond for proteolysis by ADAMTS13. Under quiescent conditions, the scissile bond is hidden by the folded structure due to the stabilisation provided by the structural specialisations of the VWF A2 domain, a vicinal disulphide bond, a calcium binding site and a N1574-glycan.The reduced circulating high MW multimers of VWF in patients with type 2A von Willebrand disease (VWD) may be associated with mutations within the VWF A2 domain and this is attributed to enhanced ADAMTS13 proteolysis. We investigated 11 VWF A2 domain variants identified in patients with type 2A VWD. In recombinant full-length VWF, enhanced ADAMTS13 proteolysis was detected for all of the expressed variants in the presence of urea-induced denaturation. A subset of the FLVWF variants displayed enhanced proteolysis in the absence of urea. The mechanism of enhancement was investigated using a novel VWF A2 domain FRET construct. In the absence of induced unfolding, 7/8 of the expressed mutants exhibited a disrupted domain fold, causing spatial separation of the N- and C- termini. Three of the type 2A mutants were not secreted when studied within the VWF A2 domain FRET construct. Urea denaturation revealed for all 8 secreted mutants reduced unfolding cooperativity and stability of the VWF A2 domain. As folding stability was progressively disrupted, proteolysis by ADAMTS13 increased. Due to the range of folding stabilities and wide distribution of VWF A2 domain mutations studied, we conclude that these mutations disrupt regulated folding of the VWF A2 domain. They enhance unfolding by inducing separation of N- and C-termini, thereby promoting a more open conformation that reveals its binding sites for ADAMTS13 and the scissile bond.

Discovery of 4,6-disubstituted pyrimidines as potent inhibitors of the heat shock factor 1 (HSF1) stress pathway and CDK9.

Heat shock factor 1 (HSF1) is a transcription factor that plays key roles in cancer, including providing a mechanism for cell survival under proteotoxic stress. Therefore, inhibition of the HSF1-stress pathway represents an exciting new opportunity in cancer treatment. We employed an unbiased phenotypic screen to discover inhibitors of the HSF1-stress pathway. Using this approach we identified an initial hit (1) based on a 4,6-pyrimidine scaffold (2.00 µM). Optimisation of cellular SAR led to an inhibitor with improved potency (25, 15 nM) in the HSF1 phenotypic assay. The 4,6-pyrimidine 25 was also shown to have high potency against the CDK9 enzyme (3 nM).