Generalized biomolecular modeling and design with RoseTTAFold All-Atom.

Deep-learning methods have revolutionized protein structure prediction and design but are presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which combines a residue-based representation of amino acids and DNA bases with an atomic representation of all other groups to model assemblies that contain proteins, nucleic acids, small molecules, metals, and covalent modifications, given their sequences and chemical structures. By fine-tuning on denoising tasks, we developed RFdiffusion All-Atom (RFdiffusionAA), which builds protein structures around small molecules. Starting from random distributions of amino acid residues surrounding target small molecules, we designed and experimentally validated, through crystallography and binding measurements, proteins that bind the cardiac disease therapeutic digoxigenin, the enzymatic cofactor heme, and the light-harvesting molecule bilin.

Blueprinting extendable nanomaterials with standardized protein blocks.

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies, in comparison, has been much more complex, largely owing to the irregular shapes of protein structures1. Here we describe extendable linear, curved and angled protein building blocks, as well as inter-block interactions, that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight 'train track' assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not previously been possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank three-dimensional canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to 'back of an envelope' architectural blueprints.

Accurate prediction of protein-nucleic acid complexes using RoseTTAFoldNA.

Protein-RNA and protein-DNA complexes play critical roles in biology. Despite considerable recent advances in protein structure prediction, the prediction of the structures of protein-nucleic acid complexes without homology to known complexes is a largely unsolved problem. Here we extend the RoseTTAFold machine learning protein-structure-prediction approach to additionally predict nucleic acid and protein-nucleic acid complexes. We develop a single trained network, RoseTTAFoldNA, that rapidly produces three-dimensional structure models with confidence estimates for protein-DNA and protein-RNA complexes. Here we show that confident predictions have considerably higher accuracy than current state-of-the-art methods. RoseTTAFoldNA should be broadly useful for modeling the structure of naturally occurring protein-nucleic acid complexes, and for designing sequence-specific RNA and DNA-binding proteins.

Computational design of sequence-specific DNA-binding proteins.

Sequence-specific DNA-binding proteins (DBPs) play critical roles in biology and biotechnology, and there has been considerable interest in the engineering of DBPs with new or altered specificities for genome editing and other applications. While there has been some success in reprogramming naturally occurring DBPs using selection methods, the computational design of new DBPs that recognize arbitrary target sites remains an outstanding challenge. We describe a computational method for the design of small DBPs that recognize specific target sequences through interactions with bases in the major groove, and employ this method in conjunction with experimental screening to generate binders for 5 distinct DNA targets. These binders exhibit specificity closely matching the computational models for the target DNA sequences at as many as 6 base positions and affinities as low as 30-100 nM. The crystal structure of a designed DBP-target site complex is in close agreement with the design model, highlighting the accuracy of the design method. The designed DBPs function in both Escherichia coli and mammalian cells to repress and activate transcription of neighboring genes. Our method is a substantial step towards a general route to small and hence readily deliverable sequence-specific DBPs for gene regulation and editing.

Blueprinting expandable nanomaterials with standardized protein building blocks.

A wooden house frame consists of many different lumber pieces, but because of the regularity of these building blocks, the structure can be designed using straightforward geometrical principles. The design of multicomponent protein assemblies in comparison has been much more complex, largely due to the irregular shapes of protein structures 1 . Here we describe extendable linear, curved, and angled protein building blocks, as well as inter-block interactions that conform to specified geometric standards; assemblies designed using these blocks inherit their extendability and regular interaction surfaces, enabling them to be expanded or contracted by varying the number of modules, and reinforced with secondary struts. Using X-ray crystallography and electron microscopy, we validate nanomaterial designs ranging from simple polygonal and circular oligomers that can be concentrically nested, up to large polyhedral nanocages and unbounded straight "train track" assemblies with reconfigurable sizes and geometries that can be readily blueprinted. Because of the complexity of protein structures and sequence-structure relationships, it has not been previously possible to build up large protein assemblies by deliberate placement of protein backbones onto a blank 3D canvas; the simplicity and geometric regularity of our design platform now enables construction of protein nanomaterials according to "back of an envelope" architectural blueprints.

Model of anesthesia care that combines anesthesiologists and registered nurses during cataract surgery.

PURPOSE: To determine the safety and practicality of a combined anesthesiologist and registered nurse model of anesthesia care in cataract surgery. SETTING: Mayo Clinic, Rochester, Minnesota, USA. DESIGN: Case series. METHODS: This retrospective review comprised consecutive patients having phacoemulsification cataract surgery and peribulbar injection anesthesia combined with propofol intravenous sedation between August 1, 2004, and July 31, 2006. In all cases, anesthesiologist-supervised intravenous propofol sedation during injection anesthesia was followed by registered nurse observation for the remainder of the surgery. Outcome measures were the rate of subsequent anesthesiologist intervention, intraoperative complications, and associated risk factors. Logistic regression models were used to estimate risk for anesthesiologist intervention. RESULTS: The study reviewed 3656 cases. There were no serious medical complications leading to postoperative hospitalization. Fifty-four cases (1.5%) required subsequent intraoperative anesthesiologist intervention. Evaluation of systolic hypertension (40 of 54 cases, 74%) was the most common reason for anesthesiologist intervention. There was no correlation between anesthesiologist intervention and patient age or sex (P=.77 and P=.41, respectively). The risk for anesthesiologist intervention increased 2.2-fold for every 1 unit increase in the American Society of Anesthesiologists Physical Status score (P=.007). CONCLUSION: The monitoring of cataract surgery patients by registered nurses after anesthesiologist-supervised intravenous propofol sedation during injection anesthesia was associated with very low complication and anesthesiologist intervention rates.

Clostridial sacroiliitis in a patient with fecal incontinence: a case report and review of the literature.

INTRODUCTION: Image-guided sacroiliac joint injections are frequently employed for both diagnostic and therapeutic relief of low back pain. CASE REPORT: An 83-year-old male with chronic lumbrosacral pain previously responsive to right sacroliac joint injections presented for repeat injection. His medical history included Parkinsonism and stool incontinence. Forty-two hours after the injection, he developed fever, dyspnea, and crepitus on the right buttock and thigh. Surgical debridement was recommended, but the family wished for comfort care only. The patient died hours later. The autopsy revealed Gram positive bacilli consistent with Clostridial myonecrosis. DISCUSSION: Pyogenic sacroiliitis is rare and usually occurs in the setting of trauma, drug abuse, or extraspinal infections. Joint infections with Clostridium have been reported after traumatic events including puncture, surgery, and abrasions. Clostridium spores are resistant to chemical preparations used for skin sterilization and require high heat for destruction. Possible practice guidelines with patients that are stool incontinent include mechanical wash prior to sterile preparation and placement of an occlusive sterile dressing after injection to prevent stool contamination of the needle puncture site. As with all rare complications, large scale studies are needed to better identify risk factors to formulate practice management strategies.