Folding of Staphylococcal Nuclease Induced by Binding of Chemically Modified Substrate Analogues Sheds Light on Mechanisms of Coupled Folding/Binding Reactions.

Several proteins have been shown to undergo a shift in the mechanism of ligand binding-induced folding from conformational selection (CS; folding precedes binding) to induced fit (IF; binding precedes folding) with increasing ligand concentration. In previous studies of the coupled folding/binding reaction of staphylococcal nuclease (SNase) in the presence of a substrate analogue, adenosine-3',5'-diphosphate (prAp), we found that the two phosphate groups make important energetic contributions toward stabilizing its complex with the native protein as well as transient conformational states encountered at high ligand concentrations favoring IF. However, the structural contributions of each phosphate group during the reaction remain unclear. To address this question, we relied on fluorescence, nuclear magnetic resonance (NMR), absorption, and isothermal titration calorimetry to study the effects of deletion of the phosphate groups of prAp on the kinetics of ligand-induced folding, using a strategy analogous to mutational ϕ-value analysis to interpret the results. Kinetic measurements over a wide range of ligand concentrations, together with structural characterization of a transient protein-ligand encounter complex using 2D NMR, indicated that, at high ligand concentrations favoring IF, (i) the 5'-phosphate group interacts weakly with denatured SNase during early stages of the reaction, resulting in loose docking of the two domains of SNase, and (ii) the 3'-phosphate group engages in some specific contacts with the polypeptide in the transition state prior to formation of the native SNase-prAp complex.

Energetics and kinetics of substrate analog-coupled staphylococcal nuclease folding revealed by a statistical mechanical approach.

Protein conformational changes associated with ligand binding, especially those involving intrinsically disordered proteins, are mediated by tightly coupled intra- and intermolecular events. Such reactions are often discussed in terms of two limiting kinetic mechanisms, conformational selection (CS), where folding precedes binding, and induced fit (IF), where binding precedes folding. It has been shown that coupled folding/binding reactions can proceed along both CS and IF pathways with the flux ratio depending on conditions such as ligand concentration. However, the structural and energetic basis of such complex reactions remains poorly understood. Therefore, we used experimental, theoretical, and computational approaches to explore structural and energetic aspects of the coupled-folding/binding reaction of staphylococcal nuclease in the presence of the substrate analog adenosine-3',5'-diphosphate. Optically monitored equilibrium and kinetic data, combined with a statistical mechanical model, gave deeper insight into the relative importance of specific and Coulombic protein-ligand interactions in governing the reaction mechanism. We also investigated structural aspects of the reaction at the residue level using NMR and all-atom replica-permutation molecular dynamics simulations. Both approaches yielded clear evidence for accumulation of a transient protein-ligand encounter complex early in the reaction under IF-dominant conditions. Quantitative analysis of the equilibrium/kinetic folding revealed that the ligand-dependent CS-to-IF shift resulted from stabilization of the compact transition state primarily by weakly ligand-dependent Coulombic interactions with smaller contributions from specific binding energies. At a more macroscopic level, the CS-to-IF shift was represented as a displacement of the reaction "route" on the free energy surface, which was consistent with a flux analysis.

Effects of ionic strength on the folding and stability of SAMP1, a ubiquitin-like halophilic protein.

Our knowledge of the folding behavior of proteins from extremophiles is limited at this time. These proteins may more closely resemble the primordial proteins selected in early evolution under extreme conditions. The small archaeal modifier protein 1 (SAMP1) studied in this report is an 87-residue protein with a ß-grasp fold found in the halophile Haloferax volcanii from the Dead Sea. To gain insight into the effects of salt on the stability and folding mechanism of SAMP1, we conducted equilibrium and kinetic folding experiments as a function of sodium chloride concentration. The results revealed that increasing ionic strength accelerates refolding and slows down unfolding of SAMP1, giving rise to a pronounced salt-induced stabilization. With increasing NaCl concentration, the rate of folding observed via a combination of continuous-flow (0.1-2 ms time range) and stopped-flow measurements (>2 ms) exhibited a >100-fold increase between 0.1 and 1.5 M NaCl and leveled off at higher concentrations. Using the Linderström-Lang smeared charge formalism to model electrostatic interactions in ground and transition states encountered during folding, we showed that the observed salt dependence is dominated by Debye-Hückel screening of electrostatic repulsion among numerous negatively charged residues. Comparisons are also drawn with three well-studied mesophilic members of the ß-grasp superfamily: protein G, protein L, and ubiquitin. Interestingly, the folding rate of SAMP1 in 3 M sodium chloride is comparable to that of protein G, ubiquitin, and protein L at lower ionic strength. The results indicate the important role of electrostatic interactions in protein folding and imply that proteins have evolved to minimize unfavorable charge-charge interactions under their specific native conditions.

Advances in Mixer Design and Detection Methods for Kinetics Studies of Macromolecular Folding and Binding on the Microsecond Time Scale.

Many important biological processes such as protein folding and ligand binding are too fast to be fully resolved using conventional stopped-flow techniques. Although advances in mixer design and detection methods have provided access to the microsecond time regime, there is room for improvement in terms of temporal resolution and sensitivity. To address this need, we developed a continuous-flow mixing instrument with a dead time of 12 to 27 µs (depending on solution viscosity) and enhanced sensitivity, sufficient for monitoring tryptophan or tyrosine fluorescence changes at fluorophore concentrations as low as 1 µM. Relying on commercially available laser microfabrication services, we obtained an integrated mixer/flow-cell assembly on a quartz chip, based on a cross-channel configuration with channel dimensions and geometry designed to minimize backpressure. By gradually increasing the width of the observation channel downstream from the mixing region, we are able to monitor a reaction progress time window ranging from ~10 µs out to ~3 ms. By combining a solid-state UV laser with a Galvano-mirror scanning strategy, we achieved highly efficient and uniform fluorescence excitation along the flow channel. Examples of applications, including refolding of acid-denatured cytochrome c triggered by a pH jump and binding of a peptide ligand to a PDZ domain, demonstrate the capability of the technique to resolve fluorescence changes down to the 10 µs time regime on modest amounts of reagents.

Semi-Quantitative MALDI Measurements of Blood-Based Samples for Molecular Diagnostics.

Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications.

Explaining multivariate molecular diagnostic tests via Shapley values.

BACKGROUND: Machine learning (ML) can be an effective tool to extract information from attribute-rich molecular datasets for the generation of molecular diagnostic tests. However, the way in which the resulting scores or classifications are produced from the input data may not be transparent. Algorithmic explainability or interpretability has become a focus of ML research. Shapley values, first introduced in game theory, can provide explanations of the result generated from a specific set of input data by a complex ML algorithm. METHODS: For a multivariate molecular diagnostic test in clinical use (the VeriStrat® test), we calculate and discuss the interpretation of exact Shapley values. We also employ some standard approximation techniques for Shapley value computation (local interpretable model-agnostic explanation (LIME) and Shapley Additive Explanations (SHAP) based methods) and compare the results with exact Shapley values. RESULTS: Exact Shapley values calculated for data collected from a cohort of 256 patients showed that the relative importance of attributes for test classification varied by sample. While all eight features used in the VeriStrat® test contributed equally to classification for some samples, other samples showed more complex patterns of attribute importance for classification generation. Exact Shapley values and Shapley-based interaction metrics were able to provide interpretable classification explanations at the sample or patient level, while patient subgroups could be defined by comparing Shapley value profiles between patients. LIME and SHAP approximation approaches, even those seeking to include correlations between attributes, produced results that were quantitatively and, in some cases qualitatively, different from the exact Shapley values. CONCLUSIONS: Shapley values can be used to determine the relative importance of input attributes to the result generated by a multivariate molecular diagnostic test for an individual sample or patient. Patient subgroups defined by Shapley value profiles may motivate translational research. However, correlations inherent in molecular data and the typically small ML training sets available for molecular diagnostic test development may cause some approximation methods to produce approximate Shapley values that differ both qualitatively and quantitatively from exact Shapley values. Hence, caution is advised when using approximate methods to evaluate Shapley explanations of the results of molecular diagnostic tests.

Mass Spectrometry-Based Multivariate Proteomic Tests for Prediction of Outcomes on Immune Checkpoint Blockade Therapy: The Modern Analytical Approach.

The remarkable success of immune checkpoint inhibitors (ICIs) has given hope of cure for some patients with advanced cancer; however, the fraction of responding patients is 15-35%, depending on tumor type, and the proportion of durable responses is even smaller. Identification of biomarkers with strong predictive potential remains a priority. Until now most of the efforts were focused on biomarkers associated with the assumed mechanism of action of ICIs, such as levels of expression of programmed death-ligand 1 (PD-L1) and mutation load in tumor tissue, as a proxy of immunogenicity; however, their performance is unsatisfactory. Several assays designed to capture the complexity of the disease by measuring the immune response in tumor microenvironment show promise but still need validation in independent studies. The circulating proteome contains an additional layer of information characterizing tumor-host interactions that can be integrated into multivariate tests using modern machine learning techniques. Here we describe several validated serum-based proteomic tests and their utility in the context of ICIs. We discuss test performances, demonstrate their independence from currently used biomarkers, and discuss various aspects of associated biological mechanisms. We propose that serum-based multivariate proteomic tests add a missing piece to the puzzle of predicting benefit from ICIs.

A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics data.

BACKGROUND: Modern genomic and proteomic profiling methods produce large amounts of data from tissue and blood-based samples that are of potential utility for improving patient care. However, the design of precision medicine tests for unmet clinical needs from this information in the small cohorts available for test discovery remains a challenging task. Obtaining reliable performance assessments at the earliest stages of test development can also be problematic. We describe a novel approach to classifier development designed to create clinically useful tests together with reliable estimates of their performance. The method incorporates elements of traditional and modern machine learning to facilitate the use of cohorts where the number of samples is less than the number of measured patient attributes. It is based on a hierarchy of classification and information abstraction and combines boosting, bagging, and strong dropout regularization. RESULTS: We apply this dropout-regularized combination approach to two clinical problems in oncology using mRNA expression and associated clinical data and compare performance with other methods of classifier generation, including Random Forest. Performance of the new method is similar to or better than the Random Forest in the two classification tasks used for comparison. The dropout-regularized combination method also generates an effective classifier in a classification task with a known confounding variable. Most importantly, it provides a reliable estimate of test performance from a relatively small development set of samples. CONCLUSIONS: The flexible dropout-regularized combination approach is able to produce tests tailored to particular clinical questions and mitigate known confounding effects. It allows the design of molecular diagnostic tests addressing particular clinical questions together with reliable assessment of whether test performance is likely to be fit-for-purpose in independent validation at the earliest stages of development.

Robust identification of molecular phenotypes using semi-supervised learning.

BACKGROUND: Modern molecular profiling techniques are yielding vast amounts of data from patient samples that could be utilized with machine learning methods to provide important biological insights and improvements in patient outcomes. Unsupervised methods have been successfully used to identify molecularly-defined disease subtypes. However, these approaches do not take advantage of potential additional clinical outcome information. Supervised methods can be implemented when training classes are apparent (e.g., responders or non-responders to treatment). However, training classes can be difficult to define when assessing relative benefit of one therapy over another using gold standard clinical endpoints, since it is often not clear how much benefit each individual patient receives. RESULTS: We introduce an iterative approach to binary classification tasks based on the simultaneous refinement of training class labels and classifiers towards self-consistency. As training labels are refined during the process, the method is well suited to cases where training class definitions are not obvious or noisy. Clinical data, including time-to-event endpoints, can be incorporated into the approach to enable the iterative refinement to identify molecular phenotypes associated with a particular clinical variable. Using synthetic data, we show how this approach can be used to increase the accuracy of identification of outcome-related phenotypes and their associated molecular attributes. Further, we demonstrate that the advantages of the method persist in real world genomic datasets, allowing the reliable identification of molecular phenotypes and estimation of their association with outcome that generalizes to validation datasets. We show that at convergence of the iterative refinement, there is a consistent incorporation of the molecular data into the classifier yielding the molecular phenotype and that this allows a robust identification of associated attributes and the underlying biological processes. CONCLUSIONS: The consistent incorporation of the structure of the molecular data into the classifier helps to minimize overfitting and facilitates not only good generalization of classification and molecular phenotypes, but also reliable identification of biologically relevant features and elucidation of underlying biological processes.

The Tyrosine Kinase c-Src Specifically Binds to the Active Integrin αIIbß3 to Initiate Outside-in Signaling in Platelets.

It is currently believed that inactive tyrosine kinase c-Src in platelets binds to the cytoplasmic tail of the ß3 integrin subunit via its SH3 domain. Although a recent NMR study supports this contention, it is likely that such binding would be precluded in inactive c-Src because an auto-inhibitory linker physically occludes the ß3 tail binding site. Accordingly, we have re-examined c-Src binding to ß3 by immunoprecipitation as well as NMR spectroscopy. In unstimulated platelets, we detected little to no interaction between c-Src and ß3. Following platelet activation, however, c-Src was co-immunoprecipitated with ß3 in a time-dependent manner and underwent progressive activation as well. We then measured chemical shift perturbations in the (15)N-labeled SH3 domain induced by the C-terminal ß3 tail peptide NITYRGT and found that the peptide interacted with the SH3 domain RT-loop and surrounding residues. A control peptide whose last three residues where replaced with those of the ß1 cytoplasmic tail induced only small chemical shift perturbations on the opposite face of the SH3 domain. Next, to mimic inactive c-Src, we found that the canonical polyproline peptide RPLPPLP prevented binding of the ß3 peptide to the RT- loop. Under these conditions, the ß3 peptide induced chemical shift perturbations similar to the negative control. We conclude that the primary interaction of c-Src with the ß3 tail occurs in its activated state and at a site that overlaps with PPII binding site in its SH3 domain. Interactions of inactive c-Src with ß3 are weak and insensitive to ß3 tail mutations.

Predictive value of a proteomic signature in patients with non-small-cell lung cancer treated with second-line erlotinib or chemotherapy (PROSE): a biomarker-stratified, randomised phase 3 trial.

BACKGROUND: An established multivariate serum protein test can be used to classify patients according to whether they are likely to have a good or poor outcome after treatment with EGFR tyrosine-kinase inhibitors. We assessed the predictive power of this test in the comparison of erlotinib and chemotherapy in patients with non-small-cell lung cancer. METHODS: From Feb 26, 2008, to April 11, 2012, patients (aged ≥18 years) with histologically or cytologically confirmed, second-line, stage IIIB or IV non-small-cell lung cancer were enrolled in 14 centres in Italy. Patients were stratified according to a minimisation algorithm by Eastern Cooperative Oncology Group performance status, smoking history, centre, and masked pretreatment serum protein test classification, and randomly assigned centrally in a 1:1 ratio to receive erlotinib (150 mg/day, orally) or chemotherapy (pemetrexed 500 mg/m(2), intravenously, every 21 days, or docetaxel 75 mg/m(2), intravenously, every 21 days). The proteomic test classification was masked for patients and investigators who gave treatments, and treatment allocation was masked for investigators who generated the proteomic classification. The primary endpoint was overall survival and the primary hypothesis was the existence of a significant interaction between the serum protein test classification and treatment. Analyses were done on the per-protocol population. This trial is registered with ClinicalTrials.gov, number NCT00989690. FINDINGS: 142 patients were randomly assigned to chemotherapy and 143 to erlotinib, and 129 (91%) and 134 (94%), respectively, were included in the per-protocol analysis. 88 (68%) patients in the chemotherapy group and 96 (72%) in the erlotinib group had a proteomic test classification of good. Median overall survival was 9·0 months (95% CI 6·8-10·9) in the chemotherapy group and 7·7 months (5·9-10·4) in the erlotinib group. We noted a significant interaction between treatment and proteomic classification (pinteraction=0·017 when adjusted for stratification factors; pinteraction=0·031 when unadjusted for stratification factors). Patients with a proteomic test classification of poor had worse survival on erlotinib than on chemotherapy (hazard ratio 1·72 [95% CI 1·08-2·74], p=0·022). There was no significant difference in overall survival between treatments for patients with a proteomic test classification of good (adjusted HR 1·06 [0·77-1·46], p=0·714). In the group of patients who received chemotherapy, the most common grade 3 or 4 toxic effect was neutropenia (19 [15%] vs one [<1%] in the erlotinib group), whereas skin toxicity (one [<1%] vs 22 [16%]) was the most frequent in the erlotinib group. INTERPRETATION: Our findings indicate that serum protein test status is predictive of differential benefit in overall survival for erlotinib versus chemotherapy in the second-line setting. Patients classified as likely to have a poor outcome have better outcomes on chemotherapy than on erlotinib. FUNDING: Italian Ministry of Health, Italian Association of Cancer Research, and Biodesix.

Ultrafast hydrogen exchange reveals specific structural events during the initial stages of folding of cytochrome c.

Many proteins undergo a sharp decrease in chain dimensions during early stages of folding, prior to the rate-limiting step in folding. However, it remains unclear whether compact states are the result of specific folding events or a general hydrophobic collapse of the poly peptide chain driven by the change in solvent conditions. To address this fundamental question, we extended the temporal resolution of NMR-detected H/D exchange labeling experiments into the microsecond regime by adopting a microfluidics approach. By observing the competition between H/D exchange and folding as a function of labeling pH, coupled with direct measurement of exchange rates in the unfolded state, we were able to monitor hydrogen-bond formation for over 50 individual backbone NH groups within the initial 140 microseconds of folding of horse cytochrome c. Clusters of solvent-shielded amide protons were observed in two α-helical segments in the C-terminal half of the protein, while the N-terminal helix remained largely unstructured, suggesting that proximity in the primary structure is a major factor in promoting helix formation and association at early stages of folding, while the entropically more costly long-range contacts between the N- and C-terminal helices are established only during later stages. Our findings clearly indicate that the initial chain condensation in cytochrome c is driven by specific interactions among a subset of α-helical segments rather than a general hydrophobic collapse.

Design of a switchable eliminase.

The active sites of enzymes are lined with side chains whose dynamic, geometric, and chemical properties have been finely tuned relative to the corresponding residues in water. For example, the carboxylates of glutamate and aspartate are weakly basic in water but become strongly basic when dehydrated in enzymatic sites. The dehydration of the carboxylate, although intrinsically thermodynamically unfavorable, is achieved by harnessing the free energy of folding and substrate binding to reach the required basicity. Allosterically regulated enzymes additionally rely on the free energy of ligand binding to stabilize the protein in a catalytically competent state. We demonstrate the interplay of protein folding energetics and functional group tuning to convert calmodulin (CaM), a regulatory binding protein, into AlleyCat, an allosterically controlled eliminase. Upon binding Ca(II), native CaM opens a hydrophobic pocket on each of its domains. We computationally identified a mutant that (i) accommodates carboxylate as a general base within these pockets, (ii) interacts productively in the Michaelis complex with the substrate, and (iii) stabilizes the transition state for the reaction. Remarkably, a single mutation of an apolar residue at the bottom of an otherwise hydrophobic cavity confers catalytic activity on calmodulin. AlleyCat showed the expected pH-rate profile, and it was inactivated by mutation of its active site Glu to Gln. A variety of control mutants demonstrated the specificity of the design. The activity of this minimal 75-residue allosterically regulated catalyst is similar to that obtained using more elaborate computational approaches to redesign complex enzymes to catalyze the Kemp elimination reaction.

PDLIM3 supports hedgehog signaling in medulloblastoma by facilitating cilia formation.

Elevated levels of PDLIM3 expression are frequently detected in sonic hedgehog (SHH) group of medulloblastoma (MB). However, the possible role of PDLIM3 in MB tumorigenesis is still unknown. Here, we found that PDLIM3 expression is necessary for hedgehog (Hh) pathway activation in MB cells. PDLIM3 is present in primary cilia of MB cells and fibroblasts, and such cilia localization is mediated by the PDZ domain of PDLIM3 protein. Deletion of PDLIM3 significantly compromised cilia formation and interfered the Hh signaling transduction in MB cells, suggesting that PDLIM3 promotes the Hh signaling through supporting the ciliogenesis. PDLIM3 protein physically interacts with cholesterol, a critical molecule for cilia formation and hedgehog signaling. The disruption of cilia formation and Hh signaling in PDLIM3 null MB cells or fibroblasts, was significantly rescued by treatment with exogenous cholesterol, demonstrating that PDLIM3 facilitates the ciliogenesis through cholesterol provision. Finally, deletion of PDLIM3 in MB cells significantly inhibited their proliferation and repressed tumor growth, suggesting that PDLIM3 is necessary for MB tumorigenesis. Our studies elucidate the critical functions of PDLIM3 in the ciliogenesis and Hh signaling transduction in SHH-MB cells, supporting to utilize PDLIM3 as a molecular marker for defining SHH group of MB in clinics.

Molecular and translational biology of the blood-based VeriStrat® proteomic test used in cancer immunotherapy treatment guidance.

Introduction: The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified. Objectives: The objective of this study was to identify the proteoforms that are measured by VS. Methods: To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks. Results: The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin. Conclusion: Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.

Retroviral integrases promote fraying of viral DNA ends.

In the initial step of integration, retroviral integrase (IN) introduces precise nicks in the degenerate, short inverted repeats at the ends of linear viral DNA. The scissile phosphodiester bond is located immediately 3' of a highly conserved CA/GT dinucleotide, usually 2 bp from the ends. These nicks create new recessed 3'-OH viral DNA ends that are required for joining to host cell DNA. Previous studies have indicated that unpairing, "fraying," of the viral DNA ends by IN contributes to end recognition or catalysis. Here, we report that end fraying can be detected independently of catalysis with both avian sarcoma virus (ASV) and human immunodeficiency virus type 1 (HIV-1) IN proteins by use of fluorescence resonance energy transfer (FRET). The results were indicative of an IN-induced intramolecular conformational change in the viral DNA ends (cis FRET). Fraying activity is tightly coupled to the DNA binding capabilities of these enzymes, as follows: an inhibitor effective against both IN proteins was shown to block ASV IN DNA binding and end fraying, with similar dose responses; ASV IN substitutions that reduced DNA binding also reduced end fraying activity; and HIV-1 IN DNA binding and end fraying were both undetectable in the absence of a metal cofactor. Consistent with our previous results, end fraying is sequence-independent, suggesting that the DNA terminus per se is a major structural determinant for recognition. We conclude that frayed ends represent a functional intermediate in which DNA termini can be sampled for suitability for endonucleolytic processing.

Microsecond unfolding kinetics of sheep prion protein reveals an intermediate that correlates with susceptibility to classical scrapie.

The microsecond folding and unfolding kinetics of ovine prion proteins (ovPrP) were measured under various solution conditions. A fragment comprising residues 94-233 of the full-length ovPrP was studied for four variants with differing susceptibilities to classical scrapie in sheep. The observed biexponential unfolding kinetics of ovPrP provides evidence for an intermediate species. However, in contrast to previous results for human PrP, there is no evidence for an intermediate under refolding conditions. Global analysis of the kinetic data, based on a sequential three-state mechanism, quantitatively accounts for all folding and unfolding data as a function of denaturant concentration. The simulations predict that an intermediate accumulates under both folding and unfolding conditions, but is observable only in unfolding experiments because the intermediate is optically indistinguishable from the native state. The relative population of intermediates in two ovPrP variants, both transiently and under destabilizing equilibrium conditions, correlates with their propensities for classical scrapie. The variant susceptible to classical scrapie has a larger population of the intermediate state than the resistant variant. Thus, the susceptible variant should be favored to undergo the PrP(C) to PrP(Sc) conversion and oligomerization.

Autoinhibitory interactions between the PDZ2 and C-terminal domains in the scaffolding protein NHERF1.

Na(+)/H(+) exchanger regulatory factor (NHERF1) is a signaling adaptor protein comprising two PDZ domains and a C-terminal ezrin-binding (EB) motif. To understand the role of intramolecular interactions in regulating its binding properties, we characterized the complex between the second PDZ domain PDZ2 and the C-terminal 242-358 fragment of NHERF1 using NMR and fluorescence methods. NMR chemical shift and relaxation data implicate 11 C-terminal residues in binding and, together with a thermodynamic analysis of mutant proteins, indicate that the EB region becomes helical when bound to PDZ2. Both specific contacts between PDZ2 and EB as well as nonspecific interactions involving a 100-residue flexible linker contribute to stabilizing two structurally distinct closed conformations of NHERF1. The affinity of mutant proteins for an extrinsic ligand is inversely related to the helix-forming propensity of the EB motif. The findings provide a structural framework for understanding how autoinhibitory interactions modulated the binding properties of NHERF1.

Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data.

RATIONALE: Prognostic tools for aiding in the treatment of hospitalized COVID-19 patients could help improve outcome by identifying patients at higher or lower risk of severe disease. The study objective was to develop models to stratify patients by risk of severe outcomes during COVID-19 hospitalization using readily available information at hospital admission. METHODS: Hierarchical ensemble classification models were trained on a set of 229 patients hospitalized with COVID-19 to predict severe outcomes, including ICU admission, development of acute respiratory distress syndrome, or intubation, using easily attainable attributes including basic patient characteristics, vital signs at admission, and basic lab results collected at time of presentation. Each test stratifies patients into groups of increasing risk. An additional cohort of 330 patients was used for blinded, independent validation. Shapley value analysis evaluated which attributes contributed most to the models' predictions of risk. MAIN RESULTS: Test performance was assessed using precision (positive predictive value) and recall (sensitivity) of the final risk groups. All test cut-offs were fixed prior to blinded validation. In development and validation, the tests achieved precision in the lowest risk groups near or above 0.9. The proportion of patients with severe outcomes significantly increased across increasing risk groups. While the importance of attributes varied by test and patient, C-reactive protein, lactate dehydrogenase, and D-dimer were often found to be important in the assignment of risk. CONCLUSIONS: Risk of severe outcomes for patients hospitalized with COVID-19 infection can be assessed using machine learning-based models based on attributes routinely collected at hospital admission.

Detection of Hepatocellular Carcinoma in a High-Risk Population by a Mass Spectrometry-Based Test.

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat® classification algorithm. Machine learning techniques then classified each sample as "Cancer" or "No Cancer". Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.