The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.

BackgroundPatients who present to an emergency department with respiratory symptoms are often conservatively triaged in favour of hospitalization. We sought to determine if an inflammatory biomarker panel that identifies the host response better predicts hospitalization in order to improve the precision of clinical decision-making in the emergency department.Patients and MethodsFrom April 2020 to March 2021, plasma samples of 641 patients with symptoms of respiratory illness were collected from emergency departments in an international multicentre study: Canada (n=310), Italy (n=131), and Brazil (n=200). Patients were followed prospectively for 28 days. Subgroup analysis was conducted on confirmed COVID-19 patients (n=245). An inflammatory profile was determined using a rapid, 50-minute, biomarker panel: Rapid Acute Lung Injury Diagnostic (RALI-Dx), which measures IL-6, IL-8, IL-10, sTNFR1, and sTREM1.ResultsRALI-Dx biomarkers were significantly elevated in patients who required hospitalization across all three sites. A machine learning algorithm that was applied to predict hospitalization using RALI-Dx biomarkers had an area under the receiver operating characteristic curve of 76±6% (Canada), 84±4% (Italy), and 86±3% (Brazil). Model performance in COVID-19 patients was 82±3% and 87±7% for patients with a confirmed pneumonia diagnosis.ConclusionsThe rapid diagnostic biomarker panel accurately identified the need for inpatient care in patients presenting with respiratory symptoms, including COVID-19. The RALI-Dx test is broadly and easily applicable across many jurisdictions and represents an important diagnostic adjunct to advance emergency department decision-making protocols.

Ex vivo lung perfusion (EVLP) is a data-intensive platform used for the assessment of isolated lungs outside the body for transplantation; however, the integration of artificial intelligence to rapidly interpret the large constellation of clinical data generated during ex vivo assessment remains an unmet need. We developed a machine-learning model, termed InsighTx, to predict post-transplant outcomes using n = 725 EVLP cases. InsighTx model AUROC (area under the receiver operating characteristic curve) was 79 ± 3%, 75 ± 4%, and 85 ± 3% in training and independent test datasets, respectively. Excellent performance was observed in predicting unsuitable lungs for transplantation (AUROC: 90 ± 4%) and transplants with good outcomes (AUROC: 80 ± 4%). In a retrospective and blinded implementation study by EVLP specialists at our institution, InsighTx increased the likelihood of transplanting suitable donor lungs [odds ratio=13; 95% CI:4-45] and decreased the likelihood of transplanting unsuitable donor lungs [odds ratio=0.4; 95%CI:0.16–0.98]. Herein, we provide strong rationale for the adoption of machine-learning algorithms to optimize EVLP assessments and show that InsighTx could potentially lead to a safe increase in transplantation rates.