Installing hydrolytic activity into a completely <i>de novo </i>protein framework

The design of enzyme-like catalysts tests our understanding of sequence-to-structure/function relationships in proteins. Here we install hydrolytic activity predictably into a completely de novo and thermostable α-helical barrel, which comprises seven helices arranged around an accessible channel. We show that the lumen of the barrel accepts 21 mutations to functional polar residues. The resulting variant, which has cysteine–histidine–glutamic acid triads on each helix, hydrolyses p-nitrophenyl acetate with catalytic efficiencies that match the most-efficient redesigned hydrolases based on natural protein scaffolds. This is the first report of a functional catalytic triad engineered into a de novo protein framework. The flexibility of our system also allows the facile incorporation of unnatural side chains to improve activity and probe the catalytic mechanism. Such a predictable and robust construction of truly de novo biocatalysts holds promise for applications in chemical and biochemical synthesis

The evolution of multiple active site configurations in a designed enzyme

Developments in computational chemistry, bioinformatics, and laboratory evolution have facilitated the de novo design and catalytic optimization of enzymes. Besides creating useful catalysts, the generation and iterative improvement of designed enzymes can provide valuable insight into the interplay between the many phenomena that have been suggested to contribute to catalysis. In this work, we follow changes in conformational sampling, electrostatic preorganization, and quantum tunneling along the evolutionary trajectory of a designed Kemp eliminase. We observe that in the Kemp Eliminase KE07, instability of the designed active site leads to the emergence of two additional active site configurations. Evolutionary conformational selection then gradually stabilizes the most efficient configuration, leading to an improved enzyme. This work exemplifies the link between conformational plasticity and evolvability and demonstrates that residues remote from the active sites of enzymes play crucial roles in controlling and shaping the active site for efficient catalysis

Plant-symbiotic fungi as chemical engineers: multi-genome analysis of the Clavicipitaceae reveals dynamics of alkaloid Loci

The fungal family Clavicipitaceae includes plant symbionts and parasites that produce several psychoactive and bioprotective alkaloids. The family includes grass symbionts in the epichloae clade (Epichloë and Neotyphodium species), which are extraordinarily diverse both in their host interactions and in their alkaloid profiles. Epichloae produce alkaloids of four distinct classes, all of which deter insects, and some—including the infamous ergot alkaloids—have potent effects on mammals. The exceptional chemotypic diversity of the epichloae may relate to their broad range of host interactions, whereby some are pathogenic and contagious, others are mutualistic and vertically transmitted (seed-borne), and still others vary in pathogenic or mutualistic behavior. We profiled the alkaloids and sequenced the genomes of 10 epichloae, three ergot fungi (Claviceps species), a morning-glory symbiont (Periglandula ipomoeae), and a bamboo pathogen (Aciculosporium take), and compared the gene clusters for four classes of alkaloids. Results indicated a strong tendency for alkaloid loci to have conserved cores that specify the skeleton structures and peripheral genes that determine chemical variations that are known to affect their pharmacological specificities. Generally, gene locations in cluster peripheries positioned them near to transposon-derived, AT-rich repeat blocks, which were probably involved in gene losses, duplications, and neofunctionalizations. The alkaloid loci in the epichloae had unusual structures riddled with large, complex, and dynamic repeat blocks. This feature was not reflective of overall differences in repeat contents in the genomes, nor was it characteristic of most other specialized metabolism loci. The organization and dynamics of alkaloid loci and abundant repeat blocks in the epichloae suggested that these fungi are under selection for alkaloid diversification. We suggest that such selection is related to the variable life histories of the epichloae, their protective roles as symbionts, and their associations with the highly speciose and ecologically diverse cool-season grasses

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Automatic multi-seed detection for MR breast image segmentation

In this paper an automatic multi-seed detection method for magnetic resonance (MR) breast image segmentation is presented. The proposed method consists of three steps: (1) pre-processing step to locate three regions of interest (axillary and sternal regions); (2) processing step to detect maximum concavity points for each region of interest; (3) breast image segmentation step. Traditional manual segmentation methods require radiological expertise and they usually are very tiring and time-consuming. The approach is fast because the multi-seed detection is based on geometric properties of the ROI. When the maximum concavity points of the breast regions have been detected, region growing and morphological transforms complete the segmentation of breast MR image. In order to create a Gold Standard for method effectiveness and comparison, a dataset composed of 18 patients is selected, accordingly to three expert radiologists of University of Palermo Policlinico Hospital (UPPH). Each patient has been manually segmented. The proposed method shows very encouraging results in terms of statistical metrics (Sensitivity: 95.22%; Specificity: 80.36%; Precision: 98.05%; Accuracy: 97.76%; Overlap: 77.01%) and execution time (4.23 s for each slice)

Early Marine Migration Patterns of Wild Coastal Cutthroat Trout (Oncorhynchus clarki clarki), Steelhead Trout (Oncorhynchus mykiss), and Their Hybrids

Hybridization between coastal cutthroat trout (Oncorhynchus clarki clarki) and steelhead or rainbow trout (Oncorhynchus mykiss) has been documented in several streams along the North American west coast. The two species occupy similar freshwater habitats but the anadromous forms differ greatly in the duration of marine residence and migration patterns at sea. Intermediate morphological, physiological, and performance traits have been reported for hybrids but little information has been published comparing the behavior of hybrids to the pure species.This study used acoustic telemetry to record the movements of 52 cutthroat, 42 steelhead x cutthroat hybrids, and 89 steelhead smolts, all wild, that migrated from Big Beef Creek into Hood Canal (Puget Sound, Washington). Various spatial and temporal metrics were used to compare the behavior of the pure species to their hybrids. Median hybrid residence time, estuary time, and tortuosity values were intermediate compared to the pure species. The median total track distance was greater for hybrids than for either cutthroat or steelhead. At the end of each track, most steelhead (80%) were located near or north of the Hood Canal, as expected for this seaward migrating species, whereas most cutthroat (89%) were within 8 kilometers of the estuary. Most hybrids (70%) were detected leaving Hood Canal, though a substantial percentage (20%) remained near the Big Beef Creek estuary. More hybrids (7.5%) than pure cutthroat (4.5%) or steelhead (0.0%) were last detected in the southern reaches of Hood Canal.Given the similarity in freshwater ecology between the species, differences in marine ecology may play an important role in maintaining species integrity in areas of sympatry

Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case

Interoperability between data sources, one of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management, can enable multi-modality research. The purpose of our study was to investigate the potential for interoperability between an imaging resource, the Medical Imaging and Data Resource Center (MIDRC), and a clinical record resource, the National COVID Cohort Collaborative (N3C). The use case was the prediction of COVID-19 severity, defined as evidence for invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice in the N3C clinical record. Patient-level matching between MIDRC and N3C was identified using Privacy Preserving Record Linking via an honest broker. We identified positive COVID-19 tests and chest radiograph procedures in N3C and used the interval between them to identify images with matching intervals in MIDRC. Of the 236 patients (306 unique images) meeting initial inclusion criteria in MIDRC, 117 patients (and 139 unique images) remained after date interval matching between repositories and exclusion of patients with multiple potential matches. The Charlson Comorbidity Index (CCI) and the minimum mean arterial pressure (MAP) on the day of the chest radiograph were used as clinical indicators. The AUC in the task of predicting severe COVID-19 was evaluated using the computer-extracted imaging index alone (MIDRC), clinical indicators alone (N3C), and both together. Our model combining imaging and clinical indicators (CCI over 2 and MAP below 70) to predict severe COVID had an AUC of 0.73 (95% CI 0.62–0.84), and the models including imaging or clinical indicators alone were 0.67 (95% CI 0.56–0.79) and 0.69 (95% CI 0.59–0.80), respectively. This study highlights the potential for cross-platform data sharing to facilitate future multi-modality research and broader collaborative studies