Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions.

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure are long-standing goals with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost-intensive. Computational methods have potential to accelerate epitope predictions; however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologues. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis were ineffective in distinguishing between two proposed binding models, parallel and perpendicular. However, our integrated approach, utilizing dynamic network analysis, demonstrated that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols, including cross-linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Conversely, AlphaFold3 failed to predict a structure bound in the perpendicular pose, highlighting the necessity for exploratory research in the search for binding epitopes and challenging the notion that AI-generated protein structures can be accepted without scrutiny. Our research underscores the potential of employing dynamic network analysis to enhance AI-based structure predictions for more accurate identification of protein-protein interaction interfaces.

Distance functions of carabids in crop fields depend on functional traits, crop type and adjacent habitat: a synthesis.

Natural pest and weed regulation are essential for agricultural production, but the spatial distribution of natural enemies within crop fields and its drivers are mostly unknown. Using 28 datasets comprising 1204 study sites across eight Western and Central European countries, we performed a quantitative synthesis of carabid richness, activity densities and functional traits in relation to field edges (i.e. distance functions). We show that distance functions of carabids strongly depend on carabid functional traits, crop type and, to a lesser extent, adjacent non-crop habitats. Richness of both carnivores and granivores, and activity densities of small and granivorous species decreased towards field interiors, whereas the densities of large species increased. We found strong distance decays in maize and vegetables whereas richness and densities remained more stable in cereals, oilseed crops and legumes. We conclude that carabid assemblages in agricultural landscapes are driven by the complex interplay of crop types, adjacent non-crop habitats and further landscape parameters with great potential for targeted agroecological management. In particular, our synthesis indicates that a higher edge-interior ratio can counter the distance decay of carabid richness per field and thus likely benefits natural pest and weed regulation, hence contributing to agricultural sustainability.

Cerebrospinal Creatine Kinase BB Isoenzyme: A Biomarker for Predicting Outcome After Cardiac Arrest.

BACKGROUND: Cerebrospinal fluid creatine kinase BB isoenzyme (CSF CK-BB) after cardiac arrest (CA) has been shown to have a high positive predictive value for poor neurological outcome, but it has not been evaluated in the setting of targeted temperature management (TTM) and modern CA care. We aimed to evaluate CSF CK-BB as a prognostic biomarker after CA. METHODS: We performed a retrospective cohort study of patients with CA admitted between 2010 and 2020 to a three-hospital health system who remained comatose and had CSF CK-BB assayed between 36 and 84 h after CA. We examined the proportion of patients at hospital discharge who achieved favorable or intermediate neurological outcome, defined as Cerebral Performance Category score of 1-3, compared with those with poor outcome (Cerebral Performance Category score 4-5) for various CSF CK-BB thresholds. We also evaluated additive value of bilateral absence of somatosensory evoked potentials (SSEPs). RESULTS: Among 214 eligible patients, the mean age was 54.7 ± 4.8 years, 72% of patients were male, 33% were nonwhite, 17% had shockable rhythm, 90% were out-of-hospital CA, and 83% received TTM. A total of 19 (9%) awakened. CSF CK-BB ≥ 230 U/L predicted a poor outcome at hospital discharge, with a specificity of 100% (95% confidence interval [CI] 82-100%) and sensitivity of 69% (95% CI 62-76%). When combined with bilaterally absent N20 response on SSEP, specificity remained 100% while sensitivity increased to 80% (95% CI 73-85%). Discordant CK-BB and SSEP findings were seen in 13 (9%) patients. CONCLUSIONS: Cerebrospinal fluid creatine kinase BB isoenzyme levels accurately predicted poor neurological outcome among CA survivors treated with TTM. The CSF CK-BB cutoff of 230 U/L optimizes sensitivity to 69% while maintaining a specificity of 100%. CSF CK-BB could be a useful addition to multimodal neurological prognostication after CA.

Iterative Machine Learning for Classification and Discovery of Single-Molecule Unfolding Trajectories from Force Spectroscopy Data.

We report the application of machine learning techniques to expedite classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression, and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) learning where a user classifies a small number of repeatable unfolding patterns encoded as images, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multidomain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways that were undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis and generate new insights in protein biophysics.

Enzyme Cascade with Horseradish Peroxidase Readout for High-Throughput Screening and Engineering of Human Arginase-1.

We report an enzyme cascade with horseradish peroxidase-based readout for screening human arginase-1 (hArg1) activity. We combined the four enzymes hArg1, ornithine decarboxylase, putrescine oxidase, and horseradish peroxidase in a reaction cascade that generated colorimetric or fluorescent signals in response to hArg1 activity and used this cascade to assay wild-type and variant hArg1 sequences as soluble enzymes and displayed on the surface of Escherichia coli. We screened a curated 13-member hArg1 library covering mutations that modified the electrostatic environment surrounding catalytic residues D128 and H141, and identified the R21E variant with a 13% enhanced catalytic turnover rate compared to wild type. Our scalable one-pot single-step arginase assay with continuous kinetic readout is amenable to high-throughput screening and directed evolution of arginase libraries and testing drug candidates for arginase inhibition.

Protocol of the Comparison of Intravesical Therapy and Surgery as Treatment Options (CISTO) study: a pragmatic, prospective multicenter observational cohort study of recurrent high-grade non-muscle invasive bladder cancer.

BACKGROUND: Bladder cancer poses a significant public health burden, with high recurrence and progression rates in patients with non-muscle-invasive bladder cancer (NMIBC). Current treatment options include bladder-sparing therapies (BST) and radical cystectomy, both with associated risks and benefits. However, evidence supporting optimal management decisions for patients with recurrent high-grade NMIBC remains limited, leading to uncertainty for patients and clinicians. The CISTO (Comparison of Intravesical Therapy and Surgery as Treatment Options) Study aims to address this critical knowledge gap by comparing outcomes between patients undergoing BST and radical cystectomy. METHODS: The CISTO Study is a pragmatic, prospective observational cohort trial across 36 academic and community urology practices in the US. The study will enroll 572 patients with a diagnosis of recurrent high-grade NMIBC who select management with either BST or radical cystectomy. The primary outcome is health-related quality of life (QOL) at 12 months as measured with the EORTC-QLQ-C30. Secondary outcomes include bladder cancer-specific QOL, progression-free survival, cancer-specific survival, and financial toxicity. The study will also assess patient preferences for treatment outcomes. Statistical analyses will employ targeted maximum likelihood estimation (TMLE) to address treatment selection bias and confounding by indication. DISCUSSION: The CISTO Study is powered to detect clinically important differences in QOL and cancer-specific survival between the two treatment approaches. By including a diverse patient population, the study also aims to assess outcomes across the following patient characteristics: age, gender, race, burden of comorbid health conditions, cancer severity, caregiver status, social determinants of health, and rurality. Treatment outcomes may also vary by patient preferences, health literacy, and baseline QOL. The CISTO Study will fill a crucial evidence gap in the management of recurrent high-grade NMIBC, providing evidence-based guidance for patients and clinicians in choosing between BST and radical cystectomy. The CISTO study will provide an evidence-based approach to identifying the right treatment for the right patient at the right time in the challenging clinical setting of recurrent high-grade NMIBC. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03933826. Registered on May 1, 2019.

Mapping Mechanostable Pulling Geometries of a Therapeutic Anticalin/CTLA-4 Protein Complex.

We used single-molecule AFM force spectroscopy (AFM-SMFS) in combination with click chemistry to mechanically dissociate anticalin, a non-antibody protein binding scaffold, from its target (CTLA-4), by pulling from eight different anchor residues. We found that pulling on the anticalin from residue 60 or 87 resulted in significantly higher rupture forces and a decrease in koff by 2-3 orders of magnitude over a force range of 50-200 pN. Five of the six internal anchor points gave rise to complexes significantly more stable than N- or C-terminal anchor points, rupturing at up to 250 pN at loading rates of 0.1-10 nN s-1. Anisotropic network modeling and molecular dynamics simulations helped to explain the geometric dependency of mechanostability. These results demonstrate that optimization of attachment residue position on therapeutic binding scaffolds can provide large improvements in binding strength, allowing for mechanical affinity maturation under shear stress without mutation of binding interface residues.

Prediction of postoperative respiratory depression and respiratory complications in patients on preoperative methadone.

PURPOSE: We developed prediction models for postoperative respiratory depression and respiratory complications for 958 patients who were on methadone preoperatively. METHODS: The primary outcome was postoperative respiratory depression as defined by respiratory rate < 10/min, oxygen saturation (SpO2) < 90%, or requirement of naloxone for 48 h postoperatively. Secondary outcome was the composite of postoperative respiratory complications. Prediction models for postoperative respiratory depression and respiratory complications were constructed using multivariate logistic regression with preoperative and intraoperative characteristics as the predictors. RESULTS: For the multivariate logistic regression model for postoperative respiratory depression, surgery duration (P = 0.005), body mass index (BMI) (P = 0.008), surgery involving digestive system (P = 0.031), and American Society of Anesthesiologists (ASA) physical status ≥ 4 (P = 0.038) were statistically significant predictors. The area under the receiver operating characteristic curve (AUROC) of the model was 0.581 (0.558-0.601) [median (95% confidence interval (CI))] with fivefold cross-validation. For the model for postoperative respiratory complications, surgery duration (P = 0.001), history of hypertension (P = 0.028), surgery involving musculoskeletal system (P < 0.001), surgery involving integumental system (P = 0.034), surgery categorized to miscellaneous therapeutic procedures (P = 0.028), combined general and regional anesthesia (P = 0.033), ASA physical status 3 (P < 0.001), and ASA physical status ≥ 4 (P < 0.001) were statistically significant predictors, and AUROC of the model was 0.726 (0.712-0.737). CONCLUSIONS: Multivariate logistic regression models including preoperative, and intraoperative characteristics as the predictors performed poorly to predict postoperative respiratory depression, and moderately for postoperative respiratory complications. Neither model is accurate enough to be subject to clinical use.

Direct Comparison of Lysine versus Site-Specific Protein Surface Immobilization in Single-Molecule Mechanical Assays.

Single-molecule force spectroscopy (SMFS) is powerful for studying folding states and mechanical properties of proteins, however, it requires protein immobilization onto force-transducing probes such as cantilevers or microbeads. A common immobilization method relies on coupling lysine residues to carboxylated surfaces using 1-ethyl-3-(3-dimethyl-aminopropyl) carbodiimide and N-hydroxysuccinimide (EDC/NHS). Because proteins typically contain many lysine groups, this strategy results in a heterogeneous distribution of tether positions. Genetically encoded peptide tags (e.g., ybbR) provide alternative chemistries for achieving site-specific immobilization, but thus far a direct comparison of site-specific vs. lysine-based immobilization strategies to assess effects on the observed mechanical properties was lacking. Here, we compared lysine- vs. ybbR-based protein immobilization in SMFS assays using several model polyprotein systems. Our results show that lysine-based immobilization results in significant signal deterioration for monomeric streptavidin-biotin interactions, and loss of the ability to correctly classify unfolding pathways in a multipathway Cohesin-Dockerin system. We developed a mixed immobilization approach where a site-specifically tethered ligand was used to probe surface-bound proteins immobilized through lysine groups, and found partial recovery of specific signals. The mixed immobilization approach represents a viable alternative for mechanical assays on in vivo-derived samples or other proteins of interest where genetically encoded tags are not feasible.

Vesicular and uncoated Rab1-dependent cargo carriers facilitate ER to Golgi transport.

Secretory cargo is recognized, concentrated and trafficked from endoplasmic reticulum (ER) exit sites (ERES) to the Golgi. Cargo export from the ER begins when a series of highly conserved COPII coat proteins accumulate at the ER and regulate the formation of cargo-loaded COPII vesicles. In animal cells, capturing live de novo cargo trafficking past this point is challenging; it has been difficult to discriminate whether cargo is trafficked to the Golgi in a COPII-coated vesicle. Here, we describe a recently developed live-cell cargo export system that can be synchronously released from ERES to illustrate de novo trafficking in animal cells. We found that components of the COPII coat remain associated with the ERES while cargo is extruded into COPII-uncoated, non-ER associated, Rab1 (herein referring to Rab1a or Rab1b)-dependent carriers. Our data suggest that, in animal cells, COPII coat components remain stably associated with the ER at exit sites to generate a specialized compartment, but once cargo is sorted and organized, Rab1 labels these export carriers and facilitates efficient forward trafficking.This article has an associated First Person interview with the first author of the paper.

In-Hospital Opioid Consumption After the Previous Cesarean Delivery Weakly Predicts Opioid Consumption After Index Delivery: A Retrospective Cohort Study.

BACKGROUND: To predict opioid consumption and pain intensity after the index cesarean delivery, we tested a hypothesis that opioid consumption after the previous cesarean delivery of the same patient can predict the opioid consumption after the index cesarean delivery. We further tested a secondary hypothesis that the pain scores after the previous cesarean delivery can predict the pain scores after the index cesarean delivery. METHODS: This is a retrospective cohort study of 470 women who underwent both previous and index cesarean deliveries at a single institution from January 2011 to June 2019. To predict the opioid consumption (primary outcome) and average pain scores (on 11-point numeric rating scale) after their index cesarean delivery, we used a linear regression model incorporating only the opioid consumption and average pain scores after the previous cesarean delivery, respectively (unadjusted models). Demographic and obstetric variables were then added as predictors (adjusted models). The bootstrap was used to compare these models with respect to proportion of variance of the outcome accounted for (R2). RESULTS: Unadjusted models were weakly predictive of opioid consumption (R2 = 0.268; 95% confidence interval [CI], 0.146-0.368) and average pain scores (R2 = 0.176; 95% CI, 0.057-0.250). An adjusted model for opioid consumption was weakly predictive (R2 = 0.363; 95% CI, 0.208-0.478), but an adjusted model for average pain scores was not predictive of the outcomes (R2 = 0.070; 95% CI, -0.143 to 0.219). Adjusted models failed to explain variances of opioid consumption and average pain scores significantly better than unadjusted models (P = .099 and P = .141, respectively). CONCLUSIONS: Opioid consumption and pain scores after women's previous cesarean delivery only explain 27% of variance of opioid consumption and 18% of variance of their pain after their index cesarean delivery. Therefore, previous cesarean delivery analgesic metrics are not robust enough to be used as clinically applicable predictors for index delivery.

Cerebral fat embolism syndrome at a single trauma center.

OBJECTIVES: Based on a 16-year case series, we sought lessons about diagnosis and treatment of cerebral fat embolism syndrome. MATERIALS AND METHODS: Using discharge codes at a Level 1 Trauma Center, we performed a retrospective chart review of clinical characteristics, diagnostic studies, treatments, and outcome in cerebral fat embolism syndrome. RESULTS: Thirty-nine (40%) of 97 patients with fat embolism syndrome were diagnosed with cerebral fat embolism syndrome, with 29 (74%) presenting with coma. All had abnormal brain magnetic resonance imaging, with scattered cytotoxic edema (starfield pattern) in 29 (74%). All but two of the 21 patients with dilated fundoscopy showed retinal embolism. Among 29 patients with transcranial Doppler, the presence of microembolic signals in 15 (52%) was associated with fever (p = 0.039), right-to-left intracardiac shunting (p = 0.046) and a trend towards initial coma. In 11 patients with serial transcranial Dopplers and treatment with high-intensity statin therapy, the frequency of microembolic signals tended to decrease after therapy was initiated. Of the 28 (72%) of the 39 patients discharged, 16 (57%) had mild to moderate disability at last follow up. CONCLUSIONS: The recognition of cerebral fat embolism syndrome may be improved with routine inclusion of brain magnetic resonance imaging, dilated fundoscopy, and transcranial Doppler. We share our empiric management algorithm for cerebral fat embolism syndrome using these studies and with consideration of experimental therapies in select patients to prevent ongoing cerebral injury.

Stochastic ordering of complexoform protein assembly by genetic circuits.

Top-down proteomics has enabled the elucidation of heterogeneous protein complexes with different cofactors, post-translational modifications, and protein membership. This heterogeneity is believed to play a previously unknown role in cellular processes. The different molecular forms of a protein complex have come to be called "complex isoform" or "complexoform". Despite the elucidation of the complexoform, it remains unclear how and whether cellular circuits control the distribution of a complexoform. To help address this issue, we first simulate a generic three-protein complexoform to reveal the control of its distribution by the timing of gene transcription, mRNA translation, and protein transport. Overall, we ran 265 computational experiments: each averaged over 1,000 stochastic simulations. Based on the experiments, we show that genes arranged in a single operon, a cascade, or as two operons all give rise to the different protein composition of complexoform because of timing differences in protein-synthesis order. We also show that changes in the kinetics of expression, protein transport, or protein binding dramatically alter the distribution of the complexoform. Furthermore, both stochastic and transient kinetics control the assembly of the complexoform when the expression and assembly occur concurrently. We test our model against the biological cellulosome system. With biologically relevant rates, we find that the genetic circuitry controls the average final complexoform assembly and the variation in the assembly structure. Our results highlight the importance of both the genetic circuit architecture and kinetics in determining the distribution of a complexoform. Our work has a broad impact on our understanding of non-equilibrium processes in both living and synthetic biological systems.

Virtual Reality Warm-up Before Robot-assisted Surgery: A Randomized Controlled Trial.

TRIAL DESIGN: This was a randomized controlled trial. BACKGROUND: Intraoperative errors correlate with surgeon skill and skill declines with intervals of inactivity. The goals of this research were to identify the optimal virtual reality (VR) warm-up curriculum to prime a surgeon's technical skill and validate benefit in the operating room. MATERIALS AND METHODS: Surgeons were randomized to receive six trial sessions of a designated set of VR modules on the da Vinci Skills Simulator to identify optimal VR warm-up curricula to prime technical skill. After performing their curricula, warm-up effect was assessed based on performance on a criterion task. The optimal warm-up curriculum was chosen from the group with the best task time and video review-based technical skill. Robot-assisted surgery-experienced surgeons were then recruited to either receive or not receive warm-up before surgery. Skill in the first 15 min of surgery was assessed by blinded surgeon and crowdworker review as well as tool motion metrics. The intervention was performing VR warm-up before human robot-assisted surgery. Warm-up effect was measured using objective performance metrics and video review using the Global Evaluative Assessment of Robotic Skills tool. Linear mixed effects models with a random intercept for each surgeon and nonparametric modified Friedman tests were used for analysis. RESULTS: The group performing only a Running Suture task on the simulator was on average 31.3 s faster than groups performing other simulation tasks and had the highest Global Evaluative Assessment of Robotic Skills scores from 41 surgeons who participated. This was chosen as the optimal curriculum. Thereafter, 34 surgeons completed 347 surgeries with corresponding video and tool motion data. No statistically significant differences in skill were observed with the warm-up intervention. CONCLUSIONS: We conclude that a robotic VR warm-up before performing the early stages of surgery does not impact the technical skill of the surgeon.

Associations of Multiple Chronic Conditions With Physical Performance and Falls Among Older Adults With Back Pain: A Longitudinal, Population-based Study.

OBJECTIVE: To determine the association of chronic conditions measured at baseline with physical performance and falls over time among older adults with back pain. We examined both number and type (depression, anxiety, arthritis) of chronic conditions. DESIGN: Retrospective cohort study. SETTING: National Health and Aging Trends Study. PARTICIPANTS: A total of 2438 community-dwelling Medicare beneficiaries aged ≥65 years with bothersome back pain (N=2438). The sample was mostly female (62%; 95% confidence interval [CI], 59%-64%) and aged 65-74 years (56%; 95% CI, 53%-58%). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Short Physical Performance Battery (SPPB) (range, 0-12, lower indicates worse function) and recurrent falls measured annually over 6 years. RESULTS: Multiple chronic conditions were highly prevalent (82%; 95% CI, 79%-84%) among those reporting back pain. Adjusted regressions using survey weights with Taylor series linearization method and containing interaction terms for comorbidity and time showed having 2-3 chronic conditions vs 0-1 was associated with lower SPPB scores, and differences grew over time (for example 0.61 points lower [95% CI, -0.88 to -0.34] and 1.22 points lower [95% CI, -1.76 to -0.67] in rounds 3 and 6, respectively). Having ≥4 chronic conditions was associated with lower SPPB scores at all time points vs 0-1 (point estimate range, -1.72 to -2.31). Arthritis alone; the combination of arthritis with depression; and the triad of arthritis, depression, and anxiety were associated with lower SPPB scores at all time points. Logistic regression models showed presence of 2-3 and ≥4 chronic conditions was associated with increased odds of recurrent falls in any given year (odds ratio, 1.91; 95% CI, 1.35-2.69 and odds ratio, 3.92; 95% CI, 2.81-5.46, respectively). Those with the triad of arthritis, depression, and anxiety had greater odds of recurrent falls vs none or 1 condition. CONCLUSIONS: Among older adults with back pain, those with multiple chronic conditions, including co-occurrence of arthritis, depression, and anxiety, have greater risk for poor physical functioning and falls over time.

Influence of Fluorination on Single-Molecule Unfolding and Rupture Pathways of a Mechanostable Protein Adhesion Complex.

We investigated the influence of fluorination on unfolding and unbinding reaction pathways of a mechanostable protein complex comprising the tandem dyad XModule-Dockerin bound to Cohesin. Using single-molecule atomic force spectroscopy, we mapped the energy landscapes governing the unfolding and unbinding reactions. We then used sense codon suppression to substitute trifluoroleucine in place of canonical leucine globally in XMod-Doc. Although TFL substitution thermally destabilized XMod-Doc, it had little effect on XMod-Doc:Coh binding affinity at equilibrium. When we mechanically dissociated global TFL-substituted XMod-Doc from Coh, we observed the emergence of a new unbinding pathway with a lower energy barrier. Counterintuitively, when fluorination was restricted to Doc, we observed mechano-stabilization of the non-fluorinated neighboring XMod domain. This suggests that intramolecular deformation is modulated by fluorination and highlights the differences between equilibrium thermostability and non-equilibrium mechanostability. Future work is poised to investigate fluorination as a means to modulate mechanical properties of synthetic proteins and hydrogels.

Scalable Online Learning in Physical Chemistry.

The SARS-CoV-2/COVID-19 pandemic has disrupted higher education across the globe. As of early November 2020, Europe now finds itself in the middle of a second wave that is even more destructive than the first. The Swiss Federal Council declared on 28 October, 2020 that face-to-face teaching at Swiss Universities was to cease within days. With large introductory lectures in natural science faculties forced entirely online, educators in Switzerland are facing new challenges and dealing with the limitations of remote instruction. Through a series of anecdotes and observations, this article identifies challenges associated with scalable online learning, and explores methods to mitigate them. Additionally, several advantages to scalable online instruction are identified. By focusing on areas where online instruction has significant advantages, I argue that we can deliver high quality instruction in the chemical sciences remotely.

A Rapid Method to Preoperatively Assess Frailty for Older Patients with Pelvic Floor Conditions.

PURPOSE: Assessment of frailty can help surgeons predict perioperative risk and guide preoperative counseling. However, current methods are often cumbersome in the clinical setting. We prospectively compared the effectiveness of a rapid picture based Clinical Frailty Scale (CFS-9) assessed by patient and surgeon against reference standard Fried Frailty Index in older patients with pelvic floor conditions. MATERIALS AND METHODS: We enrolled 71 patients between March 2018 and June 2019. Frailty assessment using CFS-9 (scale ranging from very fit to terminally ill) was performed followed by the Fried Frailty Index, a validated tool of 5 measures (shrinking, physical energy, activity, grip strength, walking speed). Correlations and agreement between Fried Frailty Index and CFS-9 scores from the treating surgeon, a second surgeon (surgeon 2) and patient were analyzed using sensitivity, specificity, area under the curve and Cohen's Kappa. RESULTS: The patient cohort was mostly female (97.2%), with a mean age (±SD) of 73.0 (±5.9) years and 23.9% were frail using the Fried Frailty Index. Compared to the Fried Frailty Index, CFS-9 scores of the treating surgeon, surgeon 2 and patient had AUC values (95% CI) of 0.86 (0.77-0.86), 0.91 (0.84-0.91) and 0.88 (0.79-0.88), respectively. As assessed by Cohen's Kappa the CFS-9 scores all had substantial (surgeon 2, Kappa 0.66, 95% CI 0.46-0.85 or moderate (all other CFS-9 measures, Kappa 0.44 to 0.58) agreement with the Fried Frailty Index scores. CONCLUSIONS: Rapid and effective validated tools to screen for frailty are needed in the clinical setting. CFS-9 is an excellent predictor of frailty compared to the Fried Frailty Index for patients with pelvic floor conditions.

Removal of a Conserved Disulfide Bond Does Not Compromise Mechanical Stability of a VHH Antibody Complex.

Single-domain VHH antibodies are promising reagents for medical therapy. A conserved disulfide bond within the VHH framework region is known to be critical for thermal stability, however, no prior studies have investigated its influence on the stability of VHH antibody-antigen complexes under mechanical load. Here, we used single-molecule force spectroscopy to test the influence of a VHH domain's conserved disulfide bond on the mechanical strength of the interaction with its antigen mCherry. We found that although removal of the disulfide bond through cysteine-to-alanine mutagenesis significantly lowered VHH domain denaturation temperature, it had no significant impact on the mechanical strength of the VHH:mCherry interaction with complex rupture occurring at ∼60 pN at 103-104 pN/sec regardless of disulfide bond state. These results demonstrate that mechanostable binding interactions can be built on molecular scaffolds that may be thermodynamically compromised at equilibrium.

Single-molecule and Single-cell Approaches in Molecular Bioengineering.

Protein sequences inhabit a discrete set in macromolecular space with incredible capacity to treat human disease. Despite our ability to program and manipulate protein sequences, the vast majority of protein development efforts are still done heuristically without a unified set of guiding principles. This article highlights work in understanding biophysical stability and function of proteins, developing new biophysical measurement tools and building high-throughput screening platforms to explore functional protein sequences. We highlight two primary areas. First, molecular biomechanics is a subfield concerned with the response of proteins to mechanical forces, and how we can leverage mechanical force to control protein function. The second subfield investigates the use of polymers and hydrogels in protein engineering and directed evolution in pursuit of new molecular systems with therapeutic applications. These two subdisciplines complement each other by shedding light onto sequence and structural features that can be used to impart stability into therapeutic proteins.