Miter gates are common structures at lock and dam sites, the geometry of which is such that, under their own significant self-weight, the gates will twist, which can be problematic for gate performance. The twisting is typically addressed by adding post-tensioned members known as diagonals, the design of which is determined by torsional analysis of the gate. The current standard practice of miter gate torsional analysis treats the gate as a cantilevered beam that is free to warp, leading to a torsional stiffness that is negligible and often ignored, leading to overestimates of required post-tensioning in diagonals. In this study, empirical measurements of miter gate torsional stiffness are collected. It is shown that the assumption of unconstrained, pure torsion underestimates the torsional stiffness of miter gates, typically by orders of magnitude. In this study, two approaches are used to provide a better estimate for torsional stiffness of the gates. First, an analytical expression is developed based on the empirical measurements that exclusively consider warping torsion of the gate. Second, a highly detailed numerical finite-element model is used to estimate the torsional stiffness of one of the gates with empirical measurements. Both methods are seen to overestimate the torsional stiffness, but provide values that are at the same order of magnitude of the empirical measurements, thus significantly reducing the error found using current practice; however, the variation in error between the proposed methods and empirically measured values, even among identically designed gates, suggests some uncertainty in boundary conditions, fabrication errors, or fundamental behavior that will be addressed in future work. (c) 2025 Published by American Society of Civil Engineers.

Antibodies that recognize insoluble antigens, such as amyloid fibrils associated with neurodegenerative disorders, are important for research, diagnostic and therapeutic applications. However, these types of antibodies are difficult to generate, typically require animal immunization and also commonly require humanization in the case of therapeutic applications. Here we report a methodology for generating high-quality, fully human, conformation-specific antibodies against amyloid fibrils using a published human nonimmune library, yeast-surface display and quantitative fluorescence-activated cell sorting. Notably, this approach enables the isolation of conformation-specific antibodies against tau fibrils (Alzheimer's disease) and α-synuclein fibrils (Parkinson's disease) with combinations of high affinity, high conformational specificity and, in some cases, low off-target binding that rival or exceed those of clinical-stage antibodies specific for tau (zagotenemab) and α-synuclein (cinpanemab). This approach is expected to simplify the generation of conformation-specific antibodies against diverse protein aggregates and other insoluble antigens.

Monoclonal antibodies that recognize conformational epitopes in protein aggregates are important for research, diagnostic, and therapeutic applications related to neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. Unfortunately, it remains challenging to discover and engineer high-quality conformational antibodies that are specific for protein aggregates and possess optimal combinations of three key binding properties, namely high affinity, high conformational specificity, and low off-target binding. Here we report a directed evolution approach for generating high-quality conformational antibodies against Alzheimer’s Aβ fibrils in the native IgG format. Our directed evolution approach uses targeted mutagenesis, yeast surface display, cell sorting, and deep sequencing to identify antibody candidates with optimized binding properties. Notably, we find that this approach yields robust isolation of IgGs with higher affinity, higher conformational specificity, and lower off-target binding than multiple clinical-stage Aβ antibodies, including aducanumab and crenezumab. This antibody engineering platform can be readily applied to generate conformational antibodies against diverse types of peptide and protein aggregates linked to human diseases.

Although antibody variable regions mediate antigen-specific binding, they can also mediate non-specific interactions with non-cognate antigens, impacting diverse immunological processes and the efficacy, safety, and half-life of antibody therapeutics. To understand the molecular basis of antibody non-specificity, we sorted two dissimilar human naïve antibody libraries against multiple reagents to enrich for variants with different levels of polyreactivity. Sequence analysis of >300,000 paired antibody variable regions revealed that the heavy chain primarily mediates human antibody polyreactivity, and this is due to the high positive charge, high hydrophobicity, and combinations thereof in the corresponding complementarity-determining regions, which can be predicted using a machine learning model developed in this work. Notably, a subset of the most important features governing antibody non-specific interactions, namely those that contain tyrosine, also govern specific antigen recognition. Our findings are broadly relevant for understanding fundamental aspects of antibody molecular recognition and the applied aspects of antibody-drug design.

One of the most important attributes of anti-amyloid antibodies is their selective binding to oligomeric and amyloid aggregates. However, current methods of examining the binding specificities of anti-amyloid β (Aβ) antibodies have limited ability to differentiate between complexes that form between antibodies and monomeric or oligomeric Aβ species during the dynamic Aβ aggregation process. Here, we present a high-resolution native ion-mobility mass spectrometry (nIM-MS) method to investigate complexes formed between a variety of Aβ oligomers and three Aβ-specific IgGs, namely two antibodies with relatively high conformational specificity (aducanumab and A34) and one antibody with low conformational specificity (crenezumab). We found that crenezumab primarily binds Aβ monomers, while aducanumab preferentially binds Aβ monomers and dimers and A34 preferentially binds Aβ dimers, trimers, and tetrameters. Through collision induced unfolding (CIU) analysis, our data indicate that antibody stability is increased upon Aβ binding and, surprisingly, this stabilization involves the Fc region. Together, we conclude that nIM-MS and CIU enable the identification of Aβ antibody binding stoichiometries and provide important details regarding antibody binding mechanisms.

Aim: Adult body size often exhibits patterns across large-scale environmental gradients, creating ecogeographic clines. However, the form of body size clines varies across taxonomic groups, with linear and non-linear patterns in body size observed in nature. Non-linear body size clines have received less study, and questions remain about how environmental gradients interact to produce non-linear clines. We examined the body size of the American horseshoe crab (Limulus polyphemus), a widely distributed marine arthropod, and evaluated the hypothesis that temperature and active season length can interact multiplicatively to result in a dome-shaped distribution.Location: Fourteen states in the United States of America and three Mexican states, representing the entire geographic range of the species.Methods: We compiled environmental data and body size measurements from more than 49,000 individual horseshoe crabs. For each location, we extracted from the literature or calculated from raw data the mean male prosoma width and the mean female prosoma width. We applied a general additive modelling (GAM) approach to characterize the body size cline, test a hypothesis regarding temperature and season length, and explore evidence for the influence of additional environmental factors.Results: Model results indicate temperature and season length could act multiplicatively to produce dome-shaped clines, and these findings align with and quantify previous anecdotal reports of a strong dome-shaped body size cline across latitude for horseshoe crabs.Main Conclusions: Active season length appears to become relatively more influential on horseshoe crab body size in the northern part of their range, while temperature effects per se appear to dominate in southern latitudes. For horseshoe crabs, the pattern of size variation is consistent with the predictions of Optimal Resource Allocation models, but more study is needed to elucidate mechanistic underpinnings. Considering climate change projections, results from our study suggest future shifts in horseshoe crab body sizes.

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

To better understand the population of the Bald Eagle (Haliaeetus leucocephalus) nesting along northern Colorado’s Front Range, from 2016 to 2022 we studied 86 occupied nests within an area of 20,586 km2. From 2017 to 2020, 279 juveniles fledged from 237 nesting attempts in a smaller, main nest-study area with 68 nests. The nests’ success over these four years ranged from 52 to 70%, and their productivity varied from 1.1 to 1.3. The average nearest-nest distances for three discrete areas in the Front Range (5.03 to 7.26 km) are at least 2.8 to 4.0 times greater than these distances in four nesting populations in wetter regions but shorter than distances observed between nests in drier Arizona. In our study area the coverage of buildings within 400 m of Bald Eagle nests is relatively low by comparison to the coverage around randomly selected points, averaging 1344 m2; for 63% of the nests this coverage was less than 800 m2. We classified the 86 nest territories into eight categories that describe the dominant resource habitat and predicts the eagles’ reliance on Black-tailed Prairie Dogs (Cynomys ludovicianus) versus fish as prey. Predation on fish was predicted to be dominant at 51% (n = 44) of the nests, predation on prairie dogs at 32% (n = 28).

MOTIVATION:Deep sequencing of antibody and related protein libraries after phage or yeast-surface display sorting is widely used to identify variants with increased affinity, specificity, and/or improvements in key biophysical properties. Conventional approaches for identifying optimal variants typically use the frequencies of observation in enriched libraries or the corresponding enrichment ratios. However, these approaches disregard the vast majority of deep sequencing data and often fail to identify the best variants in the libraries.RESULTS:Here, we present a method, Position-Specific Enrichment Ratio Matrix (PSERM) scoring, that uses entire deep sequencing datasets from pre- and post-selections to score each observed protein variant. The PSERM scores are the sum of the site-specific enrichment ratios observed at each mutated position. We find that PSERM scores are much more reproducible and correlate more strongly with experimentally measured properties than frequencies or enrichment ratios, including for multiple antibody properties (affinity and non-specific binding) for a clinical-stage antibody (emibetuzumab). We expect that this method will be broadly applicable to diverse protein engineering campaigns.AVAILABILITY AND IMPLEMENTATION:All deep sequencing datasets and code to perform the analyses presented within are available via https://github.com/Tessier-Lab-UMich/PSERM_paper.

Single-domain antibodies, also known as nanobodies, are broadly important for studying the structure and conformational states of several classes of proteins, including membrane proteins, enzymes, and amyloidogenic proteins. Conformational nanobodies specific for aggregated conformations of amyloidogenic proteins are particularly needed to better target and study aggregates associated with a growing class of associated diseases, especially neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. However, there are few reported nanobodies with both conformational and sequence specificity for amyloid aggregates, especially for large and complex proteins such as the tau protein associated with Alzheimer’s disease, due to difficulties in selecting nanobodies that bind to complex aggregated proteins. Here, we report the selection of conformational nanobodies that selectively recognize aggregated (fibrillar) tau relative to soluble (monomeric) tau. Notably, we demonstrate that these nanobodies can be directly isolated from immune libraries using quantitative flow cytometric sorting of yeast-displayed libraries against tau aggregates conjugated to quantum dots, and this process eliminates the need for secondary nanobody screening. The isolated nanobodies demonstrate conformational specificity for tau aggregates in brain samples from both a transgenic mouse model and human tauopathies. We expect that our facile approach will be broadly useful for isolating conformational nanobodies against diverse amyloid aggregates and other complex antigens.

Antibodies that recognize specific protein conformational states are broadly important for research, diagnostic and therapeutic applications, yet they are difficult to generate in a predictable and systematic manner using either immunization or in vitro antibody display methods. This problem is particularly severe for conformational antibodies that recognize insoluble antigens such as amyloid fibrils associated with many neurodegenerative disorders. Here we report a quantitative fluorescence-activated cell sorting (FACS) method for directly selecting high-quality conformational antibodies against different types of insoluble (amyloid fibril) antigens using a single, off-the-shelf human library. Our approach uses quantum dots functionalized with antibodies to capture insoluble antigens, and the resulting quantum dot conjugates are used in a similar manner as conventional soluble antigens for multi-parameter FACS selections. Notably, we find that this approach is robust for isolating high-quality conformational antibodies against tau and α-synuclein fibrils from the same human library with combinations of high affinity, high conformational specificity and, in some cases, low off-target binding that rival or exceed those of clinical-stage antibodies specific for tau (zagotenemab) and α-synuclein (cinpanemab). This approach is expected to enable conformational antibody selection and engineering against diverse types of protein aggregates and other insoluble antigens (e.g., membrane proteins) that are compatible with presentation on the surface of antibody-functionalized quantum dots.

Locks and dams facilitate the transportation of billions of dollars in goods through inland waterways annually. Miter gates are lock components that are supported by steel anchorage frames embedded in the concrete lock wall. In the US, many of these anchorages have been subjected to over 80 years of cyclic loads. The typical analysis approach for anchorages treats the steel frame as a freestanding truss, ignoring the embedding concrete. This approach predicts that some anchorages may imminently fail in fatigue. Thus, there is a push to excavate and replace these anchorages at a cost of nearly $10 million USD per site. Previous numerical modelling of an embedded miter gate anchorage, considering the effects of concrete, shows that the stress in most of the anchorage is below the endurance limit of steel. To verify these modelling results, a full-scale laboratory test is performed wherein a representative anchorage is instrumented and loaded under typical gate loads. The testing is performed in three phases: the first phase represents the freestanding truss; the second and third phases represent the fully embedded anchorage in two typical orientations. Results of the test support the numerical modelling results, suggesting that planned anchorage replacements are not required.

Tainter gates are commonly used as water control gates on civil infrastructure, with one such gate in use at the lock and dam at The Dalles, Oregon on the Columbia River. Under normal operating conditions, Tainter gates should be hoisted in a level fashion throughout the circular path they travel. Uneven hoisting may lead to a redistribution of loads or accelerated fatigue damage, and an instrumentation system was installed on the subject gate with a goal to monitor for such hoisting. Additionally, a numerical model was created to investigate changes in structural response when the gate hoists unevenly. Comparison of the initial data to the model suggested that the gate was regularly hoisting unevenly. To increase confidence in the instrumentation system, and aid in validating the numerical model, short-term monitoring was leveraged wherein the hoisting cables of the gate were instrumented with accelerometers. Then, a frequency domain method was used to infer the tension in each of the cables that hoist the gate, which is expected to be approximately equal if the gate is hoisting evenly. Results from the monitoring program support the notion that the gate is regularly hoisting unevenly, thus increasing confidence in the instrumentation system.

Intracellular protein-protein interactions are involved in many different diseases, making them prime targets for therapeutic intervention. Several diseases are characterized by their overexpression of Bcl-xL, an anti-apoptotic B cell lymphoma 2 (Bcl-2) protein expressed on mitochondrial membranes. Bcl-xL overexpression inhibits apoptosis, and selective inhibition of Bcl-xL has the potential to increase cancer cell death while leaving healthy cells comparatively less affected. However, high homology between Bcl-xL and other Bcl-2 proteins has made it difficult to selectively inhibit this interaction by small molecule drugs. We engineered stapled peptides, a chemical modification that can improve cell penetration, protease stability, and conformational stability, towards the selective inhibition of Bcl-xL. To accomplish this task, we built a focused combinatorial mutagenesis library of peptide variants on the bacterial cell surface, used copper catalyzed click chemistry to form stapled peptides, and sorted the library for high binding to Bcl-xL and minimal binding towards other Bcl-2 proteins. We characterized the sequence and staple placement trends that governed specificity and identified molecules with ∼10 nM affinity to Bcl-xL and greater than 100-fold selectivity versus other Bcl-2 family members on and off the cell surface. We confirmed the mechanism of action of these peptides is consistent with apoptosis biology through mitochondrial outer membrane depolarization assays (MOMP). Overall, high affinity (10 nM Kd) and high specificity (100-fold selectivity) peptides were developed to target the Bcl-xL protein. These results demonstrate that stapled alpha helical peptides are promising candidates for the specific treatment of cancers driven by Bcl-2 dysregulation.### Competing Interest StatementThe authors have declared no competing interest.

As-built structures typically behave differently than their analytical models because of uncertainties in material properties, boundary conditions, loading scenarios, or other modeling assumptions. Therefore, to perform structural health monitoring effectively, analytical models need to be calibrated or updated to match the measured responses from as-built structures. Bayesian model updating provides a rigorous framework to integrate data and parameter uncertainty and has been applied successfully in numerous application settings employing traditional sensors that are often sparsely distributed. Computer vision has shown tremendous success for the measurement of static and dynamic displacements of structures. Vision-based measurements have the potential to be transformative for model updating of large-scale civil infrastructure, as measurements can be obtained for dense array of points within the camera frame using a single device. However, model updating using vision-based measurements to date has been limited to the use of a few points selected manually, leading to the investigation of simple structures or their global behaviors (e.g. first few modal responses); developing a systematic and highly automated procedure for estimating structural displacement and its uncertainty densely, registering the measured data to the finite element model, and then updating the model based on all the available data remains a challenge. This study proposes Bayesian inference using dense vision-based measurements to estimate the localized response of the entire structure. Finite element models are incorporated into the threedimensional displacement measurement and registration process (model-informed approach), and then the Markov chain Monte Carlo method is applied to enable the Bayesian inference for large and complex civil infrastructure. The proposed method is applied to a laboratory-scale three-dimensional steel truss to perform inference of the overall system response based on the a posteriori knowledge. The results demonstrate the adequacy of performing Bayesian inference of structural response using vision-based measurements.

Neutralizing monoclonal antibodies and nanobodies have shown promising results as potential therapeutic agents for COVID-19. Identifying such antibodies and nanobodies requires evaluating the neutralization activity of a large number of lead molecules via biological assays, such as the virus neutralization test (VNT). These assays are typically time-consuming and demanding on-lab facilities. Here, we present a rapid and quantitative assay that evaluates the neutralizing efficacy of an antibody or nanobody within 1.5 h, does not require BSL-2 facilities, and consumes only 8 μL of a low concentration (ng/mL) sample for each assay run. We tested the human angiotensin-converting enzyme 2 (ACE2) binding inhibition efficacy of seven antibodies and eight nanobodies and verified that the IC50 values of our assay are comparable with those from SARS-CoV-2 pseudovirus neutralization tests. We also found that our assay could evaluate the neutralizing efficacy against three widespread SARS-CoV-2 variants. We observed increased affinity of these variants for ACE2, including the β and γ variants. Finally, we demonstrated that our assay enables the rapid identification of an immune-evasive mutation of the SARS-CoV-2 spike protein, utilizing a set of nanobodies with known binding epitopes.

SARS-CoV-2 variants with enhanced transmissibility represent a serious threat to global health. Here we report machine learning models that can predict the impact of receptor-binding domain (RBD) mutations on receptor (ACE2) affinity, which is linked to infectivity, and escape from human serum antibodies, which is linked to viral neutralization. Importantly, the models predict many of the known impacts of RBD mutations in current and former Variants of Concern on receptor affinity and antibody escape as well as novel sets of mutations that strongly modulate both properties. Moreover, these models reveal key opposing impacts of RBD mutations on transmissibility, as many sets of RBD mutations predicted to increase antibody escape are also predicted to reduce receptor affinity and vice versa. These models, when used in concert, capture the complex impacts of SARS-CoV-2 mutations on properties linked to transmissibility and are expected to improve the development of next-generation vaccines and biotherapeutics.

Self-association governs the viscosity and solubility of therapeutic antibodies in high-concentration formulations used for subcutaneous delivery, yet it is difficult to reliably identify candidates with low self-association during antibody discovery and early-stage optimization. Here, we report a high-throughput protein engineering method for rapidly identifying antibody candidates with both low self-association and high affinity. We find that conjugating quantum dots to IgGs that strongly self-associate (pH 7.4, PBS), such as lenzilumab and bococizumab, results in immunoconjugates that are highly sensitive for detecting other high self-association antibodies. Moreover, these conjugates can be used to rapidly enrich yeast-displayed bococizumab sub-libraries for variants with low levels of immunoconjugate binding. Deep sequencing and machine learning analysis of the enriched bococizumab libraries, along with similar library analysis for antibody affinity, enabled identification of extremely rare variants with co-optimized levels of low self-association and high affinity. This analysis revealed that co-optimizing bococizumab is difficult because most high-affinity variants possess positively charged variable domains and most low self-association variants possess negatively charged variable domains. Moreover, negatively charged mutations in the heavy chain CDR2 of bococizumab, adjacent to its paratope, were effective at reducing self-association without reducing affinity. Interestingly, most of the bococizumab variants with reduced self-association also displayed improved folding stability and reduced nonspecific binding, revealing that this approach may be particularly useful for identifying antibody candidates with attractive combinations of drug-like properties.Abbreviations: AC-SINS: affinity-capture self-interaction nanoparticle spectroscopy; CDR: complementarity-determining region; CS-SINS: charge-stabilized self-interaction nanoparticle spectroscopy; FACS: fluorescence-activated cell sorting; Fab: fragment antigen binding; Fv: fragment variable; IgG: immunoglobulin; QD: quantum dot; PBS: phosphate-buffered saline; VH: variable heavy; VL: variable light.

Conformational antibodies specific for amyloid-forming peptides and proteins are important for a range of biomedical applications, including detecting, inhibiting, and potentially treating protein aggregation disorders ranging from Alzheimer's to Parkinson's diseases. Generation of anti-amyloid antibodies is greatly complicated by the complex, heterogeneous and insoluble nature of amyloid antigens. Here we describe systematic methods for isolating and affinity maturing anti-amyloid antibodies using yeast surface display. Magnetic-activated cell sorting is used to sort single-chain antibody libraries positively for binding to amyloid antigens and negatively against the corresponding disaggregated antigens to remove antibodies that bind in a conformation-independent manner. Isolated lead antibody clones with conformational specificity are affinity matured via targeted CDR mutagenesis and magnetic-activated cell sorting.

Agonist antibodies that activate cellular signaling have emerged as promising therapeutics for treating myriad pathologies. Unfortunately, the discovery of rare antibodies with the desired agonist functions is a major bottleneck during drug development. Nevertheless, there has been important recent progress in discovering and optimizing agonist antibodies against a variety of therapeutic targets that are activated by diverse signaling mechanisms. Herein, we review emerging high-throughput experimental and computational methods for agonist antibody discovery as well as rational molecular engineering methods for optimizing their agonist activity.