Recombinant Human CASP14
- Known as:
- Recombinant Human CASP14
- Catalog number:
- CG19
- Product Quantity:
- 10ug
- Category:
- -
- Supplier:
- Novoprotein
- Gene target:
- Recombinant Human CASP14
Related genes to: Recombinant Human CASP14
- Gene:
- CASP14 NIH gene
- Name:
- caspase 14
- Previous symbol:
- -
- Synonyms:
- MICE, MGC119078, MGC119079
- Chromosome:
- 19p13.12
- Locus Type:
- gene with protein product
- Date approved:
- 1998-11-09
- Date modifiied:
- 2016-10-05
Related products to: Recombinant Human CASP14
Related articles to: Recombinant Human CASP14
- Real-valued inter-residue distance maps provide essential spatial information for understanding protein folding mechanisms and guiding downstream applications such as function annotation, drug discovery, and structural modeling. However, existing prediction methods often struggle to capture long-range dependencies and to maintain topological consistency across different structural scales. To address these challenges, we propose a novel prediction framework that integrates a Mamba architecture, based on a selective state space model, to effectively model global interactions, and incorporates the Kolmogorov-Arnold Network (KAN) to enhance nonlinear structural representation. Extensive experiments on standard benchmark datasets, including CASP13, CASP14, and CASP15, demonstrate prediction accuracies of 86.53%, 85.44%, and 82.77%, respectively, outperforming state-of-the-art approaches. These results indicate that the proposed framework substantially improves the fidelity of real-valued distance prediction and offers a promising tool for downstream structural and functional studies. - Source: PubMed
Publication date: 2026/01/27
Dong BenzhiHua YumengHou ChangXu DaliWang Guohua - Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. The chemotherapeutic drugs used in treatment often have serious side effects. In light of their anticancer potential, thiazolidinedione (TZD) derivatives are considered to be promising candidates for the development of novel antitumor agents. The objective of this study is to synthesize and produce two sets of TZD derivatives by combining the structural features of microtubule-targeting drugs used in breast cancer treatment, and to determine their molecular docking, molecular dynamics simulations, ADMET profile, antiproliferative, and apoptotic effect potential. In the present study, PZ-11 was determined by xCELLigence analysis to have the highest antiproliferative potential among all compounds tested on MCF-7 breast cancer cells. The cytotoxic activity of the synthesized compounds was evaluated against MCF-7 breast cancer cells, revealing IC values of 29.44 μM for PZ-9 and 17.35 μM for PZ-11, compared to 6.45 μM for the reference drug vincristine. Analysis of the gene expression of the PZ-11 compound, which has a stronger cytotoxic effect potential, showed that PZ-11 significantly downregulates , and , while upregulating pro-apoptotic genes such as , and . PZ-11's binding affinities were screened using a molecular docking workflow via KNIME. The robust and persistent interactions between PZ-11 and AIF were substantiated by molecular dynamics simulation. It is demonstrated by ADMET predictions that PZ compounds possess suitable pharmacokinetic properties. PZ-11 is a promising TZD-based anticancer drug candidate against breast cancer cells, as determined by computational and experimental analysis. However, further validation is required through analysis to support these findings. - Source: PubMed
Publication date: 2025/12/23
Zarrin PouriaGado SarahFarhang Boroujeni AliGadaşlı IbrahimBozkurt Fatma ZeynepCansaran-Duman DemetMutlu PelinAtes-Alagoz Zeynep - Myasthenia gravis (MG) is a chronic autoimmune neuromuscular junction disorder mediated by autoantibodies. Existing diagnostic methods mainly rely on serum antibody detection and electrophysiological testing, which are limited by invasiveness and suboptimal sensitivity and specificity. This study aimed to identify potential urinary biomarkers for noninvasive MG diagnosis using proteomics. - Source: PubMed
Publication date: 2026/01/09
Yang JingYang XiZhu Zhen-KunShen LiangSan Shi-GeXiao LianchenYe FanWang Chun-HuaMeng Kun - The widespread adoption of high-throughput sequencing technologies and multi-omics approaches has led to rapid accumulation of genomic, transcriptomic, proteomic, and even single-cell multimodal datasets, resulting in an exponential growth of biological data. The massive scale and inherent complexity of these datasets pose significant challenges for data management, analysis, and interpretation in the field of bioinformatics. Concurrently, artificial intelligence (AI) techniques, particularly deep learning and reinforcement learning, have achieved groundbreaking advances in medical diagnostics, drug discovery, and genomic analyses, providing novel theoretical tools and analytical paradigms for bioinformatics research. AI techniques are now extensively applied to DNA, RNA, and protein sequence prediction and design, 3D structural elucidation, functional annotation, integrative analysis of multi-omics data, and personalized drug design for precision medicine, significantly advancing biological research. This review systematically summarizes recent research progress and representative applications of AI techniques in bioinformatics, specifically discussing suitable scenarios and advantages of traditional machine learning algorithms, deep learning models, and reinforcement learning methods. We highlight AI's transformative impact with quantitative metrics from landmark achievements: accurate near-atomic protein structure prediction (median 0.96 Å on CASP14), robust single-cell modeling (AvgBIO $\approx $ 0.82), high protein design success rates (up to 92%), and sensitive cancer detection (Area Under Curve (AUC) $\approx $ 0.93). Furthermore, the paper provides an in-depth analysis of the latest advancements of AI in specific tasks, including biomedical text mining, multimodal omics integration, and single-cell analyses, while highlighting current challenges such as data noise and sparsity, difficulties in modeling long biological sequences, complexities in multimodal data integration, insufficient model interpretability, and ethical and privacy concerns. Finally, the paper outlines promising future research directions, emphasizing large-scale data mining, cross-domain model generalization, innovations in drug design and personalized medicine, and advocates for establishing an open and collaborative research ecosystem. - Source: PubMed
Jiang JiyueLi YunkeCao ShiweiShan YuhengLiu YuexingFei TianyiYu YuleFeng YiLi YuLi YixueYuan Jiao - Accurate de novo protein structure prediction remains a fundamental challenge, particularly in cases where homologous templates are unavailable or evolutionary information is weak. While end-to-end methods such as AlphaFold2 have achieved unprecedented accuracy, their closed box nature provides limited insight into the folding process and offers little flexibility for incorporating external evaluation. Here, we investigate whether model quality assessment (MQA) can be integrated into the structure prediction pipeline as a closed-loop feedback mechanism to iteratively improve prediction accuracy. In this study, we propose DGMFold, a de novo protein structure prediction method that establishes a feedback loop among three components: the geometric constraint prediction network (GeomNet), the structural simulation module, and the model quality evaluation network (EmaNet). In GeomNet, co-evolutionary features extracted from multiple sequence alignments (MSAs) are fed into an improved residual neural network to predict inter-residue geometric constraints, which are then used to guide structure folding. EmaNet then extracts 1D and 2D features from the folded structure model and employs a deep residual neural network to estimate the inter-residue distance deviation and per-residue lDDT. These evaluations are subsequently fed back into GeomNet as dynamic features, enabling iterative refinement of the predicted geometries and overall model accuracy. DGMFold was tested on 437 benchmark proteins and 20 FM targets of CASP14. Experimental results demonstrate that the closed-loop feedback mechanism significantly contributes to the performance of DGMFold, and the prediction accuracy of DGMFold outperforms that of the state-of-the-art de novo methods trRosetta and RaptorX at the time. When evaluated on the 124 human proteins for which AlphaFold2 yields TM-scores below 0.9, DGMFold achieves higher prediction accuracy than AlphaFold2 and RoseTTAFold on 71 and 72 targets, respectively, and outperforms both on 58 proteins. - Source: PubMed
Liu JunHe Guang-XingZhao Kai-LongZhou Xiao-GenZhang Gui-Jun