Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

RosettaCM for antibodies with very long HCDR3s and low template availability.

Antibody-antigen co-crystal structures are a valuable resource for the fundamental understanding of antibody-mediated immunity. Determination of structures with antibodies in complex with their antigens, however, is a laborious task without guarantee of success. Therefore, homology modeling of antibodies and docking to their respective antigens has become a very important technique to drive antibody and vaccine design. The quality of the antibody modeling process is critical for the success of these endeavors. Here, we compare different computational protocols for predicting antibody structure from sequence in the biomolecular modeling software Rosetta-all of which use multiple existing antibody structures to guide modeling. Specifically, we compare protocols developed solely to predict antibody structure (RosettaAntibody, AbPredict) with a universal homology modeling protocol (RosettaCM). Following recent advances in homology modeling with multiple templates simultaneously, we propose that the use of multiple templates over the same antibody regions may improve modeling performance. To evaluate whether multi-template comparative modeling with RosettaCM can improve the modeling accuracy of antibodies over existing methods, this study compares the performance of the three modeling algorithms when modeling human antibodies taken from antibody-antigen co-crystal structures. In these benchmarking experiments, RosettaCM outperformed other methods when modeling antibodies with long HCDR3s and few available templates.

Prepectoral versus Submuscular Implant-Based Breast Reconstruction: A Matched-Pair Comparison of Outcomes.

BACKGROUND: Implant-based breast reconstruction is the most common reconstructive approach after mastectomy. Prepectoral implants offer advantages over submuscular implants, such as less animation deformity, pain, weakness, and postradiation capsular contracture. However, clinical outcomes after prepectoral reconstruction are debated. The authors performed a matched-cohort analysis of outcomes after prepectoral and submuscular reconstruction at a large academic medical center. METHODS: Patients treated with implant-based breast reconstruction after mastectomy from January of 2018 through October of 2021 were retrospectively reviewed. Patients were propensity score exact matched to control demographic, preoperative, intraoperative, and postoperative differences. Outcomes assessed included surgical-site occurrences, capsular contracture, and explantation of either expander or implant. Subanalysis was done on infections and secondary reconstructions. RESULTS: A total of 634 breasts were included (prepectoral, 197; submuscular, 437). A total of 292 breasts were matched (146 prepectoral:146 submuscular) and analyzed for clinical outcomes. Prepectoral reconstructions were associated with greater rates of SSI (prepectoral, 15.8%; submuscular, 3.4%; P < 0.001), seroma (prepectoral, 26.0%; submuscular, 10.3%; P < 0.001), and explantation (prepectoral, 23.3%; submuscular, 4.8%; P < 0.001). Subanalysis of infections revealed that prepectoral implants have shorter time to infection, deeper infections, and more Gram-negative infections, and are more often treated surgically (all P < 0.05). There have been no failures of secondary reconstructions after explantation in the entire population at a mean follow-up of 20.1 months. CONCLUSIONS: Prepectoral implant-based breast reconstruction is associated with higher rates of infection, seroma, and explantation compared with submuscular reconstructions. Infections of prepectoral implants may need different antibiotic management to avoid explantation. Secondary reconstruction after explantation can result in long-term success. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.

How do implants overlying the spine influence "The Law of Diminishing Returns" in early-onset scoliosis patients?

PURPOSE: The "law of diminishing returns" (LODR) in early-onset scoliosis (EOS) is well-known. We hypothesized that previously observed variations between constructs may be related to the lateral distance that each construct lies from the spine. We therefore sought to determine whether the curve magnitude improvement and spinal length gains for distraction-based constructs in EOS are positively correlated with the collinearity of the spine and the convex-sided implant on posteroanterior radiographs. METHODS: A prospectively-collected, multicenter EOS registry was queried for all patients who underwent non-fusion, distraction-based instrumentation surgery. Post-index radiographs were graded from 1 to 5 based on amount of overlap between the convex-sided rod and the apical vertebra. Grade 1: convex rod is lateral to convex-sided pedicle; Grade 2: overlaps the convex-sided pedicle; Grade 3: lies between pedicles; Grade 4: overlaps concave-sided pedicle; Grade 5: medial to concave-sided pedicle. ANOVA assessed the correlations between post-index overlap grade and change in (a) curve magnitude and (b) T1-T12 height. Multivariable regression modeling further assessed these associations. RESULTS: 284 patients met all selection criteria and were included. On ANOVA, post-index grade was associated with curve magnitude (p <0.001) and T1-12 height (p = 0.028) change. Better curve correction and height change were associated with higher grade. On regression modeling, curve correction (R = 0.574) and T1-T12 height change (R = 0.339) remained significantly associated with grade when controlling for time, anchor locations, age, underlying diagnosis, and pre-index curve magnitude. CONCLUSION: More apical overlap by the convex rod was associated with better spinal deformity control and improved height gain. LEVEL OF EVIDENCE III: Therapeutic.

Illness Severity of Children Admitted to the PICU From Referring Emergency Departments.

OBJECTIVES: To compare patient factors and outcomes among children admitted to PICUs from referring versus children's hospital emergency departments (EDs). METHODS: Pediatric patients (<19 years old) admitted to PICUs from referring and children's hospital EDs from July 1, 2011 to June 30, 2013. We compared demographic and clinical factors, including severity of illness as measured by a recalibrated Pediatric Index of Mortality, version 2 score. RESULTS: Of 80 045 children from 109 PICUs, 35.6% were admitted from referring EDs and 64.4% were admitted from children's hospital EDs. Children from referring EDs had higher illness severity (Pediatric Index of Mortality, version 2-predicted risk of mortality, 3.1% vs 2.2%, P < .001), were more likely to be mechanically ventilated within their first hour in the PICU (28.4% vs 23.4%, P < .001), and had higher observed mortality (3.3% vs 2.1%, P < .001). Once adjusted for illness severity and other confounders in a multivariable logistic regression model, there was no difference in the odds of mortality between children from referring and children's hospital EDs (odds ratio: 0.90; 95% confidence interval: 0.79 to 1.02, P = .09) CONCLUSIONS: Children transferred to PICUs from referring EDs had higher illness severity on arrival compared with children admitted from children's hospital EDs. Variations in patient selection for transfer or pretransfer treatment at referring EDs may contribute to the greater illness severity of transferred children. Referring hospitals may benefit from leveraging existing resources to improve patient stabilization before transfer.