Type 3 (T3) Cu proteins play essential roles in binding and activating molecular oxygen (O2) and are prevalent across all domains of life. Despite sharing the same coordination motif, T3 Cu proteins display divergent functions: hemocyanin transports O2, while tyrosinase catalyzes the hydroxylation of monophenols and the subsequent oxidation of diphenols and catechol oxidase oxidizes only diphenols. Here, we report the design and characterization of a di-Cu protein (Cu-HC4) inspired by the active sites of natural T3 Cu proteins to investigate the structural features that facilitate catalytic oxidase activity. Cu-HC4 is roughly 1/5th the size of the commercially available mushroom tyrosinase and shares only around 20% sequence identity with the T3 Cu protein templates. Notably, Cu-HC4 displays high thermostability and exhibits diphenol oxidation activity at ambient and elevated temperatures (≥60 °C). Cu-HC4 also initiates the formation of melanin polymers, mimicking melanin biosynthesis of natural tyrosinases. Mechanistic investigations demonstrate that Cu-HC4 utilizes both Cu centers cooperatively for diphenol oxidation and requires O2 for catalysis like natural Cu oxidases but follows a distinct catalytic pathway compared to those enzymes. Cryo-EM characterization of a tetrameric form of HC4 reveals slight deviations in the relative positions of the active site His residues that may account for differences in reactivity between Cu-HC4 and natural T3 Cu enzymes.

Protein-templated gold nanoclusters (Au NCs) provide an atomically precise platform for investigating ion-protein interactions and bio-template synthesis. However, the influence of protein conformational dynamics and soft molecular confinement on NCs formation is often overlooked. Here, we address this question using lysozyme, a protein with defined cysteine quantity and conformation sensitivity as template. Evidenced by circular dichroism, zeta potential measurements, and small-angle neutron scattering, conformational changes in lysozyme, manifested as residue rearrangement, unfolding, oligomerization and globular loosening, collectively modulate local microenvironment, thereby governing the coordination and growth behavior of Au species. Ostwald ripening of NCs growth captured by time-resolved optical spectroscopy and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry show atomic precision. Tuning conformation by pH, Au─S coordination and NCs ripening pathways are governed with distinct kinetic characteristics. Our findings demonstrate that dynamic conformation of the protein template actively governs ion coordination and NCs formation. Contrast to conventional heterogeneous catalyst systems, typically based on rigid, extended nanoparticle or single-atom architectures, this work provides direct evidence that ripening can proceed within soft, biomolecular confinement and is modulated by conformation. This study expands the conceptual boundaries of classical ripening theory and provides a mechanistic framework for protein-directed synthesis in soft and dynamic environments.

Data storage using proteins offers high capacity and stability, enabling utilization of protein techniques for data storage and retrieval. However, expressing unnatural proteins with random sequences for data storage and sequencing them for accurate data retrieval remain challenging. In this study, by encoding digital data into amino acid sequences and incorporating them into collagen-like protein templates, we achieve successful expression of the proteins via E. coli for data storage; the data-bearing proteins containing selective amino acids and arginine intervals can be sequenced through tryptic digestion followed by LC-MS/MS analysis to achieve complete data recovery, even for protein mixtures encoding multiple datasets. We further demonstrate much higher stability of the data-bearing protein than DNA, and random access and cryptographic data protection using affinity-tagged proteins. This work establishes a robust framework for protein-based data storage, opening up avenues for data storage and retrieval, protein engineering and chemistry, synthetic biology, proteomics, and beyond.

Cardiovascular diseases （CVDs） are among the leading cause of global morbidity and mortality. Due to their high prevalence and often asymptomatic progression， there is a pressing need for diagnostic tools that enable the early， accurate， and accessible detection of them. Acute coronary syndrome （ACS）， as a common and severe CVDs with high morbidity and mortality rates， has attracted considerable scientific interest. Various methods have been developed to detect ACS rapidly and accurately. Traditional diagnostic methods relying on antibody-based assays are effective. However， they face significant limitations， including high production costs， poor stability under varying environmental conditions， batch-to-batch variability， and cross-reactivity leading to false positives. These challenges have motivated the search for robust， cost-effective alternatives capable of detecting biomarkers with high sensitivity and specificity. Molecularly imprinted polymers （MIPs） have emerged as a promising alternative solution， offering antibody-like molecular recognition capabilities， superior stability， lower production costs， and resistance to harsh environmental conditions. This review systematically examines the latest advancements in MIP-based sensors for ACS biomarker detection in the last fifteen years， including imprinting strategies for key ACS biomarkers， sensor development and integration， and current challenges along with future perspectives. The first section focuses on the molecular imprinting techniques for essential ACS biomarkers， such as cardiac troponin （cTnI/cTnT）， myoglobin （Myo）， and creatine kinase （CK）. It compares whole-protein imprinting with epitope imprinting， highlighting the advantages of the latter in reducing template costs and enhancing binding specificity. Epitope imprinting using short peptide sequences has demonstrated femtomolar detection limits while overcoming challenges associated with large protein templates， such as structural denaturation and difficult template removal. The review also explores innovative approaches like dummy template imprinting， where structurally similar but cheaper molecules are used to create MIPs for high-cost biomarkers， achieving comparable specificity and sensitivity. The second section discusses the integration of MIPs with advanced biosensing platforms. Electrochemical sensors， using MIP-modified electrodes， have achieved remarkable sensitivity and rapid response times， making them suitable for point-of-care testing （POCT）. Optical sensors， particularly those based on surface-enhanced Raman spectroscopy and surface plasmon resonance， enable label-free， real-time detection with ultra-low detection limits. The review also addresses the integration of MIPs with microfluidic technology， where miniaturized devices facilitate automated， high-throughput biomarker analysis. Examples include paper-based microfluidic sensors that combine capillary action with MIP-SERs tags for multiplexed detection， achieving low detection limits without complex instrumentation. Despite these advancements， the review identifies key challenges hindering widespread clinical adoption of the MIP's based ACS sensor. Although the sensitivity and specificity of MIPs are impressive， they still lag behind those of monoclonal antibodies in some applications， particularly for low-abundance biomarkers. Reproducibility issues arise from variations in polymerization conditions and template removal efficiency. Commercialization barriers include the lack of standardized production protocols and regulatory frameworks for MIP-based diagnostics. The review proposes several strategic directions to address these limitations. Computational modeling and machine learning could optimize monomer selection and polymerization conditions to enhance MIP's performance. The development of hybrid systems combining MIPs with nanomaterials may further improve sensitivity and signal transduction. Multidisciplinary collaborations among chemists， engineers， and clinicians will be essential to translate laboratory innovations into commercially viable diagnostic tools. Additionally， the integration of MIPs with artificial intelligence machine learning algorithms could support the development of personalized diagnosis and treatment strategies. These future perspectives are likely to have a significant impact on the early diagnosis and treatment of cardiovascular diseases. In conclusion， MIP-based sensors represent a promising direction in ACS diagnostics， offering a unique combination of affordability， stability， and precision. By addressing current technical and translational challenges， MIP technology has the potential to revolutionize early disease detection， particularly in resource-limited areas. This review not only summarizes a decade of research progress but also provides a plan for future developments that could make personalized， decentralized cardiovascular diagnostics a widespread reality.

Screening of small-molecule drugs to suppress both protein aggregation and reactive oxygen species (ROS) generation is critical for developing therapies for neurodegenerative diseases (NDs). However, existing methods are limited to characterizing only a single pathological feature (either protein aggregation or ROS generation) in a single measurement. Herein, taking α-synuclein (α-Syn) as the template protein, we developed a dual-mode electrochemical sensing platform for concurrently monitoring protein aggregation and ROS generation characteristics. A gold electrode functionalized with α-Syn via self-assembled monolayers (SAMs) was constructed as the sensing platform, realizing both ordered α-Syn immobilization and monitoring of metal ion (e.g., Cu(ii))-driven aggregation. This was accomplished by synchronously recording the electrochemiluminescence (ECL) and cyclic voltammetry (CV) dual responses of the tris(2,2'-bipyridine) ruthenium(ii) (Ru(bpy)3 2+) reporter in a single integrated assay. The catalysis of DNA oxidation by Ru(bpy)3 2+ enables the amplification of ECL and CV dual-mode signals, which increased the detection sensitivity for both aggregation and ROS generation accompanied by the α-Syn - Cu(ii) complex. Machine learning algorithms were then utilized to analyze ECL and CV responses of small molecules with known drug effects. This analysis culminated in the development of a linear discriminant analysis (LDA) screening model, which enabled the assessment of drug efficacy against the two pathological features. The predictive capability of the screening model was verified through transmission electron microscopy (TEM), cell viability and intracellular aggregation studies. This model was further successfully applied to assess two previously unexplored small molecules: diethylenetriaminepentaacetic dianhydride (DTPA) and deferiprone. Collectively, this dual-mode sensing platform, integrating DNA-amplified monitoring of protein aggregation and ROS generation, enables the robust establishment of a machine learning-assisted small-molecule drug screening model, offering a novel approach for the in vitro characterization of protein-related pathological features.

In Silico Characterization of a Hypothetical Protein from Pseudomonas aeruginosa LESB58: A Structural and Functional Perspective

Salmonella bongori, a gram-negative, rod-shaped bacterium, is responsible for causing salmonellosis, a gastrointestinal illness marked by symptoms such as sudden fever, nausea, vomiting, cramping diarrhea, and abdominal discomfort. Identifying the relevant protein could potentially facilitate the development of effective treatments for S. bongori infection. Currently, many proteins in S. bongori remain unidentified and are called hypothetical proteins (HPs). This study aimed to elucidate the structure and function of an uncharacterized HP (accession no. QXY84013.1) from S. bongori. Analysis of subcellular localization and various physicochemical properties suggested that this protein is cytoplasmic and exhibits stability. NCBI-CD Search, the functional annotation software, indicated that our selected protein would be categorized as a constituent of the formate-dependent nitrite reductase complex, known explicitly as NrfG. NrfG, composed of tetratricopeptide repeat (TPR) proteins, plays a pivotal role in bacterial virulence, aiding in virulence factor transfer into host cells and phagolysosome maturation inhibition. Additionally, it plays a crucial role in developing the heme lyase complex (NrfEFG), potentially impacting bacterial iron acquisition and pathogenesis, thus influencing the severity of human bacterial infections. The predominant secondary structure observed was the alpha helix. Utilizing homology modeling via the SWISS-MODEL server, the protein’s threedimensional (3D) structure was determined, employing a template protein with 100% sequence similarity (PDB ID: A0A5U3DZQ4.1.A). Several quality assessment tools including ERRAT, QMEAN, and PROCHECK were used to verify the 3D structure. Furthermore, the modeled structure’s active site was predicted using the CASTp server. These findings hold promise as a potential foundation for the development of future antibacterial treatments. FENI UNIVERSITY JOURNAL, 2024, 3(1), ISSN [2518-3869], PP. (1-30)

Understanding protein functions is essential for advancing quantitative synthetic biology, which applies quantitative and systems approaches to understand how biological functions emerge from building blocks, thereby guiding the rational design of complex living systems. Apart from a few model organisms, most species contain many proteins with unverified functions, highlighting the need for accurate, automated protein function annotation methods. Recent advances in protein bioinformatics, particularly in predicting structures and functions, have been driven by artificial intelligence (AI), especially deep learning models. Top-performing methods in the Critical Assessment of Function Annotation (CAFA) challenge have leveraged large language models to perform text mining-based protein function prediction, extracting features from scientific literature or using template proteins with similar descriptions in the literature. Despite these advances, several challenges remain. Current predictors often depend on PubMed abstracts curated by UniProt, leading to redundancy with manual annotations and to the overlooking of uncurated or full-text literature that contains richer functional evidence. Few systems automatically classify literature types or assess their relevance, limiting precision and interpretability. Benchmarking remains difficult due to the absence of unbiased gold standards, making it hard to evaluate true predictive capability. Furthermore, integrating heterogeneous evidence—from text, sequences, and structural or network data—presents additional challenges for model harmonization. This review not only summarizes current methods and limitations but also highlights strategies to improve text mining-based protein function annotation using recent AI developments. Overall, this work aims to guide the development of next-generation tools for more accurate and comprehensive protein function predictions.

Nature routinely exploits liquid-liquid phase separation (LLPS) of proteins to control the assembly and mineralization of hybrid materials. Here, we show that fusion of the Car9 silica-binding peptide to an elastin-like polypeptide (ELP) yields temperature- and sequence-programmable soft matter templates for the synthesis of silicified architectures ranging in size from nanometers to micrometers. Specifically, we demonstrate unprecedented control over the diameter of silica nanoparticles (SiNP) in the 30-60 nm range with 4 nm precision, show that a single arginine residue (R4) in the Car9 sequence underpins the transition from micelles to proteinosomes, and find that substitutions in other basic residues modulate electrostatic repulsion and solvation to enable access to kinetically trapped species. These structures, which include interconnected micelles, small (∼200 nm) and large (>5 µm) vesicles, are readily visualized by SEM imaging following silicification. Molecular dynamics (MD) simulations and AlphaFold predictions reveal that mutations in positively charged residues alter interfacial packing, hydration, and conformational freedom of the silica-binding segments. Overall, our results establish sequence and thermal energy as synergistic levers for morphological control across length scales using solid-binding ELPs and establish mineralization as a powerful tool to visualize the structure of dynamic soft matter assemblies.

Supramolecular proteins have emerged as promising templates for guiding metal ion mineralization into well-defined nanomaterials because of their structural versatility and chemical diversity. However, the precise control of metal ion nucleation on the different reactive sites of protein templates remains challenging. In this study, a genetically engineered hollow tobacco mosaic virus protein fiber (TMVF) with excellent structural stability was employed to achieve selective mineralization of noble metal nanostructures either on its external surface or within its internal channel. Moreover, the Pt/Pd bimetallic nanowire (NW) was also successfully prepared by co-depositing Pt and Pd on the TMVF. The bimetallic NWs demonstrated a peroxidase-like activity, which enabled their application for cholesterol detection by cooperating with cholesterol oxidase.

Biomimetic synthesis of strontium-substituted mesoporous platelet-like hydroxyapatite composites using gelatin type B as a protein template for bone regeneration applications

Ongoing advances in force field and computer hardware development enable the use of molecular dynamics (MD) to simulate increasingly complex systems with the ultimate goal of reaching cellular complexity. At the same time, rational design by high-throughput (HT) simulations is another forefront of MD. In these areas, the Martini coarse-grained force field, especially the latest version (i.e. v3), is being actively explored because it offers an enhanced spatial-temporal resolution. However, the automation tools for preparing simulations with the Martini force field, accompanying the previous version, were not designed for HT simulations or studies of complex cellular systems. Therefore, they become a major limiting factor. To address these shortcomings, we present the open-source Vermouth python library. Vermouth is designed to become the unified framework for developing programs, which prepare, run, and analyze Martini simulations of complex systems. To demonstrate the power of the Vermouth library, the Martinize2 program is showcased as a generalization of the martinize script, originally aimed to set up simulations of proteins. In contrast to the previous version, Martinize2 automatically handles protonation states in proteins and post-translation modifications, offers more options to fine-tune structural biases such as the elastic network (EN), and can convert non-protein molecules such as ligands. Finally, Martinize2 is used in two high-complexity benchmarks. The entire I-TASSER protein template database as well as a subset of 200,000 structures from the AlphaFold Protein Structure Database are converted to CG resolution and we illustrate how the checks on input structure quality can safeguard HT applications.

Ongoing advances in force field and computer hardware development enable the use of molecular dynamics (MD) to simulate increasingly complex systems with the ultimate goal of reaching cellular complexity. At the same time, rational design by high-throughput (HT) simulations is another forefront of MD. In these areas, the Martini coarse-grained force field, especially the latest version (i.e. v3), is being actively explored because it offers an enhanced spatial-temporal resolution. However, the automation tools for preparing simulations with the Martini force field, accompanying the previous version, were not designed for HT simulations or studies of complex cellular systems. Therefore, they become a major limiting factor. To address these shortcomings, we present the open-source Vermouth python library. Vermouth is designed to become the unified framework for developing programs, which prepare, run, and analyze Martini simulations of complex systems. To demonstrate the power of the Vermouth library, the Martinize2 program is showcased as a generalization of the martinize script, originally aimed to set up simulations of proteins. In contrast to the previous version, Martinize2 automatically handles protonation states in proteins and post-translation modifications, offers more options to fine-tune structural biases such as the elastic network (EN), and can convert non-protein molecules such as ligands. Finally, Martinize2 is used in two high-complexity benchmarks. The entire I-TASSER protein template database as well as a subset of 200,000 structures from the AlphaFold Protein Structure Database are converted to CG resolution and we illustrate how the checks on input structure quality can safeguard HT applications.

The search for novel low-molecular regulators using molecular docking continues to be crucial for addressing challenges in modern biomedical science. However, the current literature lacks examples of modeling covalent interactions between the ligands being docked and those already present within the proteins, such as enzyme cofactors. This study aims to improve the existing algorithms for modeling such interactions, exemplified by those in thiamine diphosphate (ThDP)-dependent enzymes. Structures containing adducts of ThDP with enzyme substrates or inhibitors are used as protein templates; the putative ligand models are prepared as (R)- or (S)-hydroxy derivatives. The Gnina framework with AD4 or Vinardo favors ligand conformations resembling those found in the protein templates and consistent with their relative inhibitory potentials in experiments in vitro. For example, local hydrophobic regions within pyruvate and branched-chain 2-oxo acid dehydrogenase structures favor the binding of esterified substrate analogs compared to their de-esterified counterparts. The preferred binding of esterified vs. de-esterified ligands is absent or even reversed for 2-oxoglutarate dehydrogenase. As a result, covalent docking of 2-oxo acid analogs to enzyme structures containing ThDP coenzyme offers a predictive capability for protein–ligand complex formation and should be used when inhibitors mimic transition states in enzymatic reactions, as observed with ThDP-dependent catalysis.

Quaternary carbon stereocenters are a crucial component of many bioactive molecules, but they can be challenging to prepare stereoselectively. Olefin hydroarylations are an attractive means for preparing this motif; however, existing methods struggle to set stereocenters on substrates lacking traditional catalyst binding handles. Here, we report a stereoselective photoenzymatic olefin hydroarylation using a repurposed Baeyer-Villiger monooxygenase. Three rounds of iterative site-saturation mutagenesis yielded a photoenzyme capable of preparing valuable tetrahydroquinolines in high yields with excellent enantioselectivity. The engineered variant accepts various arene substituents, highlighting the synthetic utility of this methodology. DFT calculations and control experiments suggest that the protein templates a through-space interaction between the tertiary radical and aromatic group, which attenuates the oxidation potential of the radical, enabling C-C bond formation.

The selective modification of natural protein templates has emerged as a powerful tool for investigating the protein structure and function as well as for designing therapeutic bioconjugates. While significant progress has been made in modifying protein side chains and terminal groups, backbone modifications remain underexplored due to the inherent inertness of amide bonds and the challenge of achieving site specificity. Despite the critical role of the backbone in the protein function, its selective chemical modification under physiological conditions has proven to be difficult. With this research, we introduce a site-specific, chemoselective, and two-step strategy for protein backbone modification via thiol/amine coupling and cyclization reactions driven by the release of volatile methyl mercaptans via a small-molecular conjugate acceptor, operating on small-molecule cysteine mimics, peptides, and proteins. This approach leverages the unique reactivity of the conjugate acceptor to first couple a cysteine residue, followed by intramolecular cyclization with an adjacent amide, forming a five-membered heterocyclic unit under aqueous conditions without requiring catalysts or heating. Additionally, molecular dynamics simulations reveal that the resulting rigid structure induces a local backbone torsion, hydrogen bond disruption, and altered side-chain orientation, thereby influencing protein folding. Preliminary investigations further explore the consequent changes in the protein thermal stability and enzymatic activity induced by backbone modification. More importantly, the process is accompanied by a fluorescence turn-on signal, enabling the real-time in situ monitoring of the modification process. Thus, this unique strategy offers a new platform for backbone-specific chemical modifications, paving the way for potential protein and peptide functional studies, fluorogenic labeling, and the development of novel bioconjugates.

Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, driving extensive research into enzymatic biodegradation. However, existing PET hydrolases (PETases) are limited by narrow sequence diversity and suboptimal performance. This study introduces VenusMine, a protein discovery pipeline that integrates protein language models (PLMs) with a representation tree to identify PETases based on structural similarity using sequence information. Using the crystal structure of IsPETase as a template, VenusMine identifies and clusters target proteins. Candidates are further screened using PLM-based assessments of solubility and thermostability, leading to the selection of 34 proteins for biochemical validation. Results reveal that 14 candidates exhibit PET degradation activity across 30–60 °C. Notably, a PET hydrolase from Kibdelosporangium banguiense (KbPETase) demonstrates a melting temperature (Tm) 32 °C higher than IsPETase and exhibits the highest PET degradation activity within 30 – 65 °C among wild-type PETases. KbPETase also surpasses FastPETase and LCC in catalytic efficiency. X-ray crystallography and molecular dynamics simulations show that KbPETase possesses a conserved catalytic domain and enhanced intramolecular interactions, underpinning its improved functionality and thermostability. This work demonstrates a novel deep learning approach for discovering natural PETases with enhanced properties.

BK polyomavirus (BKV) lies latent and asymptomatic within the human populace today. However, when activated, most commonly in renal transplant recipients, the virus replicates within the body causing cell death and eventually organ dysfunction. This work, reports on the novel synthesis, characterization and application of multi-shaped ZnTeSe@CoCu core/shell quantum dots (QDs) for the rapid, ultrasensitive and selective electrochemical impedimetric detection of BKV VP1 protein with surface imprinted poly(o-phenylenediamine)-BKV VP1 protein on screen-printed gold electrode (SPAuE) integrated on a portable hand-held electrochemical device. The QDs were synthesized using a combination of semiconducting metals as the core and the shell layer being composed of electroactive metals, thus, making the QDs material to be both fluorescent and electrochemically conductive. The QDs surface morphology was characterized of spherical, rod, cubic, pentagonal and hexagonal-shaped particles. Electropolymerization via cyclic voltammetry was used to functionalise the QDs/SPAuE surface with molecularly imprinted polymer (MIP) with o-phenylenediamine being used as the functional monomer to imprint the BKV VP1 protein template. Under optimum reaction conditions, impedimetric detection of BKV VP1 protein was achieved using the MIP@QDs/SPAuE within the linear range of 0.1 fg/mL to 100 pg/mL and a detection limit of 0.51 pg/mL was obtained. BKV VP1 protein was successfully detected in human serum and thus proved the potential of the MIP@QDs/SPAuE for BKV VP1 protein detection in complex biological matrix. • MPA-capped ZnSeTe@CoCu core/shell quantum dots (QDs) were synthesized. • Screen-printed gold electrode (SPAuE) was modified with the QDs. • A poly(o-phenylenediamine)-BK polyomavirus (BKV) incorporated QDs/SPAuE was developed. • BK polyomavirus was successfully detected using the MIP@QDs/SPAuE sensor. • BKV was successfully detected in human serum.

Abstract The unique plasmonic resonance properties, surface‐enhanced catalytic efficiency, and exceptional chemical inertness of gold nanoparticles (AuNPs) make them highly promising for a wide range of interdisciplinary applications. A critical factor in their functional utility is the precise spatial organization of AuNPs, where controlled assembly enhances emergent properties—such as collective plasmon coupling for sub‐wavelength light manipulation, amplified catalytic hot‐spot generation, and programmable mechanical responsiveness—that are unattainable in isolated particles. Despite these advantages, achieving precise architectural control over AuNPs remains a significant challenge. Multidimensional protein templates offer a compelling solution, exploiting stereochemical specificity to direct AuNP assembly or Au3+ reduction into gold nanostructures (AuNSs) with tunable dimensionality—1D nanowires, 2D arrays, and 3D crystals. This review systematically assesses recent advancements and the current state of AuNSs directed by protein templates, encompassing strong bond‐like, relatively weak, and noncovalent interactions, and the latest strategies that facilitate the formation of multidimensional AuNSs. Additionally, the unique properties and applications of AuNSs in sensing, catalysis, solar cells, biofuel cells, bioimaging, and tissue engineering are discussed. Finally, key challenges and future opportunities—including precise multidimensional assembly, environmental stability, manufacturing scalability, and the integration of theory‐driven research paradigms—are discussed.

A general method to improve imprinting efficiency in surface protein imprinting by enhanced pre-assembly.