De novo design of modular peptide-binding proteins by superhelical matching.

General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1-3. Here, inspired by natural and re-engineered protein-peptide systems4-11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.

De novo design of luciferases using deep learning.

De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence-structure relationships. Here we describe a deep-learning-based 'family-wide hallucination' approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M-1 s-1) comparable to that of native luciferases, but a much higher substrate specificity. The creation of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable generation of a wide range of luciferases and other enzymes.

De novo design of protein homodimers containing tunable symmetric protein pockets.

Function follows form in biology, and the binding of small molecules requires proteins with pockets that match the shape of the ligand. For design of binding to symmetric ligands, protein homo-oligomers with matching symmetry are advantageous as each protein subunit can make identical interactions with the ligand. Here, we describe a general approach to designing hyperstable C2 symmetric proteins with pockets of diverse size and shape. We first designed repeat proteins that sample a continuum of curvatures but have low helical rise, then docked these into C2 symmetric homodimers to generate an extensive range of C2 symmetric cavities. We used this approach to design thousands of C2 symmetric homodimers, and characterized 101 of them experimentally. Of these, the geometry of 31 were confirmed by small angle X-ray scattering and 2 were shown by crystallographic analyses to be in close agreement with the computational design models. These scaffolds provide a rich set of starting points for binding a wide range of C2 symmetric compounds.

Thermodynamically coupled biosensors for detecting neutralizing antibodies against SARS-CoV-2 variants.

We designed a protein biosensor that uses thermodynamic coupling for sensitive and rapid detection of neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in serum. The biosensor is a switchable, caged luciferase-receptor-binding domain (RBD) construct that detects serum-antibody interference with the binding of virus RBD to angiotensin-converting enzyme 2 (ACE-2) as a proxy for neutralization. Our coupling approach does not require target modification and can better distinguish sample-to-sample differences in analyte binding affinity and abundance than traditional competition-based assays.

Design of protein-binding proteins from the target structure alone.

The design of proteins that bind to a specific site on the surface of a target protein using no information other than the three-dimensional structure of the target remains a challenge1-5. Here we describe a general solution to this problem that starts with a broad exploration of the vast space of possible binding modes to a selected region of a protein surface, and then intensifies the search in the vicinity of the most promising binding modes. We demonstrate the broad applicability of this approach through the de novo design of binding proteins to 12 diverse protein targets with different shapes and surface properties. Biophysical characterization shows that the binders, which are all smaller than 65 amino acids, are hyperstable and, following experimental optimization, bind their targets with nanomolar to picomolar affinities. We succeeded in solving crystal structures of five of the binder-target complexes, and all five closely match the corresponding computational design models. Experimental data on nearly half a million computational designs and hundreds of thousands of point mutants provide detailed feedback on the strengths and limitations of the method and of our current understanding of protein-protein interactions, and should guide improvements of both. Our approach enables the targeted design of binders to sites of interest on a wide variety of proteins for therapeutic and diagnostic applications.

Detection of antibodies neutralizing historical and emerging SARS-CoV-2 strains using a thermodynamically coupled de novo biosensor system.

With global vaccination efforts against SARS-CoV-2 underway, there is a need for rapid quantification methods for neutralizing antibodies elicited by vaccination and characterization of their strain dependence. Here, we describe a designed protein biosensor that enables sensitive and rapid detection of neutralizing antibodies against wild type and variant SARS-CoV-2 in serum samples. More generally, our thermodynamic coupling approach can better distinguish sample to sample differences in analyte binding affinity and abundance than traditional competition based assays.

Multiple Simultaneous Mature Teratomas of the Spinal Cord in an Adult.

Teratomas of the spinal cord are rare tumors, particularly in adults, but there is an increasing body of literature documenting both their diagnosis and successful treatment with surgical resection. However, to date, the literature has largely characterized spinal teratomas as single solitary lesions. Here, we report on an adult patient who presented with signs of progressive lower extremity weakness. Imaging demonstrated two simultaneous lesions of the spine, an extramedullary lesion in the upper thoracic region and an intramedullary lesion in the mid-thoracic region. Both lesions were resected and pathologically determined to be mature spinal teratomas. To our knowledge, this is only the second report of this tumor presenting simultaneously at more than one location in the spine and the first time it has presented both as an intra-axial and extra-axial lesion. Our results suggest that the presence of more than one simultaneous lesion does not necessarily increase the risk of a more aggressive immature pathology.

Repurposing antimicrobial stewardship tools in the electronic medical record for the management of COVID-19 patients.

During the COVID-19 pandemic, the antimicrobial stewardship module in our electronic medical record was reconfigured for the management of COVID-19 patients. This change allowed our subspecialist providers to review charts quickly to optimize potential therapy and management during the patient surge.

A randomized trial of decision support for tobacco dependence treatment in an inpatient electronic medical record: clinical results.

BACKGROUND: Smokers usually abstain from tobacco while hospitalized but relapse after discharge. Inpatient interventions may encourage sustained quitting. We previously demonstrated that a decision support tool embedded in an electronic health record (EHR) improved physicians' treatment of hospitalized smokers. This report describes the effect on quit rates of this decision support tool and order set for hospitalized smokers. METHODS: In a single hospital system, 254 physicians were randomized 1:1 to receive a decision support tool and order set, embedded in the EHR. When an adult patient was admitted to a medical service, an electronic alert appeared if current smoking was recorded in the EHR. For physicians receiving the intervention, the alert linked to an order set for tobacco treatment medications and electronic referral to the state tobacco quitline. Additionally, "Tobacco Use Disorder" was added to the patient's problem list, and a secure message was sent to the patient's primary care provider (PCP). In the control arm, no alert appeared. Patients were contacted by phone at 1, 6, and 12 months; those reporting tobacco abstinence at 12 months were asked to return to measure exhaled carbon monoxide. Generalized estimating equations were used to model the data. RESULTS: From 2013 to 2016, the alert fired for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication, populate the problem list with tobacco use disorder, refer to the quitline, and notify the patient's PCP. In a subset of 1044 patients recruited for intensive follow-up, one-year quit rates for intervention and control patients were, respectively, 11.5% and 11.6%, (p = 0.94), after controlling for age, sex, race, ethnicity, and insurance. Similarly, there were no differences in 1- and 6-month quit rates. CONCLUSIONS: Although we were able to improve processes of care, long-term tobacco quit rates were unchanged. This likely reflects, in part, the need for sustained quitting interventions, and higher-than-expected quit rates in controls. Future enhancements should improve prescription of medications for smoking cessation at discharge, engagement of primary care providers, and perhaps direct engagement of patients in a more longitudinal approach. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01691105 . Registered on September 12, 2012.

Design and implementation of decision support for tobacco dependence treatment in an inpatient electronic medical record: a randomized trial.

Tobacco dependence treatment for hospitalized smokers results in long-term cessation if treatment continues at least 30 days post-discharge. Health information technology may facilitate ongoing tobacco dependence treatment after hospital discharge. To describe the use and impact of a new decision support tool and order set for inpatient physicians, addressing tobacco dependence treatment for hospitalized smokers, embedded in an electronic health record (EHR). In a cluster-randomized trial, 254 physicians were randomized (1:1) to either receive or not receive the decision support tool and order set, which were embedded in the Epic (Madison, WI) EHR used at 2 hospitals in a single city. When an adult patient was admitted to a medical service, an electronic alert appeared if the patient was coded in the EHR as a smoker. For physicians randomized to the intervention, the alert linked to an order set to prescribe tobacco treatment medications and refer the patient to the state tobacco quitline. Additionally, "tobacco use disorder" was added to the patient's problem list, and an e-mail was sent to the patient's primary care provider (PCP). In the control arm, an alert fired with no screen visibility. Generalized estimating equations were used to model the data. Since August 2013, the alert has appeared for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication (35 vs. 29%, P < 0.0001), populate the problem list with tobacco use disorder (41 vs. 2%, P < 0.0001), and make a referral to the state smokers' quitline (30 vs. 0%, P < 0.0001). In addition, intervention physicians sent an e-mail to the patient's PCP 4152 (99%) times. Designing and implementing an order set and alert for tobacco treatment in an EHR is feasible and helps physicians place more orders for tobacco treatment medication, referrals to the state smokers' quitline, and e-mails to patients' PCPs. Data on cessation outcomes are pending. TRIAL REGISTRATION: www.ClinicalTrials.gov (NCT01691105).

A human neurodevelopmental model for Williams syndrome.

Williams syndrome is a genetic neurodevelopmental disorder characterized by an uncommon hypersociability and a mosaic of retained and compromised linguistic and cognitive abilities. Nearly all clinically diagnosed individuals with Williams syndrome lack precisely the same set of genes, with breakpoints in chromosome band 7q11.23 (refs 1-5). The contribution of specific genes to the neuroanatomical and functional alterations, leading to behavioural pathologies in humans, remains largely unexplored. Here we investigate neural progenitor cells and cortical neurons derived from Williams syndrome and typically developing induced pluripotent stem cells. Neural progenitor cells in Williams syndrome have an increased doubling time and apoptosis compared with typically developing neural progenitor cells. Using an individual with atypical Williams syndrome, we narrowed this cellular phenotype to a single gene candidate, frizzled 9 (FZD9). At the neuronal stage, layer V/VI cortical neurons derived from Williams syndrome were characterized by longer total dendrites, increased numbers of spines and synapses, aberrant calcium oscillation and altered network connectivity. Morphometric alterations observed in neurons from Williams syndrome were validated after Golgi staining of post-mortem layer V/VI cortical neurons. This model of human induced pluripotent stem cells fills the current knowledge gap in the cellular biology of Williams syndrome and could lead to further insights into the molecular mechanism underlying the disorder and the human social brain.

Reasoning About Trust Among Individuals With Williams Syndrome.

The present study examines whether individuals with Williams syndrome (WS) might indiscriminately trust in others, as is suggested by their strong tendency to approach and interact with strangers. To assess this possibility, adults with WS (N=22) and typical development (N=25) were asked to reason about the trustworthiness of people who lie to avoid getting in trouble versus to avoid hurting others' feelings. Findings indicated that participants with WS distrusted both types of liars and made little distinction between them. These results suggest that the high level of social approach behavior in individuals with WS cannot be explained in terms of indiscriminate trust.