Search Results (99)

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article

Potassium (K⁺) is a vital macronutrient for plant growth and stress resilience, with KT/HAK/KUP transporters playing a central role in its homeostasis. Although these transporters are known to influence photosynthesis, the molecular mechanisms by which fertilization promotes assimilate accumulation in peach crops remain poorly understood. In this study, 17 PpHAK genes were identified based on the peach genome and classified into four distinct clades through phylogenetic analysis, a classification further supported by conserved gene structures and motifs. Interspecific collinearity analysis revealed that transporters are highly conserved among Rosaceae species. Physiological measurements demonstrated that foliar application significantly enhanced photosynthetic capacity, as evidenced by a 33% increase in net photosynthetic rate (Pn) and improved photoelectron yield (Y(II)). At the same time, the transcript levels of the transporters PpHAK1, PpHAK5, and PpHAK9 were significantly upregulated, as confirmed by quantitative real-time RT-PCR (qRT-PCR) analysis. Furthermore, the expression of genes involved in sugar metabolism and transport, particularly PpPLT5-1, was significantly induced. Collectively, these results indicate that foliar K⁺ application enhances photosynthesis and promotes assimilate accumulation by modulating the expression of both K⁺ and sugar transporters. These findings offer a theoretical basis for optimizing nutrient management to improve fruit quality in stone fruit production. Full article

Metal-free electrodes are essential to promote electrochemical reactions, the core of sustainable energy resources. In search of better carbon-based electrode materials, we have explored several spatial arrangements of boron (B) within proximity in the graphene lattice, as evident in recent experimental observations. Multi-boron substitution enriches sites by tuning electronic structure and strengthens binding of key intermediates of oxygen reduction, oxygen evolution, and hydrogen evolution reactions facilitating electrocatalytic performance. Our optimal B-doped site shows near thermo-neutral H adsorption ($\Delta G_{H^{*}}$∼$\pm 0.4\mathrm{eV}$), consistent with experiments. The overpotentials are highly sensitive to the dopant motifs and the spread among configurations shows that experimentally accessible multi-B doping can serve as a practical active site engineering knob to achieve optimized multi-functional performance. In parallel, we find that specific multi-B configurations selectively capture and pre-activate NO_x (NO/NO₂) under ambient conditions while retaining weak affinity for NH₃. These sites also interact with SO₂ and related hazardous species, enabling selective air filtration and targeted NO_x control within the electrocatalytic scope of this study. Full article

Background/Objectives: Cancer remains one of the most significant challenges in modern medicine, requiring the continuous development of novel molecular scaffolds with anticancer potential that act through multiple pathways. Heterocyclic compounds incorporating indole, triazole, oxadiazole, and thiadiazine motifs have attracted considerable attention due to their diverse pharmacological activities. This study aimed to design, synthesize, and evaluate new hybrid heterocyclic systems, including 1,2,4-triazole, 1,3,4-oxadiazole, and thiadiazine motifs, targeting liver and breast cancer. Methods: A series of indolyl-based heterocyclic compounds was synthesized using efficient and environmentally friendly protocols. Indolyl-triazol-thiadiazin-6-ol 5 was prepared via solvent-free fusion of esters 2 and 3 or the corresponding acid 4. Oxadiazole derivatives were produced by reacting hydrazide intermediates with carbon disulfide. Triazole derivatives were synthesized via cylization of thiosemicarbazide 9 in aqueous KOH (4.0 N). Structural characterization was performed using Fourier Transform InfraRed (FTIR), ¹H and ¹³C NMR spectroscopy, and electron impact mass spectrometry (EIMS). Cytotoxic activity was evaluated against liver and breast cancer cell lines, and VEGFR-2 kinase inhibition was assessed for selected derivatives. Results: The synthesized compounds demonstrated notable cytotoxicity activity, with compounds 4, 5, and 9 exhibiting IC₅₀ values in the low micromolar range. Enzymatic assays revealed that compounds 4 and 9 showed strong VEGFR-2 inhibition (97.9% and 96.4%, respectively), indicating apoptosis-inducing effects. Conclusions: The synthesized indolyl-based hybrid heterocycles represent a promising chemotype with in vitro cytotoxic activity and VEGFR-2 inhibitory effects, supporting further investigation, optimization, and mechanistic studies to evaluate their potential lead for anticancer drug development. Full article

DNA sequence classification is a fundamental problem in bioinformatics, playing an indispensable role in gene annotation and disease prediction. Whereas most deep learning models, such as CNNs, BiLSTM networks, and GRUs, have been found individually optimal, each of these methods excels in modeling a specific aspect of sequence data: local motifs, long-range dependencies, and efficient temporal modeling of the sequences. Here, we present and evaluate an ensemble model that integrates CNN, BiLSTM, and GRU architectures via a majority voting combination scheme so that their complementary strengths can be harnessed. We trained and evaluated each standalone and the integrated model on a DNA dataset comprising 4380 sequences falling under five functional categories. The ensemble model achieved a classification accuracy of 90.6% with precision, recall, and F1 score equal to 0.91, thereby outperforming the state-of-the-art techniques by large margins. Although previous studies have tried analyzing each Deep Learning method individually for DNA classification tasks, none have attempted a systematic combination of CNN, BiLSTM, and GRU based on their ability to extract features simultaneously. The current research aims at presenting a novel method that combines these architectures based on a Majority Voting strategy and proves how their combination is better at extracting local patterns and long dependency information when compared individually. In particular, the proposed ensemble model smoothed the high recall of BiLSTM with the high precision of CNN, leading to more robust and reliable classification. The experiments involved a publicly available DNA sequence data set of 4380 sequences distributed over 5 classes. Our results emphasized the prospect of hybrid ensemble deep learning as a strong approach for complex genomic data analysis, opening ways toward more accurate and interpretable bioinformatics research. Full article

Against the backdrop of China’s commitment to achieving carbon peaking by 2030 and carbon neutrality by 2060, inter-provincial carbon emissions form a complex interconnected spatial network—clarifying its operational mechanisms is crucial for optimizing regional carbon reduction strategies. Based on 2006–2021 data from 30 Chinese provinces, this study constructs the China Provincial Carbon Emission Spatial Correlation Network (CPCESCN) using a modified gravity model. Social Network Analysis (SNA) explores its structural characteristics, while motif and QAP correlation analyses identify endogenous structural and attribute variables. Innovatively integrating Exponential Random Graph Models (ERGM) and Stochastic Actor-Oriented Models (SAOM), it investigates the network’s static formation mechanisms and dynamic evolution drivers. Results show CPCESCN has a stable multi-threaded structure without isolated nodes, with Jiangsu, Guangdong, Shandong, Zhejiang, Henan, and Sichuan as high-centrality core nodes with high centrality. GDP, green technology innovation, urbanization rate, industrialization rate, energy consumption intensity, and environmental regulations significantly influence network dynamics, with reciprocal relationships as key endogenous drivers. While geographic proximity still facilitates network formation, its impact has weakened notably, and functional complementarity has become the dominant evolutionary driver—based on the findings, policy suggestions are proposed, including deepening inter-provincial functional cooperation, implementing differentiated carbon reduction policies, and optimizing multi-dimensional low-carbon transformation systems. Full article

Scaffold-aware artificial intelligence (AI) models enable systematic exploration of chemical space conditioned on protein-interacting ligands, yet the representational principles governing their behavior remain poorly understood. The computational representation of structurally complex kinase small molecules remains a formidable challenge due to the high conservation of ATP active site architecture across the kinome and the topological complexity of structural scaffolds in current generative AI frameworks. In this study, we present a diagnostic, modular and chemistry-first generative framework for design of targeted SRC kinase ligands by integrating ChemVAE-based latent space modeling, a chemically interpretable structural similarity metric (Kinase Likelihood Score), Bayesian optimization, and cluster-guided local neighborhood sampling. Using a comprehensive dataset of protein kinase ligands, we examine scaffold topology, latent-space geometry, and model-driven generative trajectories. We show that chemically distinct scaffolds can converge toward overlapping latent representations, revealing intrinsic degeneracy in scaffold encoding, while specific topological motifs function as organizing anchors that constrain generative diversification. The results demonstrate that kinase scaffolds spanning 37 protein kinase families spontaneously organize into a coherent, low-dimensional manifold in latent space, with SRC-like scaffolds acting as a structural “hub” that enables rational scaffold transformation. Our local sampling approach successfully converts scaffolds from other kinase families (notably LCK) into novel SRC-like chemotypes, with LCK-derived molecules accounting for ~40% of high-similarity outputs. However, both generative strategies reveal a critical limitation: SMILES-based representations systematically fail to recover multi-ring aromatic systems—a topological hallmark of kinase chemotypes—despite ring count being a top feature in our structural similarity metric. This “representation gap” demonstrates that no amount of scoring refinement can compensate for a generative engine that cannot access topologically constrained regions. By diagnosing these constraints within a transparent pipeline and reframing scaffold-aware ligand design as a problem of molecular representation our work provides a conceptual framework for interpreting generative model behavior and for guiding the incorporation of structural priors into future molecular AI architectures. Full article

Codon optimization is widely used to improve heterologous gene expression in Escherichia coli. However, many existing methods focus primarily on maximizing the codon adaptation index (CAI) and neglect broader aspects of biological context. In this study, we present ColiFormer, a transformer-based codon optimization framework fine-tuned on 3676 high-expression E. coli genes curated from the NCBI database. Built on the CodonTransformer BigBird architecture, ColiFormer employs self-attention mechanisms and a mathematical optimization method (the augmented Lagrangian approach) to balance multiple biological objectives simultaneously, including CAI, GC content, tRNA adaptation index (tAI), RNA stability, and minimization of negative cis-regulatory elements. Based on in silico evaluations on 37,053 native E. coli genes and 80 recombinant protein targets commonly used in industrial studies, ColiFormer demonstrated significant improvements in CAI and tAI values, maintained GC content within biologically optimal ranges, and reduced inhibitory cis-regulatory motifs compared with established codon optimization approaches, while maintaining competitive runtime performance. These results represent computational predictions derived from standard in silico metrics; future experimental work is anticipated to validate these computational predictions in vivo. ColiFormer has been released as an open-source tool alongside the benchmark datasets used in this study. Full article

Peptide-based biomaterials have emerged as versatile tools for pharmaceutical drug delivery due to their biocompatibility and tunable sequences, yet a comprehensive overview of their categories, mechanisms, and optimization strategies remains lacking to guide clinical translation. This review systematically collates advances in peptide-based biomaterials, covering peptide excipients (cell penetrating peptides, tight junction modulating peptides, and peptide surfactants/stabilizers), self-assembling peptides (peptide-based nanospheres, cyclic peptide nanotubes, nanovesicles and micelles, peptide-based hydrogels and depots), and peptide linkers (for antibody drug-conjugates, peptide drug-conjugates, and prodrugs). We also dissect sequence-based optimization strategies, including rational design and biophysical optimization (cyclization, stapling, D-amino acid incorporation), functional motif integration, and combinatorial discovery with AI assistance, with examples spanning marketed drugs and research-stage candidates. The review reveals that cell-penetrating peptides enable efficient intracellular payload delivery via direct penetration or endocytosis; self-assembling peptides form diverse nanostructures for controlled release; and peptide linkers achieve site-specific drug release by responding to tumor-associated enzymes or pH cues, while sequence optimization enhances stability and targeting. Peptide-based biomaterials offer precise, biocompatible and tunable solutions for drug delivery, future advancements relying on AI-driven design and multi-functional modification will accelerate their transition from basic research to clinical application. Full article

Breast cancer remains the most commonly diagnosed malignancy among women worldwide and continues to pose significant therapeutic challenges, particularly in advanced and refractory disease. Although traditionally considered less immunogenic compared with other solid tumours, growing evidence demonstrates that subsets of breast cancer, particularly triple-negative and HER2-positive subtypes, exhibit immune-responsive features. This recognition has spurred the development and clinical evaluation of immunotherapeutic strategies, with immune checkpoint inhibitors (ICIs) emerging as the most prominent approach. This new class of drugs targeting the programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) axis has demonstrated meaningful clinical activity in select patient populations, leading to regulatory approvals in combination with chemotherapy for advanced triple-negative breast cancer. Despite these advances, response rates remain modest, and the benefits are largely restricted to patients with PD-L1-positive tumours. Ongoing studies are evaluating predictive biomarkers, optimal treatment combinations, and mechanisms of resistance to expand the efficacy of ICIs across broader breast cancer subtypes. Furthermore, novel checkpoint targets such as cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), lymphocyte-activation gene 3 (LAG-3), and T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT) are under investigation, with the potential to enhance or complement PD-1/PD-L1 blockade. This review summarises the current state of knowledge on breast cancer immunotherapy with an emphasis on ICIs, highlighting key clinical trial findings, as well as emerging biomarkers of response, and strategies to overcome therapeutic resistance, if cancer cells eventually develop resistance. By integrating preclinical insights with clinical progress, we aim to provide a comprehensive overview of the evolving role of checkpoint blockade in breast cancer and outline future directions to optimise patient outcomes. Full article

The role of alternative nucleic acid structures (ANS) in biology is an area of increasing interest. These non-canonical structures include the Z-DNA and Z-RNA duplexes (ZNA), the three-stranded triplex, the four-stranded G-quadruplex (GQ), and i-motifs. Previously, the biological relevance of ANS was dismissed. Their formation in vitro often required non-physiological conditions, and there was no genetic evidence for their function. Further, structural studies confirmed that sequence-specific transcription factors (TFs) bound B-DNA. In contrast, ANS are formed dynamically by a subset of repeat sequences, called flipons. The flip requires energy, but not strand cleavage. Flipons are enriched in promoters where they modulate transcription. Here, computational modeling based on AlphaFold V3 (AF3), under optimized conditions, reveals that known B-DNA-binding TFs also dock to ANS, such as ZNA and GQ. The binding of HLH and bZIP homodimers to Z-DNA is promoted by methylarginine modifications. Heterodimers only bind preformed Z-DNA. The interactions of TFs with ANS likely enhance genome scanning to identify cognate B-DNA-binding sites in active genes. Docking of TF homodimers to Z-DNA potentially facilitates the assembly of heterodimers that dissociate and are stabilized by binding to a cognate B-DNA motif. The process enables rapid discovery of the optimal heterodimer combinations required to regulate a nearby promoter. Full article

Viral fusion proteins are indispensable mediators of viral entry that orchestrate the fusion of viral and host membranes, making them primary targets for antiviral interventions. Class I fusion proteins, displayed on the surface of enveloped viruses (such as HIV-1, RSV, SARS-CoV-2, Nipah, influenza, and Ebola viruses), share conserved structural features, including the fusion peptide or loop and heptad repeat regions. These elements are essential for the formation of the post-fusion six-helix bundle during membrane fusion. Peptide inhibitors that mimic heptad repeat motifs have consequently emerged as an effective strategy for blocking the fusion process. This review summarizes design strategies for such inhibitors and highlights how sequence and structural insights have enabled their optimization via α-helical stabilization, hydrocarbon stapling, lactam bridges, lipid conjugation, macrocyclization, and multivalency. Using representative examples across major viral systems, this review illustrates how these strategies have led to the development of potent, stable, and even broad-spectrum antiviral peptides. This review provides insights to guide the rational design of next-generation peptide-based fusion inhibitors targeting viral membrane fusion. Full article

Mongolian patterns represent an important component of Mongolian cultural heritage, characterized by their dual structure of geometric symmetry and dynamic ornamental motifs. However, existing artificial intelligence-based generative methods struggle to preserve both low-frequency structural regularity and high-frequency decorative detail under limited data conditions. This study proposes a parameter-efficient digitization framework based on latent diffusion models (LDMs) fine-tuned with low-rank adaptation (LoRA) to achieve high-fidelity reconstruction of classic Mongolian patterns. A curated few-shot dataset and a low-rank constraint enable effective learning from only eight representative samples, while a dual-prompt mechanism and MSE-driven optimization improve geometric stability and semantic consistency. Integrated within a transparent ComfyUI workflow, the method supports controllable generation and reproducible experimentation. Experimental evaluations demonstrate that the proposed LoRA-LDM model achieves superior structural accuracy, reduced visual distortion, and enhanced motif preservation compared with baseline models. The results confirm the method’s applicability for digital preservation, reconstruction, and derivative design of structured cultural heritage motifs. Full article

This study explores the enhancement of Beech wood (Fagus sylvatica L.) surfaces through the laser engraving of motifs inspired by Romanian textile heritage, combining cultural preservation with modern surface design techniques. A digitization and computer-aided design (CAD)-based workflow was employed to accurately transfer traditional motifs onto wood substrates. Engraving was performed using a nitrogen laser at ten different power settings ranging from 10 W to 150 W, followed by color analysis of the engraved areas. The resulting surfaces were evaluated using the International Commission on Illumination (CIELab) system to identify optimal engraving conditions. Based on colorimetric analysis, three laser power settings were selected for final motif reproduction: 30 W, 45 W, and 105 W. The process enabled the accurate rendering of a traditional three-color motif, achieving both visual fidelity and aesthetic appeal. Results demonstrate that color-based laser engraving allows precise, durable, and culturally significant ornamentation of wooden surfaces. The conclusions highlight the potential of this technique to add artistic and commercial value to wood products while preserving and promoting cultural identity. Full article

Aptamers are promising synthetic molecular receptors that bind to specific targets by adopting a unique tertiary structure. However, their selection using standard SELEX protocols often does not allow the achievement of high affinity to the targets. Due to the lack and difficulty of obtaining data on the 3D structure of aptamers and their complexes, the design of known aptamers based on simple rules and software is in demand. The presented work considers the comparative characterization and design of DNA aptamers specific to the antibiotic kanamycin based on complementary interactions and structural motifs (bulges, mismatches, loops) predicted by NUPACK, RNAfold, and UNAFold software. The design included the elimination of non-functional parts of the aptamers and the stabilization of the kanamycin-binding loop. Seven novel aptamers, chosen based on these predictions, were synthesized, and their affinities were measured using an isothermal titration calorimetry technique. The prediction of end stem and hairpin loop structures was confirmed by comparison with circular dichroism data. As a result of sequential design with truncation of unnecessary nucleotides, a novel optimal 42-base-long aptamer was designed and demonstrated a dissociation constant of 109 ± 15 nM, which is 4.7-fold lower than the initial preparation (470 ± 40 nM) and overcomes all known aptamers to kanamycin. Full article