Molecular dynamics (MD) simulations are powerful tools for studying biomolecular systems, but they are fundamentally limited by accessible time scales, making the study of rare events such as protein folding or ligand unbinding computationally challenging. While several enhanced-sampling methods exist to accelerate such processes, their efficacy depends critically on the choice of collective variables (CVs), which are typically intuition-driven and must be defined a priori. To address this, we introduced a unified framework that couples the recently developed OPES-eABF hybrid scheme with a data-driven machine learning workflow for automated CV discovery. We first extrapolated the applicability of OPES-eABF across systems of increasing complexity─alanine dipeptide in explicit water, chignolin folding-unfolding, and benzene unbinding from T4 (L99A) lysozyme. In all cases, the hybrid method achieved faster sampling than either of OPES or eABF alone. The efficiency of the framework was found to depend strongly on the FULLSAMPLES/PACE ratio, which regulates the balance between eABF force estimations and OPES bias depositions, wherein a ratio < 1 yielded the most reversible and ergodic sampling behavior. To improve reaction-coordinate quality, the OPES-eABF framework was integrated with a Koopman-reweighted DeepTICA-LASSO pipeline for interpretable CV construction. The machine-learned CVs successfully captured the slow folding and unbinding modes across systems, producing physically meaningful free-energy surfaces for chignolin. In the T4 lysozyme-benzene complex, the Koopman-LASSO CV yielded an unbinding free energy of 5.78 ± 0.04 kcal/mol, in close agreement with the experimental value (-5.19 ± 0.16 kcal/mol), while committor and transition-path analyses confirmed its mechanistic relevance. Moreover, accounting for the volume corrections further improved the agreement with the experimental binding affinity, resulting in a final value of -5.35 kcal/mol. Overall, this study demonstrates that the integration of OPES-eABF with data-driven CV optimization provides a robust, transferable, and physically interpretable approach for probing complex rare-event kinetics and thermodynamics in biomolecular systems.

Natural protein sequences somehow encode the structural forms that these molecules adopt. Recent developments in structure prediction are agnostic to the mechanisms by which proteins fold and represent them as static objects. However, the amino acid sequences also encode information about how the folding process can happen and how variations in the sequences impact on the populations of the distinct structural forms that proteins acquire. Here, we present a method to infer protein folding dynamics based only on sequence information. For this, we will rely first on the obtention of a precise "evolutionary field" from the observed variations in the sequences of homologous proteins. We then show how to map the energetics to a coarse-grained folding model where the protein is treated as a string of foldons that interact. We then describe how, for any given protein sequence of a family, the equilibrium folding curve can be computed and how the emergence of protein-folding subdomains can be identified. We finally present protocols to analyze how mutations perturb both the folding stability and the cooperativity, which represent predictions for a deep mutational scan of a protein of interest.A Google Colab implementation of the method is available at https://colab.research.google.com/github/eagalpern/folding-ising-globular/blob/master/notebooks/custom_potts_simulation_colab.ipynb .

Potential protein biomarkers for heat tolerance in wheat at seedling and ear peep stages.

Metal ion-mediated redox process and protein folding are fundamental to numerous physiological functions. However, the mechanisms underlying copper ion interactions with macromolecules remain insufficiently understood, and the therapeutic potential of polymer-copper complexes is largely underexplored. Here, a biomimetic metallopolymer is reported in which copper ion coordination induces a conformational transition from β-sheet to α-helix, accompanied by oxidative self-encapsulation and fluorescence quenching. By leveraging the tunable intrinsic fluorescence of the polymer, the first systematic elucidation of the coordination interaction mechanism between polythiols and copper ions is presented. This interaction enhances the structural stability, catalytic efficiency, membrane activity, and drug loading capacity. Furthermore, the polymer-copper complex demonstrates tumor-activated fluorescence and dual enzyme-mimetic activities, enabling precise tumor imaging and multimodal therapeutic efficacy both in vitro and in vivo. This work provides new insights into the interactions between macromolecules and metal ions and establishes a versatile and intelligent nanosystem for advanced disease diagnostics and therapeutics.

Endoplasmic reticulum stress orchestrates cGAS-STING activation in lipid metabolism-associated disorders.

Mammalian cell lines are the preferred host cells for the biopharmaceutical industry. Chinese hamster ovary (CHO) and human embryonic kidney 293 (HEK293) cells are frequently utilized in the production of recombinant therapeutic proteins (RTPs) owing to their capacity to facilitate appropriate protein folding and perform accurate post-translational modifications (PTMs). However, there are still some bottlenecks in the biopharmaceutical process using mammalian cells, including lower productivity, higher production cost and increased risk of contamination compared with bacterial or yeast expression systems. In addition to vector, media and bioprocess optimization, advances in host cell engineering including gene overexpression, knockout and knockdown have significantly advanced cell-line development. Besides targeting known genes and pathways, miRNA engineering and omics analysis are also employed to enhance mammalian cell culture performance. Optimization of cell engineering and production processes further drives the development of more efficient mammalian cell expression systems. This review systematically summarizes key achievements in cell engineering and offers insights into potential targets and pathways for improving therapeutic protein production in mammalian cells.

MDG1-mediated transcriptional reprogramming enhances cellulase production and alters thermal activity in recombinant Saccharomyces cerevisiae.

Optimizing protein folding in prokaryotes: Strategies to enhance soluble expression of recombinant proteins.

KLHL13 functional defects cause neurodevelopmental disorder in humans that can be rescued via inhibition of AURKB in cellular and animal models.

N-glycosylation driven destabilization of BRASSINOSTEROID INSENSITIVE1 (BRI1) and BRI1-ASSOCIATED RECEPTOR KINASE1 (BAK1) in tomato during Ralstonia solanacearum infection.

New moonlighting activities for various GroEL/Hsp60 proteins, mainly characterized using recombinant M. tuberculosis GroEL1.

Protein folding

Background: Leukemia is a major cause of cancer-related morbidity and mortality, representing the most common childhood malignancy and remaining associated with poor outcomes and relapse in adult acute subtypes. Despite therapeutic progress in leukemia management, survival outcomes remain poor, highlighting the urgent need for reliable biomarkers and novel therapeutic targets. Heat shock protein 70 (HSP70), a molecular chaperone central to protein folding, apoptosis regulation, and oncogenic signalling, has been increasingly implicated in leukemogenesis and therapy resistance. However, the clinical utility of HSP70 remains uncertain due to small cohorts and methodological heterogeneity across studies. This systematic review and meta-analysis provides a quantitative evaluation of HSP70 expression in leukemia and explores its potential as a diagnostic, prognostic, and therapeutic biomarker. Methods: A systematic review was conducted in accordance with PRISMA guidelines. EBSCO, PubMed, ProQuest, ScienceDirect, and Scopus were searched for studies reporting quantitative HSP70 expression in leukemia and healthy controls. Eligible studies met predefined inclusion and exclusion criteria. Data were pooled using a random-effects model, with effect sizes calculated as standardised mean differences (Cohen’s d), and statistical analyses performed in IBM SPSS Statistics (v28.0). Results: After applying eligibility criteria, data from 261 leukemia patients and 214 healthy controls across three studies (four datasets) were included. Pooled analysis demonstrated significantly elevated HSP70 expression in leukemia patients compared with controls (Cohen’s d = 1.498, 95% CI: 1.279 - 1.717, p &lt; 0.001), representing a large effect size. No heterogeneity was observed (I² = 0%). The study was prospectively registered with PROSPERO (CRD420251019261). Conclusion: This meta-analysis demonstrates consistent overexpression of HSP70 in leukemia, supporting its potential as a diagnostic, prognostic, and therapeutic biomarker, while highlighting the need for further clinical validation.

Pathogenic fungi pose major threats to both global food security and human health, yet the molecular basis of their virulence remains only partially understood. Beyond genetic and transcriptional control, emerging evidence highlights protein glycosylation as a key post-translational modification that governs fungal development, stress adaptation, and host interactions. Glycosylation regulates protein folding, stability, trafficking, and immune evasion, thereby shaping infection processes across diverse pathogens. While extensively studied in model organisms, our understanding of glycosylation in pathogenic fungi remains fragmented and lacks a coherent framework linking glycosylation dynamics to fungal development and pathogenicity. This review synthesizes recent advances from proteomic, transcriptomic, and glycomic studies in pathogenic fungi, focusing on interspecific variation in glycogenes and enzymes, hierarchical regulatory networks, and glycoprotein-mediated mechanisms of virulence. Finally, we outline current challenges and highlight glycosylation-targeted strategies as promising avenues for antifungal intervention.

Background/Objectives. Antibiotic resistance is an escalating global health concern, highlighting the need for innovative antibacterial strategies beyond traditional drugs. GroEL, a highly conserved bacterial chaperonin essential for protein folding and stress tolerance, represents a promising but underexplored therapeutic target. This study aimed to identify short peptides capable of binding GroEL monomers and potentially altering their function, with the long-term goal of disrupting bacterial survival mechanisms. Methods. A phage display screening of a 12-mer peptide library was performed against purified GroEL monomers, yielding five candidate peptides (G1–G5). Their interactions with GroEL were analyzed through molecular docking and molecular dynamics simulations using three-dimensional GroEL structures (1MNF, 1XCK, 8S32). Stability of binding and interaction profiles were assessed through molecular dynamics-based analyses and MM/GBSA free energy calculations. Results. Peptides G4 and G5 displayed the most stable and energetically favorable interactions, with G4–8S32 showing the strongest binding (−116.68 kcal/mol). These peptides localized near inter-subunit interfaces, suggesting potential interference with GroEL oligomerization or allosteric transitions, which are critical for its biological function. Conclusions. Our findings demonstrate that short peptides can stably bind GroEL and potentially modulate its activity. Peptides G4 and G5 represent at our knowledge the first promising scaffolds for developing a novel class of peptide-based antibacterial agents targeting conserved chaperonin systems. This work introduces a new avenue that warrants further experimental validation.

The Niemann-Pick type 1 and 2 proteins (NPC1 and NPC2) coordinate cholesterol egress from late endosomes-lysosomes (LE/LY). Proper folding, trafficking, and localization of both NPC proteins are essential for normal LE/LY cholesterol handling. Accordingly, mutations in NPC genes cause Niemann-Pick type C (NPC) disease, a progressive neurodegenerative lysosomal cholesterol storage disorder. The routes by which NPC1 reaches the LE/LY compartment in mammalian cells are not fully elucidated. Therefore, to interrogate NPC1 trafficking, we developed genome-engineered HeLa cells expressing endogenous NPC1mNeon. We demonstrate that endogenous NPC1 localizes to the LE/LY compartment and by using protein proximity-based approaches that NPC1 resides in the same membranes as Vacuolar Protein Sorting-associated protein 41 (VPS41), one of the two unique subunits of the homotypic fusion and vacuole protein sorting complex. Loss of VPS41 increases NPC1 and Lysosomal Associated Membrane Protein 1 (LAMP1) abundance. Paradoxically, this results in marked accumulation of lysosomal cholesterol and induction of sterol regulatory element-binding protein signaling. Mechanistically, using immuno-fluorescence and electron microscopy imaging in combination with a VPS41-dependent ectopic recruitment assay, we demonstrate that this is due to a shift in the localization of NPC1 and LAMP1 from LE/LY to biosynthetic vesicles called LAMP carriers. These vesicles have been recently described to transport lysosomal-destined cargo directly from the trans-Golgi (TGN) network to LE/LY. In conclusion, we identify NPC1 as a cargo for VPS41-dependent LAMP carriers that are instrumental for the delivery of NPC1 to LE/LY and maintaining cellular cholesterol homeostasis.

ConspectusNoncovalent interactions serve as the molecular glue of living systems, governing the structural complexity and dynamic balance, particularly those remarkable affinities involving cations and aromatic rings, which have attracted significant attention in protein folding and were first identified by Kebarle et al. in 1981 ( J. Phys. Chem. 1981, 85, 1814-1820). In 1990, Dougherty ( Angew. Chem., Int. Ed. 1990, 29, 915-918) introduced the term cation-π interaction to denote the affinity between positively charged cations and electron-rich π units; they have subsequently attracted widespread attention across multiple disciplines due to their strong binding force, structural stability, remarkable adaptability, and inherent charge transfer characteristics. To mimic this natural cation-π system, scientists have invested considerable efforts into developing artificial cation-π interactions, including organic ammonium, alkali metal, and aromatic cation-π systems, thereby deepening the understanding of their fundamental principles and enabling the exploration of specific functions. Despite these achievements, there are still significant challenges in the controllability of cation-π interaction modes and their structure-function relationship: (1) cation-π interactions lack fixed directionality with strength and orientation highly dependent on spatial arrangement; (2) their bonding ratios are unpredictable, and precise control over molecular order and assembly pathways remains difficult; (3) the inherent difficulty in controlling molecular order and assembly directions has so far limited the rational development of cation-π-based supramolecular assemblies and further constrained the exploration of their potential functionalities. Thus, elucidating the key parameters that govern cation-π interaction modes and their corresponding structural and functional implications is essential for the effective regulation of these interactions in complex supramolecular assembly systems and advanced materials.In this Account, we introduce the concept of controllable cation-π chemistry to emphasize precise control over modular monomer design, directed supramolecular assembly, and the realization of multifunctional applications across diverse fields. First, we highlight the outstanding achievements in designing modular cation-π monomers with fine-tuned regulation of essential parameters, including spatial positioning, interaction modes, bonding ratios, and the directionality of forces. Second, by leveraging the precision of cation-π monomers, key factors governing molecular stacking during assembly can be identified, enabling the deliberate construction and fine modulation of supramolecular architectures across one-, two-, and three-dimensional space with predictable order and organization. Finally, taking advantage of both the ordered architecture and the intrinsic characteristics of cation-π interactions, we develop a wide range of multifunctional supramolecular materials for catalytic, optical and electronic, adsorption, and biological applications. We believe that this Account will advance controllable cation-π chemistry by promoting its continued development across various disciplines within the scientific community, including chemistry, biology, and materials science.

Proteins perform diverse functions critical to cellular processes. Transitions between functional states are often regulated by post‐translational modifications (PTMs) such as phosphorylation, which dynamically influence protein structure, function, folding, and interactions. Dysregulation of PTMs can therefore contribute to diseases such as cancer and Alzheimer's. However, the structure–function relationship between proteins and their modifications remains poorly understood due to a lack of experimental structural data, the inherent diversity of PTMs, and the dynamic nature of proteins. Recent advances in deep learning, particularly AlphaFold, have transformed protein structure prediction with near‐experimental accuracy. However, it remains unclear whether these models can effectively capture PTM‐driven conformational changes, such as those induced by phosphorylation. Here, we systematically evaluated AlphaFold models (AF2, AF3‐non phospho, and AF3‐phospho) to assess their ability to predict phosphorylation‐induced structural diversity. By analyzing experimentally derived conformational ensembles, we found that all models predominantly aligned with dominant structural states, often failing to capture phosphorylation‐specific conformations. Despite its phosphorylation‐aware design, AF3‐phospho predictions provided only modest improvement over AF2 and AF3‐non phospho predictions. Our findings highlight key challenges in modeling PTM‐driven structural landscapes and underscore the need for more adaptable structure prediction frameworks capable of capturing modification‐induced conformational variability.

During the last few years, the body of data on proteins is expanding almost exponentially with the development of advanced methods for gene sequencing, protein structure determination, particularly by cryoelectron microscopy, and structure prediction using artificial intelligence-based approaches. These developments create the potential for a comprehensive exploration of the protein universe, the entirety of the proteins existing in the biosphere. Elucidation of the relationships among proteins including the most distant ones, where only the core fold is shared, is crucial for understanding protein functions, folding mechanisms, and evolution, as well as the evolution of cellular life forms and viruses. In this brief review, we discuss methods that shaped the field of protein bioinformatics, first, through comparative sequence analysis, and the recent developments in protein structure prediction that transformed the state of the art in the comparative analysis of distantly related proteins. The combination of the rapidly growing databases of genome and metagenome sequences with sensitive methods for sequence comparison and the new generation of structure analysis tools can make charting the protein universe at the structural level a realistic goal.

High-temperature requirement A (HtrA) aids in protein homeostasis by playing a key dual role as a chaperone and protease. HtrA ensures protein folding quality control during secretion and protects cells against protein aggregation by degrading misfolded proteins. HtrA proteins are typically composed of a protease domain and at least one PDZ domain, proposed to help regulate their activity and interactions with substrates. In gram-positive bacteria, HtrA contributes to critical cellular functions and has been linked to processes such as maintaining envelope integrity, stress resistance, and virulence. In addition, HtrA has been shown to contribute to the modulation of competence and biofilm dynamics as well as the degradation of host proteins in infection models. In some gram-positive bacteria, HtrA expression is regulated by two-component systems, but many HtrA upstream signals and downstream targets remain unclear. As antibiotic resistance continues to rise, HtrA is gaining attention as a promising target of inhibition for new antibacterial strategies. However, a lack of structural information, unclear regulatory mechanisms, and unknown substrates make designing effective HtrA inhibitors challenging. This review highlights these knowledge gaps and aims to spark more focused research on HtrA in gram-positive species.