Study of changes in folding/unfolding properties and stability of Arabidopsis thaliana MYB12 transcription factor following UV-B exposure in vitro.

Flavonoids mostly protect plant cells from the harmful effects of UV-B radiation from the sun. In plants, the R2R3-subfamily of the MYB transcription factor, MYB12, is a key inducer of the biosynthesis of flavonoids. Our study involves the biophysical characterization of Arabidopsis thaliana MYB12 protein (AtMYB12) under UV-B exposure in vitro. Tryptophan fluorescence studies using recombinant full-length AtMYB12 (native) and the N-terminal truncated versions (first N-terminal MYB domain absent in AtMYB12Δ1, and both the first and second N-terminal MYB domains absent in AtMYB12Δ2) have revealed prominent alteration in the tryptophan microenvironment in AtMYB12Δ1 and AtMYB12Δ2 protein as a result of UV-B exposure as compared with the native AtMYB12. Bis-ANS binding assay and urea-mediated denaturation profiling showed an appreciable change in the structural conformation in AtMYB12Δ1 and AtMYB12Δ2 proteins as compared with the native AtMYB12 protein following UV-B irradiation. UV-B-treated AtMYB12Δ2 showed a higher predisposition of aggregate formation in vitro. CD spectral analyses revealed a decrease in α-helix percentage with a concomitant increase in random coiled structure formation in AtMYB12Δ1 and AtMYB12Δ2 as compared to native AtMYB12 following UV-B treatment. Overall, these findings highlight the critical function of the N-terminal MYB domains in maintaining the stability and structural conformation of the AtMYB12 protein under UV-B stress in vitro.

Model-based federated learning for accurate MR image reconstruction from undersampled k-space data.

Deep learning-based methods have achieved encouraging performances in the field of Magnetic Resonance (MR) image reconstruction. Nevertheless, building powerful and robust deep learning models requires collecting large and diverse datasets from multiple centers. This raises concerns about ethics and data privacy. Recently, federated learning has emerged as a promising solution, enabling the utilization of multi-center data without the need for data transfer between institutions. Despite its potential, existing federated learning methods face challenges due to the high heterogeneity of data from different centers. Aggregation methods based on simple averaging, which are commonly used to combine the client's information, have shown limited reconstruction and generalization capabilities. In this paper, we propose a Model-based Federated learning framework (ModFed) to address these challenges. ModFed has three major contributions: (1) Different from existing data-driven federated learning methods, ModFed designs attention-assisted model-based neural networks that can alleviate the need for large amounts of data on each client; (2) To address the data heterogeneity issue, ModFed proposes an adaptive dynamic aggregation scheme, which can improve the generalization capability and robustness of the trained neural network models; (3) ModFed incorporates a spatial Laplacian attention mechanism and a personalized client-side loss regularization to capture the detailed information for accurate image reconstruction. The effectiveness of the proposed ModFed is evaluated on three in-vivo datasets. Experimental results show that when compared to six existing state-of-the-art federated learning approaches, ModFed achieves better MR image reconstruction performance with increased generalization capability. Codes will be made available at https://github.com/ternencewu123/ModFed.

Spin Polarization Enhances the Catalytic Activity of Monolayer MoSe2 for Oxygen Reduction Reaction.

The key factors in achieving high energy efficiency for proton exchange membrane fuel cells are reducing overpotential and increasing the oxygen reduction rate. Based on first-principles calculations, we induce H atom adsorption on 4 × 4 × 1 monolayer MoSe2 to induce spin polarization, thereby improving the catalytic performance. In the calculation of supercells, the band unfolding method is used to address the band folding effect in doped systems. Furthermore, it is evident from analyzing the unique energy band configuration of MoSe2 that a higher valley splitting value has better catalytic effects on the oxygen reduction reaction. We believe that the symmetries of the distinct adsorption site result in different overpotentials. In addition, when an even number of hydrogen atoms is adsorbed, the monolayer MoSe2 has no spin polarization. The spin can affect the electron transfer process and alter the hybrid energy with the reaction products, thereby regulating its catalytic performance.

A Hierarchical Low-Rank Denoising Model for Remote Sensing Images Based on Deep Unfolding.

Recently, the low-rank representation (LRR) model has been widely used in the field of remote sensing image denoising due to its excellent noise suppression capability. However, those low-rank-based methods always discard important edge details as residuals, leading to a common issue of blurred edges in denoised results. To address this problem, we take a new look at low-rank residuals and try to extract edge information from them. Therefore, a hierarchical denoising framework was combined with a low-rank model to extract edge information from low-rank residuals within the edge subspace. A prior knowledge matrix was designed to enable the model to learn necessary structural information rather than noise. Also, such traditional model-driven approaches require multiple iterations, and the solutions may be very complex and computationally intensive. To further enhance the noise suppression performance and computing efficiency, a hierarchical low-rank denoising model based on deep unrolling (HLR-DUR) was proposed, integrating deep neural networks into the hierarchical low-rank denoising framework to expand the information capture and representation capabilities of the proposed shallow model. Sufficient experiments on optical images, hyperspectral images (HSI), and synthetic aperture radar (SAR) images showed that HLR-DUR achieved state-of-the-art (SOTA) denoising results.

A tomographic spatial-unfolding method for Compton gamma imaging measurements.

An advanced spatial-unfolding technique capable of reconstructing the activity distribution within an exclusion zone from Compton gamma imager measurements taken outside of it is introduced. Although the method is generally applicable to extended sources, we demonstrate it here on a calibrated Cs-137 point source through Monte Carlo simulation studies as well as with measurements made using a Silicon Compton Telescope for Safety and Security (SCoTSS) gamma imager. For synthetic data the method accurately reconstructs the total activity contained within the mapped zone of interest, even when the size of the basis elements used to reconstruct the activity distribution is larger than the source itself. For experimental data, the method reliably located the source but underestimated its activity by up to 17%. This is accurate enough for real-world security applications. The underestimation is likely due to effects not yet included in the simulated response of the detector. The method has widespread applicability in the radiological/nuclear safety and security field, particularly for scenarios in which a threat material or contaminated area lies within a no-entry or no-fly zone.

Job embeddedness, career adaptability, and perceived overqualification under career shocks-findings from PLS and NCA.

BACKGROUND: Amidst the post-COVID-19 economic downturn and the expanding higher education landscape in China, employee employment challenges have given rise to the widespread overqualification issue. This phenomenon has attracted extensive attention and is prompting a need for an in-depth exploration of perceived overqualification. However, existing studies predominantly concentrate on its outcomes rather than antecedents, leaving a notable gap in understanding the influence mechanism between individual advantageous resources (e.g., job embeddedness, career adaptability) and overqualification, particularly in specific events such as career shocks. OBJECTIVE: This study aims to examine the interplay between employees' career adaptability, job embeddedness, and the mediating role of relative deprivation in shaping perceived overqualification, particularly in the aftermath of career shocks. METHODS: A comprehensive analysis was conducted using data gathered from 339 questionnaire responses. Partial Least Square (PLS) path analysis, R's necessary condition analysis (NCA), and the Random Forest (RF) algorithm were employed to scrutinize the relationships and identify critical factors influencing perceived overqualification. RESULTS: The findings indicate that after encountering career shocks, career adaptability and job embeddedness not only directly impact perceived overqualification but also exert their influence indirectly through the mediation of relative deprivation; Career adaptability, job embeddedness, and relative deprivation are necessary conditions for perceived overqualification, with relative deprivation having the most significant impact. CONCLUSIONS: Based on the results, focusing on the psychological changes of employees after suffering career shocks provides valuable guidance for managers in channelling the emotional and cognitive responses of their employees.

Efficient leather spreading operations by dual-arm robotic systems.

To achieve precise grasping and spreading of irregular sheet-like soft objects (such as leather) by robots, this study addresses several challenges, including the irregularity of leather edges and the ambiguity of feature recognition points. To tackle these issues, this paper proposes an innovative method that involves alternately grasping the lowest point twice and using planar techniques to effectively spread the leather. We improved the YOLOV8 algorithm by incorporating the BIFPN network structure and the WIOU loss function, and trained a dedicated dataset for the lowest grasping points and planar grasping points, thereby achieving high-precision recognition. Additionally, we determined the optimal posture for grasping the lowest point and constructed an experimental platform, successfully conducting multiple rounds of leather grasping and spreading experiments with a success rate of 72%. Through an in-depth analysis of the failed experiments, this study reveals the limitations of the current methods and provides valuable guidance for future research.

Investigating Directional Invariance in an Item Response Tree Model for Extreme Response Style and Trait-Based Unfolding Responses.

Item response tree (IRTree) approaches have received increasing attention in the response style literature due to their capability to partial out response style latent traits from content-related latent traits by considering separate decisions for agreement and level of agreement. Additionally, it has shown that the functioning of the intensity of agreement decision may depend upon the agreement decision with an item, so that the item parameters and person parameters may differ by direction of agreement; when the parameters across direction are the same, this is called directional invariance. Furthermore, for non-cognitive psychological constructs, it has been argued that the response process may be best described as following an unfolding process. In this study, a family of IRTree models to handle unfolding responses with the agreement decision following the hyperbolic cosine model and the intensity of agreement decision following a graded response model is investigated. This model family also allows for investigation of item- and person-level directional invariance. A simulation study is conducted to evaluate parameter recovery; model parameters are estimated with a fully Bayesian approach using JAGS (Just Another Gibbs Sampler). The proposed modeling scheme is demonstrated with two data examples with multiple model comparisons allowing for varying levels of directional invariance and unfolding versus dominance processes. An approach to visualizing the final model item response functioning is also developed. The article closes with a short discussion about the results.

Combining Native Mass Spectrometry and Proteomics to Differentiate and Map the Metalloform Landscape in Metallothioneins.

Within the intricate landscape of the proteome, approximately 30% of all proteins bind metal ions. This repertoire is even larger when considering all the different forms of a protein, known as proteoforms. Here, we propose the term "metalloforms" to refer to different structural or functional variations of a protein resulting from the binding of various hetero- or homogeneous metal ions. Using human Cu(I)/Zn(II)-metallothionein-3 as a representative model, we developed a chemical proteomics strategy to simultaneously differentiate and map Zn(II) and Cu(I) metal binding sites. In the first labeling step, N-ethylmaleimide reacts with Cysteine (Cys), resulting in the dissociation of all Zn(II) ions while Cu(I) remains bound to the protein. In the second labeling step, iodoacetamide is utilized to label Cu(I)-bound Cys residues. Native mass spectrometry (MS) was used to determine the metal/labeling protein stoichiometries, while bottom-up/top-down MS was used to map the Cys-labeled residues. Next, we used a developed methodology to interrogate an isolated rabbit liver metallothionein fraction containing three metallothionein-2 isoforms and multiple Cd(II)/Zn(II) metalloforms. The approach detailed in this study thus holds the potential to decode the metalloproteoform diversity within other proteins.

A rapid method to monitor structural perturbations of high-concentrated therapeutic antibody solutions using Intrinsic Tryptophan Fluorescence Emission spectroscopy.

Drug product development of therapeutic antibody formulations is still dictated by the risk of protein particle formation during processing or storage, which can lead to loss of potency and potential immunogenic reactions. Since structural perturbations are the main driver for irreversible protein aggregation, the conformational integrity of antibodies should be closely monitored. The present study evaluated the applicability of a plate reader-based high throughput method for Intrinsic Tryptophan Fluorescence Emission (ITFE) spectroscopy to detect protein aggregation due to protein unfolding in high-concentrated therapeutic antibody samples. The impact of fluorophore concentration on the ITFE signal in microplate readers was investigated by analysis of dilution series of two therapeutic antibodies and pure tryptophan. At low antibody concentrations (< 5 mg/mL, equivalent to 0.8 mM tryptophan), the low inner filter effect suggests a quasi-linear relationship between antibody concentration and ITFE intensity. In contrast, the constant ITFE intensity at high protein concentrations (> 40 mg/mL, equivalent to 6.1 mM tryptophan) indicate that ITFE spectroscopy measurements of IgG1 antibodies are feasible in therapeutically relevant concentrations (up to 223 mg/mL). Furthermore, the capability of the method to detect low levels of unfolding (around 1 %) was confirmed by limit of detection (LOD) determination with temperature-stressed antibody samples as degradation standards. Change of fluorescence intensity at the maximum (ΔIaM) was identified as sensitive descriptor for protein degradation, providing the lowest LOD values. The results demonstrate that ITFE spectroscopy performed in a microplate reader is a valuable tool for high-throughput monitoring of protein degradation in therapeutic antibody formulations.

Efficient molecular conformation generation with quantum-inspired algorithm.

CONTEXT: Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5Å), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware becomes mature. METHODS: The objective function of MU is defined as the sum of all internal distances between atoms in the molecule, which is a high-order unconstrained binary optimization (HUBO) problem. The degree of freedom of variables is discretized and encoded with binary variables by the phase encoding method. We employ the quantum-inspired simulated bifurcation algorithm for optimization. The public QM9 dataset is generated by DFT. The simulation experiment of quantum computation is implemented by MindQuantum using QAOA.

Recent advances in gas phase unfolding: Instrumentation and applications.

Broader adoption of native mass spectrometry (MS) and ion mobility-mass spectrometry (IM-MS) has propelled the development of several techniques which take advantage of the selectivity, sensitivity, and speed of MS to make measurements of complex biological molecules in the gas phase. One such method, collision induced unfolding (CIU), has risen to prominence in recent years, due to its well documented capability to detect shifts in structural stability of biological molecules in response to external stimuli (e.g., mutations, stress, non-covalent interactions, sample conditions etc.). This increase in reported CIU measurements is enabled partly due to advances in IM-MS instrumentation by vendors, and also innovative method development by researchers. This perspective highlights a few of these advances and concludes with a look forward toward the future of the gas phase unfolding field.

Development of clinical decision-making among undergraduate nursing students: the effect of unfolding case-based learning.

OBJECTIVES: This prospective cohort study evaluated the effect of unfolding case-based learning on undergraduate nursing students' self-perceived clinical decision-making ability. METHODS: Students' self-reported responses to Jenkins's Clinical Decision Making in Nursing Scale were compared between the unfolding case-based learning cohort (n=140) and the comparison cohort (n=126) at a school of nursing in the United States. RESULTS: The results revealed similar students' responses between the two study cohorts. However, unfolding case-based learning significantly increased students' perceived proficiency in "search for information and unbiased assimilation of new information". CONCLUSIONS: Findings from the present study highlight possibilities presented by unfolding case-based learning in undergraduate nursing education. The study supports that unfolding case studies can be introduced early on, and then nurtured throughout the undergraduate program to influence the development of nursing students' clinical decision-making skills.

The Unfolding Model of Voluntary Turnover: An Analysis and Evaluation.

Background and Purpose: Hospital nurse turnover is a global concern. This author aims to analyze and evaluate the unfolding model of voluntary turnover (UMVT) theory, initially theorized by Lee and Mitchell in 1991, to determine its current usage related to nursing turnover. Methods: A literature search was conducted using the search phrases "unfolding theory of turnover" and "unfolding model of voluntary turnover" to identify empirical evidence. Full-text, English-only journals that primarily utilized the UMVT theory on nurses or other service industries were selected for inclusion. Of the 57 articles identified, 11 were deemed appropriate for analysis. The six steps outlined by Walker and Avant (2019) were used for theory analysis, while Bedow's (2017) points of internal and external criticisms were used to evaluate the theory. Results: The UMVT theory has been tested on a variety of populations, including nurses. This has resulted in new insights into profession-specific turnover and understanding one's decision-making process related to turnover. Despite its decreased usage in the past decade, this theory still underscores benefits for hospital administrators to better understand nursing turnover. Although this theory is not currently ideal for explaining turnover in all populations, such as new graduate nurses or more irrational decision-makers, continued testing of the theory may provide new knowledge regarding voluntary turnover in nursing and highlight areas for refinement. Implications for Practice: The UMVT theory has demonstrated an ability to understand turnover in a variety of professions but remains underresearched internationally. Therefore, new opportunities to test this theory globally are present.

Using unfolding case studies to develop critical thinking for Graduate Entry Nursing students: an educational design research study.

BACKGROUND: Graduate Entry Nursing (GEN) programmes have been introduced as another entry point to nurse registration. In the development of a new GEN programme, a problem-based approach to learning was used to develop critical thinking and clinical reasoning skills of motivated and academically capable students. OBJECTIVE: To explore and evaluate the design and delivery of course material delivered to GEN students embedded in authentic learning pedagogy from the perspectives of both GEN students and academic staff using an unfolding case study approach. METHODS: An educational design research approach was used to explore the learning experiences of GEN students using an unfolding case study approach situated in experiential pedagogy and the teaching experiences of the academics who designed it. Data were collected through semi-structured interviews with students once they had finished the course and weekly reflective diary recordings by academic staff throughout implementation. Thematic analysis was used to analyse the data. FINDINGS: Student reflections highlighted that this cohort had insight into how they learned and were comfortable voicing their needs to academic staff. While the unfolding case studies were not liked by all participants, for some it offered a unique learning opportunity; particularly when scaffolded with podcasts, simulation labs, tutorials and clinical placements. Staff reflections primarily aligned with student experiences. CONCLUSION: The gaps highlighted in the delivery of the course suggest that a blended pedagogical approach to graduate entry nurse education is required. Specifically, GEN students are aware of the learning needs and are happy to express these to academic staff, thus suggesting that engaging with a co-design curriculum approach will benefit future cohorts.

Targeting the protein folding transition state by mutation: Large scale (un)folding rate accelerations without altering native stability.

Proteins are constantly undergoing folding and unfolding transitions, with rates that determine their homeostasis in vivo and modulate their biological function. The ability to optimize these rates without affecting overall native stability is hence highly desirable for protein engineering and design. The great challenge is, however, that mutations generally affect folding and unfolding rates with inversely complementary fractions of the net free energy change they inflict on the native state. Here we address this challenge by targeting the folding transition state (FTS) of chymotrypsin inhibitor 2 (CI2), a very slow and stable two-state folding protein with an FTS known to be refractory to change by mutation. We first discovered that the CI2's FTS is energetically taxed by the desolvation of several, highly conserved, charges that form a buried salt bridge network in the native structure. Based on these findings, we designed a CI2 variant that bears just four mutations and aims to selectively stabilize the FTS. This variant has >250-fold faster rates in both directions and hence identical native stability, demonstrating the success of our FTS-centric design strategy. With an optimized FTS, CI2 also becomes 250-fold more sensitive to proteolytic degradation by its natural substrate chymotrypsin, and completely loses its activity as inhibitor. These results indicate that CI2 has been selected through evolution to have a very unstable FTS in order to attain the kinetic stability needed to effectively function as protease inhibitor. Moreover, the CI2 case showcases that protein (un)folding rates can critically pivot around a few key residues-interactions, which can strongly modify the general effects of known structural factors such as domain size and fold topology. From a practical standpoint, our results suggest that future efforts should perhaps focus on identifying such critical residues-interactions in proteins as best strategy to significantly improve our ability to predict and engineer protein (un)folding rates.

Distinct antithrombin activation modes for fondaparinux and natural heparins detected using millisecond hydrogen deuterium exchange and collision induced unfolding.

Low molecular weight heparin and synthetic mimetics such as fondaparinux show different binding kinetics, protease specificity, and clinical effects. A combination of allosteric and template-mediated bridging mechanisms have been proposed to explain the differences in rate acceleration and specificity. The difficulty in working with heterogeneous heparin species has rendered a crystallographic interpretation of the differences in antithrombin activation between mimetics and natural heparin inaccessible. In this study, we examine the allosteric changes in antithrombin caused by binding fondaparinux, enoxaparin and depolymerized natural heparins using millisecond hydrogen deuterium exchange mass spectrometry (TRESI-HDX MS) and relate these conformational changes to complex stability in the gas phase using collision induced unfolding (CIU). This exploration reveals that in addition to the dynamic changes caused by fondaparinux, long chain heparins reduce structural flexibility proximal to Arg393, the cleavable residue in the reactive centre loop of the protein. These local changes in protein dynamics are associated with an increase in overall complex stability that increases with heparin chain length. Ultimately, these results shed light on the molecular mechanisms underlying differences in activity and specificity between heparin mimetics and natural heparins.

The impact of applying unfolding case-study learning on critical care nursing students' knowledge, critical thinking, and self-efficacy; a quasi-experimental study.

BACKGROUND: The unfolding case-study learning approach is a growing modernized learning strategy implemented in different health disciplines. However, there is a lack of existing research that examines the effects of unfolding case studies in advanced nursing courses. AIM: To examine the impact of applying an unfolding case-study learning approach on critical care nursing students' knowledge, critical thinking, and self-efficacy. METHODS: This posttest-only, quasi-experimental study was conducted at XXX University in Palestine. A single-stage cluster sampling was used to assign nursing students enrolled in the critical care nursing course into experiment and conventional groups. The intervention group (n= 91) underwent unfolding case-study learning for selected cardiovascular topics, whereas the conventional group (n= 78) was taught using the traditional teaching methods. The posttest assessment was conducted using Knowledge Acquisition tests, Yoon`s Critical Thinking Disposition Instrument (YCTD), and the Self-Efficacy for Learning and Performance instruments. The Social Constructivist Theoretical Framework was integrated into the study. RESULTS: Homogeneity was achieved between both groups concerning Age, Gender, and GPA. The experiment group scored significantly higher than the conventional group regarding the posttest knowledge acquisition tests (7.12 vs. 5.49, respectively, t=-12.7, P<0.001, CI: -1.89 to -1.38), critical thinking (4.32 vs. 3.63 respectively, t=17.390, p<0.001, CI: -77 to -61) and self-efficacy (6.12 vs. 4.4 respectively, t=-30.897, p<0.001, CI: -1.82 to -1.60). Multivariate analysis revealed that 69 % of the variations of posttest scores were influenced by critical thinking scores (Adjusted R Squared=0.690, F=3.47, P=0002, η2=0.969). Similarly, self-efficacy has been shown to contribute by 74 % to the variations of scores after conducting the study program (Adjusted R Squared=0.743, F=4.21, P=0001, η2=0.974). However, the variations of both critical thinking and self-efficacy scores were not significantly influenced by the contribution of knowledge acquisition (p=0.772 and 0.857, respectively) and students' GPA (p=0.305 and 0.956, respectively). CONCLUSIONS: Irrespective of knowledge level and GPA, the unfolding case-study learning approach can enhance the critical thinking and self-efficacy of students enrolling in advanced nursing courses.

Exploring the formation of surficial whey protein deposits under shear stress by rheofluidic approach.

Understanding how shear affects whey protein stability is crucial to deal with typical industrial issues occurring at the bulk solution/surface interface, such as fouling during heat treatments. However, at the state of the art, this effect remains unclear, contrary to that of temperature. This article presents a novel strategy to study the impact of shear rate and concentration on the accumulation of whey protein surficial deposits. It consists in applying a range of shear rates (0-200 s-1) at controlled temperature (65 °C) on whey protein solutions (5-10 wt%) by a parallel plate rheometer equipped with a glass disc, thus allowing the off-line characterization of the deposits by microscopy. Our results highlight an unequivocal effect of increasing shear stress. At 5 wt%, it fosters the formation of primary deposits (≈ 10 µm), whereas at 10 wt% it results in the development of complex branched structures (≈ 50 µm) especially for shear rates ranging from 140 s-1 to 200 s-1. Based on the classification by size of the observed populations, we discuss possible hypotheses for the deposit growth kinetics, involving the interplay of different physico-chemical protein-surface interactions and paving the way to future further investigations.

Light exacerbates local and global effects induced by pH unfolding of Ipilimumab.

Monoclonal antibodies (mAbs) are an essential class of therapeutic proteins for the treatment of cancer, autoimmune and rare diseases. During their production, storage, and administration processes, these proteins encounter various stressors such as temperature fluctuations, vibrations, and light exposure, able to induce chemico-physical modifications to their structure. Viral inactivation is a key step in downstream processes, and it is achieved by titration of the mAb at low pH, followed by neutralization. The changes of the pH pose a significant risk of unfolding and subsequent aggregation to proteins, thereby affecting their manufacturing. This study aims to investigate whether a combined exposure to light during the viral inactivation process can further affect the structural integrity of Ipilimumab, a mAb primarily used in the treatment of metastatic melanoma. The biophysical and biochemical characterization of Ipilimumab revealed that pH variation is a considerable risk for its stability with irreversible unfolding at pH 2. The threshold for Ipilimumab denaturation lies between pH 2 and 3 and is correlated with the loss of the protein structural cooperativity, which is the most critical factor determining the protein refolding. Light has demonstrated to exacerbate some local and global effects making pH-induced exposed regions more vulnerable to structural and chemical changes. Therefore, specific precautions to real-life exposure to ambient light during the sterilization process of mAbs should be considered to avoid loss of the therapeutic activity and to increase the yield of production. Our findings underscore the critical role of pH optimization in preserving the structural integrity and therapeutic efficacy of mAbs. Moreover, a detailed conformational study on the structural modifications of Ipilimumab may improve the chemico-physical knowledge of this effective drug and suggest new production strategies for more stable products under some kind of stress conditions.