Search Results (282)

An efficient bacteriophage delivery system needs to be developed to overcome the challenges associated with phage instability, rapid diffusion, and loss of infectivity at the infection site. Hydrogels have been found to be potential carriers. Hydrogels have emerged as promising carriers due to their biocompatibility, tunable physicochemical properties and capacity for controlled release. However, the molecular factors that regulate phage–hydrogel interactions remain poorly understood. In this study, we employed an in silico framework combining molecular docking, molecular dynamics (MD) simulations, MM/PBSA binding energy calculations, machine learning-based adhesion prediction, and diffusion modeling to explore phage–hydrogel interactions at the molecular level. Surface-exposed bacteriophage proteins, such as capsid and tail proteins, were evaluated against eight different hydrogel polymers. Binding site analysis revealed the presence of multiple solvent-accessible pockets that can interact with the polymer. Docking studies showed favorable and stable interactions, with hyaluronic acid showing strong binding affinity to multiple phage proteins (−5.5 to −5.7 kcal/mol) and GelMA showing high affinity to the capsid gp10 protein (−5.6 kcal/mol). The integrity of the structural complexes was further confirmed by 100 ns MD simulations, stable RMSD and RMSF trajectories, compact structural conformations, and favorable MM/PBSA binding energies. Machine learning classification successfully differentiated high- and low-adhesion systems and identified hydrogen bonding and electrostatic interactions as key determinants of sustained yet reversible phage retention. Collectively, our findings suggest that the hydrogels enriched with charged and polar functional groups can facilitate stable but non-destructive phage binding, enabling controlled and sustained release. This study provides mechanistic insights into rational hydrogel design for phage delivery systems and highlights the potential of high-throughput computational strategies to accelerate the development of optimized phage therapeutics. Full article

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

The eukaryotic translation initiation factor 4E (eIF4E) family plays a dual role in plants, regulating cap-dependent protein synthesis and mediating susceptibility to viruses in the family Potyviridae. In cowpea (Vigna unguiculata (L.) Walp.), an economically important legume cultivated worldwide, the structural determinants of these isoforms remain largely unexplored. This study characterizes the genomic organization, evolutionary history, and conformational dynamics of eIF4E, eIF(iso)4E, and nCBP in cowpea using a multi-omics approach. Genome mining identified three paralogous genes located on chromosomes 4, 6, and 7, showing high synteny with Phaseolus vulgaris. Phylogenetic analysis confirmed nCBP as the ancestral Class I lineage, distinct from the Class II eIF4E and eIF(iso)4E clades. Theoretical models for the isoforms were generated and subsequently validated by molecular dynamics simulations, revealing that while all isoforms preserve the canonical tertiary architecture and an electropositive cap-binding pocket, eIF(iso)4E exhibits superior structural compactness and hydrogen-bond stability. These biophysical features highlight their role as a stable anchor for viral VPg proteins. By elucidating the atomic-level landscape of these factors, we provide a robust structural framework to guide allele mining and genome-editing strategies aiming to engineer virus-resistant cowpea cultivars without compromising agronomic performance. Full article

Protein kinase inhibition can be achieved through various mechanisms, including blocking phosphorylation activity or disrupting regulatory interactions. While small molecule inhibitors have shown promise, their selectivity remains challenging due to the structural similarities among kinase catalytic sites. To design selective kinase inhibitors based on peptide terminal tail interactions with the activation segment, focusing on five kinases with different conformational states: GSK3, PAK4, TTN (OUT conformation) and PKB, FLT3 (IN conformation). Three-dimensional structures from RCSB PDB were optimized using MODELLER version 9.0. Peptide sequences were designed with PeptiDerive (Rosetta) and RosettaDesign version 3.5, followed by pharmacophore modeling based on key interaction residues. Virtual screening was then conducted with PyRx 0.8 and molecular docking with AutoDock Vina 1.1.2. Molecular dynamics simulations were performed using Desmond v6.6 (Schrödinger Suite 2016, Multisim v3.8.5.19) (100 ns, NPT ensemble, 300 K). Analysis of the five kinases revealed distinct interaction profiles with designed peptidomimetic compounds. Kinases displaying the IN conformation of the activation segment (PKB and FLT3) consistently showed superior stability and stronger interaction profiles compared to those in the OUT conformation. The designed compounds formed key hydrogen bonds and hydrophobic interactions with critical residues in the activation segment binding pocket. The most promising inhibitors demonstrated stability throughout the molecular dynamics simulations, with IN conformation kinases maintaining more consistent conformational profiles than their OUT conformation counterparts. Kinases with IN conformation of the activation segment demonstrated superior stability and interaction profiles compared to OUT conformations. These findings contribute to our understanding of selective kinase inhibition and provide a framework for developing novel inhibitors, particularly for PKB and FLT3. The implications of this study extend to rational drug design approaches that leverage natural regulatory mechanisms for therapeutic intervention, though further optimization is needed for GSK-3β, PAK4, and TTN to improve stability and binding affinity. Full article

Background/Objectives: 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a highly toxic environmental contaminant whose adverse biological effects are primarily mediated through activation of the aryl hydrocarbon receptor (AhR). Upon ligand binding, AhR undergoes conformational changes that initiate nuclear translocation and transcriptional activation of xenobiotic-responsive genes, contributing to toxicity, carcinogenesis, and dysregulated immune and metabolic responses. Understanding the molecular basis of AhR activation by TCDD is therefore critical for the rational development of targeted therapeutic strategies. Methods: In this study, molecular docking simulations were employed to characterize the interaction of TCDD and selected AhR antagonists (CH223191, BAY 2416964, GNF-351) with the ligand-binding domain of AhR, with particular emphasis on the canonical PAS-B domain. Results: Docking analyses identified the PAS-B cavity (pocket C1) as the most biologically relevant binding site for high-affinity ligands, consistent with experimental evidence. Comparative docking of known AhR antagonists revealed stable binding poses characterized by hydrophobic packing, π–π interactions, and hydrogen-bonding networks that competitively block agonist access and prevent receptor activation. These findings support a competitive antagonism mechanism as a viable approach to counteract TCDD-induced AhR signaling. Conclusions: Collectively, this in silico study provides mechanistic insight into TCDD toxicity at the molecular level and highlights AhR antagonism as a promising strategy for the development of targeted therapies against dioxin-related pathologies. Full article

Allosteric modulation has emerged as a powerful strategy for achieving superior selectivity and safety in drug discovery and protein function regulation. Unlike highly conserved orthosteric sites, allosteric pockets are structurally diverse and less evolutionarily constrained, making them particularly suitable for modulation by designed miniproteins. Miniproteins can provide extended binding interfaces and high affinity for shallow, dynamic, or cryptic regulatory surfaces that are often inaccessible to small molecules. Recent advances in artificial intelligence (AI) are transforming this field through deep learning-based structure prediction and generative modeling. These AI-driven approaches enable the identification of allosteric hotspots, characterization of conformational ensembles, and de novo design of structured miniprotein binders. They are rapidly expanding the landscape for designing selective modulators across diverse allosteric targets, including GPCRs, receptor tyrosine kinases, nuclear receptors, ion channels, and other protein–protein interaction systems. This review summarizes state-of-the-art AI-driven computational methodologies for designing miniproteins as potential allosteric modulators and discusses their current challenges and future opportunities in allosteric drug discovery. Full article

The rapid spread of antibiotic resistance through plasmid-mediated conjugation remains a primary global health concern. Despite its critical role in horizontal gene transfer, no approved drugs currently target this process, leaving a critical therapeutic gap. Integration Host Factor (IHF), a DNA-binding protein essential for plasmid replication and mobilization, emerges as a promising yet underexplored target for anti-conjugation strategies. This work aimed to develop a predictive computational model and identify small molecules that disrupt IHF function, thereby reducing plasmid transfer and limiting resistance gene dissemination. A curated dataset of 65 compounds with reported anti-plasmid activity was analyzed using a 3D-QSAR model based on algebraic descriptors computed with QuBiLS-MIDAS. The model was validated through leave-one-out cross-validation (Q² = 0.82), Tropsha’s criteria, and Y-scrambling. Representative compounds were selected via pharmacophore clustering and evaluated through molecular docking at both the DNA-binding site and a predicted allosteric pocket of IHF. The most promising complexes underwent 200 ns molecular dynamics simulations to assess stability and interaction patterns. The QSAR model demonstrated strong predictive performance (R² = 0.90). Docking simulations revealed more favorable binding energies at the allosteric site (up to −12.15 kcal/mol) compared to the DNA-binding site. Molecular dynamics confirmed the stability of these interactions, with allosteric complexes showing lower RMSD fluctuations and consistent binding energy profiles. Dynamic cross-correlation analysis revealed that allosteric ligand binding induces conformational changes in key catalytic residues, including Pro65, Pro61, and Leu66. These alterations may compromise DNA recognition and disrupt the initiation of replication. To our knowledge, this is the first computational study proposing allosteric inhibition of IHF as an anti-conjugation strategy. These findings provide a foundation for experimental validation and the development of novel agents to prevent horizontal gene transfer, offering a promising approach to restoring antibiotic efficacy against multidrug-resistant pathogens. Full article

Chronic, dysregulated inflammation contributes to colitis-associated colorectal cancer (CRC), and the cGAS–STING pathway represents a central but therapeutically challenging node because both insufficient and excessive STING activity can be pathogenic. Here, we integrate AlphaFold3 (AF3) receptor modeling, diffusion-based docking, and explicit-solvent molecular dynamics (MD) simulations to characterize how the marine briarane diterpenoid excavatolide B (ExcB) engages the human STING (hSTING) cyclic dinucleotide (CDN)-binding cleft. The structural integrity of the AF3 hSTING model was validated through both intrinsic confidence scores (pLDDT, PAE) and comparative benchmarking against experimental CTD structures (PDB: 4EF5, 6A05). Notably, the local geometries of key pocket-defining residues—including His157, Tyr167, and Thr263—remained consistent with established crystallographic data. Across three independent 100 ns MD replicas, ExcB exhibits a consistent spatial progression from an entrance-proximal pose at the solvent-accessible rim of the cleft (Site-2) to a more embedded, non-canonical corridor on the Cys148-adjacent side (Site-2′). Distance and contact analyses support a predominantly non-covalent within-cleft mechanism and do not indicate a persistent approach to the literature-reported covalent regime near Cys91. Residue-level profiling over the stabilized sampling window defines a reproducible corridor “contact signature” and reveals within-cavity sub-pose diversity rather than a single rigid bound pose. Mechanistically, competitive docking of the native agonist cGAMP to ExcB-conditioned receptor snapshots yields consistently less favorable docking outcomes in ExcB-conditioned conformations than docking to the native/open receptor; retaining ExcB coordinates does not further penalize cGAMP, supporting a receptor-reshaping (conformational conditioning) component rather than persistent static steric clash. Our findings characterize ExcB as a non-covalent modulator targeting a cryptic pocket within the STING CDN-binding cleft, establishing a structural basis for targeted mutagenesis and structure-activity relationship (SAR) studies. Full article

Background/Objectives: Breast cancer remains a leading cause of cancer-related mortality in women, with therapeutic resistance frequently arising from tumor heterogeneity and an immunosuppressive tumor immune microenvironment (TIME). While Cynomorium songaricum Rupr. (CS) has been used traditionally in Chinese medicine and exhibits preliminary anti-tumor activity, its bioactive constituents and precise mechanisms against breast cancer remain to be elucidated. Methods: The chemical constituents of CS were systematically profiled using ultra-high-performance liquid chromatography coupled with Q Exactive Orbitrap mass spectrometry (UHPLC-Q-Exactive Orbitrap MS/MS). Network pharmacology and functional enrichment analyses were performed to identify immuno-related targets and pathways, followed by molecular docking to prioritize component–target pairs. Molecular dynamics (MD) simulations were conducted to validate the stability of a representative docked complex and to characterize binding stability, interaction persistence, molecular mechanics/(Poisson–Boltzmann) surface area (MM/(P)BSA) energetics, and principal component analysis (PCA)-based conformational landscapes. Results: We identified 1100 compounds, of which 84 satisfied the in silico drug-likeness criteria, including 12 phenylpropanoids, 4 terpenes, 35 flavonoids, 2 quinones, 1 phenol, 3 alkaloids, and other phytochemicals. Network pharmacology analysis revealed 776 overlapping targets associated with both breast cancer and immune regulation. Functional enrichment analysis underscored significant involvement in immune-related pathways, and molecular docking studies supported high-affinity interactions between the components and their targets. MD analyses further supported a stable bound ensemble for the representative SRC–Tomentogenin complex during the equilibrated window, with persistent pocket occupancy, consistent interaction signatures, favorable MM/(P)BSA binding energetics, and a concentrated low-energy basin on the PCA-based free energy landscape. Conclusions: These findings elucidate the chemical basis of CS and uncover its immunomodulatory mechanism against breast cancer, offering a foundation for developing CS-based immunotherapeutic strategies and supporting multi-target drug discovery from traditional medicines. Full article

Cyclin-dependent kinase 2 (CDK2) plays a central role in cell cycle regulation and represents an important molecular target in anticancer drug development. In this study, a series of novel isatin derivatives substituted with a trifluoromethoxy group at the C6 position were designed and evaluated as potential CDK2 inhibitors using a comprehensive in silico approach. Density functional theory calculations were applied to analyze the electronic properties of the proposed compounds. Molecular docking and molecular dynamics simulations were used to investigate binding modes, conformational stability, and key interactions within the CDK2 active site. Binding free energies were estimated using the Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) method, while QSAR-based (Quantitative Structure–Activity Relationship) ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses were performed to assess drug-likeness and pharmacokinetic profiles. The results indicate that the investigated derivatives form stable complexes with CDK2, supported by persistent hydrogen bonds in the hinge region and favorable hydrophobic interactions. The trifluoromethoxy substituent significantly affects ligand orientation and promotes deeper insertion into the hydrophobic pocket compared with previously studied isatin analogues. ADMET predictions suggest generally favorable absorption and toxicity profiles, with moderate solubility limitations. Overall, these findings support the potential of 6-trifluoromethoxy-isatin derivatives as promising CDK2 inhibitors and provide a basis for further experimental studies. Full article

Fyn is a Src-family tyrosine kinase implicated in synaptic dysfunction and neuroinflammation across multiple neurodegenerative disorders, including Alzheimer’s disease (AD) and Parkinson’s disease (PD). Saracatinib (AZD0530) is a potent Src-family inhibitor that has been explored as a repurposed therapeutic; however, its clinical utility is limited by poor kinase selectivity caused by high sequence conservation within Src-family ATP-binding sites. Here, we combine surface plasmon resonance (SPR) and X-ray crystallography to define saracatinib recognition by the Fyn kinase domain (KD). SPR single-cycle kinetics shows that saracatinib binds the isolated Fyn KD and full-length Fyn with low-nanomolar affinity, whereas dasatinib binds with subnanomolar affinity and markedly slower dissociation. We determined the crystal structure of the Fyn KD-saracatinib complex at 2.22 Å resolution. The kinase adopts an active-like conformation with the DFG motif and αC-helix in the ‘in’ state and a conserved β3 αC Lys-Glu salt bridge. Saracatinib occupies the adenine and ribose pockets, and engages the hinge through direct and water-mediated hydrogen bonding while complementing a hydrophobic back pocket by van der Waals contacts. Comparison with reported saracatinib-bound structures of other kinases suggests that the active-state geometry observed for Fyn creates a pocket not observed in inactive-like complexes, providing a structural handle for designing Fyn-selective inhibitors. Comparison with all saracatinib-bound kinase co-structures currently available in the PDB (ALK2 and PKMYT1) indicates a conserved monodentate hinge binding mode but kinase-dependent αC-helix conformations, providing a structural rationale for designing Fyn-selective analogues. Full article

Artificial intelligence (AI) is becoming more and more integrated into manufacturing processes, revolutionizing conventional production, like CNC (Computer Numerical Control) machining. This study analyzes how large language models (LLMs), exemplified by ChatGPT, behave when tasked with G-code optimization for improving surface quality and productivity of automotive metal parts, with emphasis on systematically documenting failure modes and limitations that emerge when general-purpose AI encounters specialized manufacturing domains. Even if software programming remains essential for highly regulated sectors, free AI tools will be increasingly used due to advantages like cost-effectiveness, adaptability, and continuous innovation. The condition is that there is sufficient technical expertise available in-house. The experiment carried out involved milling three identical parts using a Haas VF-3 SS CNC machine. The G-code was generated by SolidCam and was optimized using ChatGPT considering user-specified criteria. The aim was to improve the quality of the part’s surface, as well as increase productivity. The measurements were performed using an ISR C-300 Portable Surface Roughness Tester and Axiom Too 3D measuring equipment. The experiment revealed that while AI-generated code achieved a 37% reduction in cycle time (from 2.39 to 1.45 min) and significantly improved surface roughness (Ra—arithmetic mean deviation of the evaluated profile—decreased from 0.68 µm to 0.11 µm—an 84% improvement), it critically eliminated the pocket-milling operation, resulting in a non-conforming part. The AI optimization also removed essential safety features including tool length compensation (G43/H codes) and return-to-safe-position commands (G28), which required manual intervention to prevent tool breakage and part damage. Critical analysis revealed that ChatGPT failures stemmed from three factors: (1) token-minimization bias in LLM training leading to removal of the longest code block (31% of total code), (2) lack of semantic understanding of machining geometry, and (3) absence of manufacturing safety constraints in the AI model. This study demonstrates that current free AI tools like ChatGPT can identify optimization opportunities but lack the contextual understanding and manufacturing safety protocols necessary for autonomous CNC programming in production environments, highlighting both the potential, but also the limitation, of free AI software for CNC programming. Full article

CK2α and CK2α’, two paralogous members of the human kinome, are catalytic subunits of protein kinase CK2. Together with the regulatory subunit CK2β, they form heterotetrameric holoenzymes. CK2 is the subject of efforts to develop effective and selective inhibitors. For this, secondary binding sites remote from the canonical ATP/GTP cavity are critical. A crystallographic fragment screening with CK2α’ crystals and an established molecular fragment collection was performed to identify new ligands at known or novel sites. It resulted in fourteen CK2α’/fragment structures. Five fragments were found at the CK2β interface of CK2α’ and three fragments at the established αD pocket, which exhibits subtle differences between CK2α and CK2α’; comparative co-crystallisations with CK2α showed that one of them binds to the αD pocket of CK2α’ exclusively. No fragments bound at the substrate-binding region of CK2α’, but a CK2α’ structure with dp10, a decameric section of the substrate-competitive inhibitor heparin, and the indenoindole-type ATP-competitive inhibitor 4w was determined. A comparison with a published CK2α/dp10 structure revealed features consistent with reports about substrate specificity differences between the isoenzymes: dp10 binds to CK2α’ and CK2α with opposite strand orientations, and the local conformations of the isoenzymes in the helix αD region are significantly different. Full article

Fruit sclerotiniosis (FS) is becoming the most important disease in recently transformed mulberry fruit gardens (TFMGs), where traditional production of mulberry leaves for sericulture takes place; FS has a long history as a secondary disease in Zhejiang province, China. Thiophanate-methyl and boscalid are the two main fungicides adopted for the management of FS in these gardens. A decrease in efficacy has been observed by growers and local technicians. For this new situation regarding TFMGs, however, the resistance status of them has not yet been investigated and reported. In the present study, pathogens were isolated from diseased fruits and identified through a combination of morphological characteristics with ITS sequence analysis. Results showed that the pathogens included Scleromitrula shiraiana and Sclerotinia sclerotiorum. All isolates of S. shiraiana (n = 12) and S. sclerotiorum (n = 12) were sensitive to boscalid, and no resistance was detected. The S. shiraiana sub-population was sensitive to thiophanate-methyl, whereas the S. sclerotiorum sub-population developed resistance with a frequency of 33.3%. Thiophanate-methyl-resistant (Th^R) S. sclerotiorum grew faster at a low temperature (15 °C) than sensitive ones. These Th^R exhibited negative cross-resistance to diethofencarb, as previously observed in Botrytis cinerea, and showed no cross-resistance to procymidone or boscalid. Further studies indicated that resistance to thiophanate-methyl is caused by a novel double mutation (E198V+V349I) in the β-tubulin of S. sclerotiorum. This E198V+V349I mutation produced structural alterations in the β-tubulin protein, the action target of thiophanate-methyl, leading to reorientation of the substrate binding site and conformational change in the active pocket. In conclusion, avoiding the sole use of thiophanate-methyl on TFMGs is necessary. Application of boscalid in combination or rotation with other fungicides without cross-resistance is recommended for the management of FS in TFMG practices. Full article

Sestrin2 (SESN2) is a highly conserved stress-inducible protein that serves as a central hub for integrating cellular responses to nutrient availability, oxidative stress, and endoplasmic reticulum (ER) stress. A key function of SESN2 is its role as a direct sensor for the branched-chain amino acid (BCAA) leucine, which modulates the activity of the mechanistic target of rapamycin complex 1 (mTORC1), a master regulator of cell growth and metabolism. While the functional link between leucine and SESN2 is well-established, the precise molecular determinants that confer its high specificity for leucine over other BCAAs, such as isoleucine and valine, remain poorly understood. This study employs an integrated computational approach, spanning atomic interactions to global protein dynamics, combining molecular docking, extensive all-atom molecular dynamics (MD) simulations, and binding free energy calculations, to elucidate the structural and dynamic basis of BCAA-SESN2 recognition. Our thermodynamic analysis reveals a distinct binding affinity hierarchy (Leucine > Isoleucine > Valine), which is primarily driven by superior van der Waals interactions and the shape complementarity of leucine’s isobutyl side chain within the protein’s hydrophobic pocket. Critically, a quantitative analysis of the conformational ensemble reveals that leucine induces a dramatic collapse of the protein’s structural heterogeneity. This “conformational locking” mechanism funnels the flexible, high-entropy unbound protein—which samples 35 distinct conformations—into a sharply restricted ensemble of just 9 stable states. This four-fold reduction in conformational freedom is accompanied by a kinetic trapping effect, which significantly lowers the rate of transitions between states. This process of conformational selection stabilizes a well-defined, signaling-competent structure, providing a comprehensive, atom-to-global-scale model of SESN2’s function. In the context of these findings, this work provides a critical framework for understanding SESN2’s complex role in disease and offers a clear rationale for the design of next-generation allosteric therapeutics. Full article