Review



model predicted mic values  (ATCC)


Bioz Verified Symbol ATCC is a verified supplier
Bioz Manufacturer Symbol ATCC manufactures this product  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 99
      Buy from Supplier

    Structured Review

    ATCC model predicted mic values
    Host proteins are proteolytically processed by the proteasome under both normal and infection conditions, generating “encrypted peptides” that may exhibit antimicrobial activity (the “cross-talk hypothesis”). The APEX deep-learning model screens these peptides for their predicted minimal inhibitory concentration <t>(MIC)</t> against clinically <t>relevant</t> <t>pathogens.</t> Peptides meeting the MIC cutoff (≤64□μmol□L□ ) are designated as “proteasomins.” Comparative analyses—encompassing known antimicrobial peptides (AMPs), physicochemical profiling, and dimensionality reduction (UMAP)—further refine and characterize proteasomins, highlighting their distinct sequence space and potential as novel therapeutic agents.
    Model Predicted Mic Values, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 19310 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/model predicted mic values/product/ATCC
    Average 99 stars, based on 19310 article reviews
    model predicted mic values - by Bioz Stars, 2026-04
    99/100 stars

    Images

    1) Product Images from "Proteasome-derived antimicrobial peptides discovered via deep learning"

    Article Title: Proteasome-derived antimicrobial peptides discovered via deep learning

    Journal: bioRxiv

    doi: 10.1101/2025.03.17.643752

    Host proteins are proteolytically processed by the proteasome under both normal and infection conditions, generating “encrypted peptides” that may exhibit antimicrobial activity (the “cross-talk hypothesis”). The APEX deep-learning model screens these peptides for their predicted minimal inhibitory concentration (MIC) against clinically relevant pathogens. Peptides meeting the MIC cutoff (≤64□μmol□L□ ) are designated as “proteasomins.” Comparative analyses—encompassing known antimicrobial peptides (AMPs), physicochemical profiling, and dimensionality reduction (UMAP)—further refine and characterize proteasomins, highlighting their distinct sequence space and potential as novel therapeutic agents.
    Figure Legend Snippet: Host proteins are proteolytically processed by the proteasome under both normal and infection conditions, generating “encrypted peptides” that may exhibit antimicrobial activity (the “cross-talk hypothesis”). The APEX deep-learning model screens these peptides for their predicted minimal inhibitory concentration (MIC) against clinically relevant pathogens. Peptides meeting the MIC cutoff (≤64□μmol□L□ ) are designated as “proteasomins.” Comparative analyses—encompassing known antimicrobial peptides (AMPs), physicochemical profiling, and dimensionality reduction (UMAP)—further refine and characterize proteasomins, highlighting their distinct sequence space and potential as novel therapeutic agents.

    Techniques Used: Infection, Activity Assay, Concentration Assay, Sequencing



    Similar Products

    model predicted mic values  (ATCC)
    99
    ATCC model predicted mic values
    Host proteins are proteolytically processed by the proteasome under both normal and infection conditions, generating “encrypted peptides” that may exhibit antimicrobial activity (the “cross-talk hypothesis”). The APEX deep-learning model screens these peptides for their predicted minimal inhibitory concentration <t>(MIC)</t> against clinically <t>relevant</t> <t>pathogens.</t> Peptides meeting the MIC cutoff (≤64□μmol□L□ ) are designated as “proteasomins.” Comparative analyses—encompassing known antimicrobial peptides (AMPs), physicochemical profiling, and dimensionality reduction (UMAP)—further refine and characterize proteasomins, highlighting their distinct sequence space and potential as novel therapeutic agents.
    Model Predicted Mic Values, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/model predicted mic values/product/ATCC
    Average 99 stars, based on 1 article reviews
    model predicted mic values - by Bioz Stars, 2026-04
    99/100 stars
    		  Buy from Supplier

    Image Search Results


    Host proteins are proteolytically processed by the proteasome under both normal and infection conditions, generating “encrypted peptides” that may exhibit antimicrobial activity (the “cross-talk hypothesis”). The APEX deep-learning model screens these peptides for their predicted minimal inhibitory concentration (MIC) against clinically relevant pathogens. Peptides meeting the MIC cutoff (≤64□μmol□L□ ) are designated as “proteasomins.” Comparative analyses—encompassing known antimicrobial peptides (AMPs), physicochemical profiling, and dimensionality reduction (UMAP)—further refine and characterize proteasomins, highlighting their distinct sequence space and potential as novel therapeutic agents.

    Journal: bioRxiv

    Article Title: Proteasome-derived antimicrobial peptides discovered via deep learning

    doi: 10.1101/2025.03.17.643752

    Figure Lengend Snippet: Host proteins are proteolytically processed by the proteasome under both normal and infection conditions, generating “encrypted peptides” that may exhibit antimicrobial activity (the “cross-talk hypothesis”). The APEX deep-learning model screens these peptides for their predicted minimal inhibitory concentration (MIC) against clinically relevant pathogens. Peptides meeting the MIC cutoff (≤64□μmol□L□ ) are designated as “proteasomins.” Comparative analyses—encompassing known antimicrobial peptides (AMPs), physicochemical profiling, and dimensionality reduction (UMAP)—further refine and characterize proteasomins, highlighting their distinct sequence space and potential as novel therapeutic agents.

    Article Snippet: The model predicted MIC values against 11 clinically relevant pathogens, including E. coli ATCC 11775, P. aeruginosa PAO1, P. aeruginosa PA14, S. aureus ATCC 12600, E. coli AIC221, E. coli AIC222, K. pneumoniae ATCC 13883, A. baumannii ATCC 19606, methicillin-resistant S. aureus ATCC BAA-1556, vancomycin-resistant E. faecalis ATCC 700802 and vancomycin-resistant E. faecium ATCC 700221, after which peptides were ranked by median MIC <64 μmol L -1 .

    Techniques: Infection, Activity Assay, Concentration Assay, Sequencing