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ncbi reference sequence nm 020476  (OriGene)


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    OriGene ncbi reference sequence nm 020476
    Ncbi Reference Sequence Nm 020476, supplied by OriGene, used in various techniques. Bioz Stars score: 90/100, based on 2 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/human+reference+sequences/ANK1+(NM_020476)+Human+Tagged+ORF+Clone/pmc12269838-394-12-5
    Average 90 stars, based on 2 article reviews
    ncbi reference sequence nm 020476 - by Bioz Stars, 2026-11
    90/100 stars

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    Related Articles

    Sequencing:

    Article Title: Molecular consequences of SCA5 mutations in the spectrin-repeat domains of β-III-spectrin
    Article Snippet: PM-GFP was a gift from Tobias Meyer, Stanford University, Stanford, CA (Addgene plasmid #21213). mCherry-Mem (plasmid #55779), mCherry-Calnexin-N-14 (plasmid #55005), FRB-mCherry-Giantin (plasmid #186575), mCherry-Rab5a-7 (plasmid #55126), mCherry-Rab7a-7 (plasmid #55127), LAMP1-mCherry (plasmid #45147), and mCherry-Golgi-7 (plasmid #55052) were purchased from Addgene. .. The pCMV6-ANK1-Myc was purchased from Origene, and the cDNA sequence corresponds to NCBI reference sequence NM_020476 . .. PCR amplifications were performed using iProof-HF polymerase (Bio-Rad).



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    Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, human/virus sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent protein biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein sequences, with outputs ranked to return the top five highest-confidence matches.

    Journal: Biosafety and Health

    Article Title: DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models

    doi: 10.1016/j.bsheal.2025.07.005

    Figure Lengend Snippet: Schematic overview of training and inference workflows. A) Training phase: Positive and negative sample pairs are processed through the embedding module to generate four modality-specific embedding representations, human/virus sequence embedding is encoded by ESM-2 to represent amino acid sequence features, human/virus chemical embedding is extracted from AAindex profiles to represent protein biochemical properties. The cross-fusion module computes loss via contrastive learning and executes forward propagation. Model parameters are iteratively updated using the Adam optimizer, with final weights preserved for inference. B) Binary task inference. Pre-trained weights are loaded to compute task-specific losses, enabling downstream classification prediction. C) Conditional generative inference. A sequence decoder module translates fused modality embeddings into human protein sequences, with outputs ranked to return the top five highest-confidence matches.

    Article Snippet: Since model-generated sequences may not correspond to natural proteins, we retrieved 20,078 human reference protein sequences from National Center of Biotechnology Information (NCBI) (accession: GCF_000001405.40) and 17,451 reviewed viral protein sequences from UniProt Swiss-Pro (taxon ID 10239) to ensure biological relevance.

    Techniques: Virus, Sequencing

    Cosine similarity analysis of generated human protein sequences and viral protein sequences. A) Cosine similarity between generated and ground-truth human proteins, illustrating the distribution of cosine similarity values between human protein sequences. B) Cosine similarity between generated and ground-truth viral proteins, depicting the distribution for viral protein sequences, which exhibits slightly lower and more variable similarity scores. In both cases, the distributions are sharply peaked around 0.8, indicating a generally strong semantic alignment across samples. Human and viral protein sequences from the test set were analyzed using DeepHVI to generate a density distribution of similarity scores between reconstructed sequences and human interactors. Abbreviations: Std, standard deviation; Min, minimum; Max, maximum.

    Journal: Biosafety and Health

    Article Title: DeepHVI: A multimodal deep learning framework for predicting human-virus protein-protein interactions using protein language models

    doi: 10.1016/j.bsheal.2025.07.005

    Figure Lengend Snippet: Cosine similarity analysis of generated human protein sequences and viral protein sequences. A) Cosine similarity between generated and ground-truth human proteins, illustrating the distribution of cosine similarity values between human protein sequences. B) Cosine similarity between generated and ground-truth viral proteins, depicting the distribution for viral protein sequences, which exhibits slightly lower and more variable similarity scores. In both cases, the distributions are sharply peaked around 0.8, indicating a generally strong semantic alignment across samples. Human and viral protein sequences from the test set were analyzed using DeepHVI to generate a density distribution of similarity scores between reconstructed sequences and human interactors. Abbreviations: Std, standard deviation; Min, minimum; Max, maximum.

    Article Snippet: Since model-generated sequences may not correspond to natural proteins, we retrieved 20,078 human reference protein sequences from National Center of Biotechnology Information (NCBI) (accession: GCF_000001405.40) and 17,451 reviewed viral protein sequences from UniProt Swiss-Pro (taxon ID 10239) to ensure biological relevance.

    Techniques: Generated, Standard Deviation