WSPC Biotin-PEG3-DBCO

Name: WSPC Biotin-PEG3-DBCO
Brand: BOC Sciences
Rating: 5 (1 reviews)

Cat No.: BP-501889 4.5

WSPC Biotin-PEG3-DBCO is a heterobifunctional, PEG-based linker that combines a biotin handle with a strained cyclooctyne (DBCO) for copper-free strain-promoted azide–alkyne cycloaddition. Structurally, it features a short, three–ethylene glycol unit PEG spacer that provides aqueous solubility and reduces steric interference between the biotin moiety and the DBCO reactive group. In PROTAC and targeted degradation workflows, this linker can be used to conjugate a biotinylated component (e.g., for affinity capture, pull-down, or immobilization on streptavidin matrices) to an azide-functionalized partner, enabling modular assembly of degradation constructs or analytical probes. The DBCO–azide reaction proceeds rapidly under mild conditions without copper, which is advantageous for preserving sensitive ligands and maintaining biological activity. Overall, it supports robust construct characterization and streamlined experimental design in targeted protein degradation research.

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Category

PROTAC Linker

Molecular Formula

C₅₃H₆₈N₈O₁₇S₂

Molecular Weight

1153.28

* For research and manufacturing use only. Not for human or clinical use.

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Comprehensive PROTAC Platform
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Please store the product under the recommended conditions in the Certificate of Analysis.

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Room temperature in continental US; may vary elsewhere.

IUPACName

1-[2-[2-[2-[2-[5-[(3aS,4S,6aR)-2-oxo-1,3,3a,4,6,6a-hexahydrothieno[3,4-d]imidazol-4-yl]pentanoylamino]ethoxy]ethoxy]ethoxy]ethylamino]-3-[4-[4-[1-[[3-(2-azatricyclo[10.4.0.04,9]hexadeca-1(16),4,6,8,12,14-hexaen-10-yn-2-yl)-3-oxopropyl]carbamoyloxy]ethyl]-2-methoxy-5-nitrophenoxy]butanoylamino]-1-oxopropane-2-sulfonic acid

InChI Key

VXNHEVDLDQWVJJ-NHJLCCERSA-N

InChI

InChI=1S/C53H68N8O17S2/c1-35(78-53(67)56-20-19-49(64)60-33-38-12-4-3-10-36(38)17-18-37-11-5-6-13-41(37)60)39-30-43(73-2)44(31-42(39)61(68)69)77-23-9-16-48(63)57-32-46(80(70,71)72)51(65)55-22-25-75-27-29-76-28-26-74-24-21-54-47(62)15-8-7-14-45-50-40(34-79-45)58-52(66)59-50/h3-6,10-13,30-31,35,40,45-46,50H,7-9,14-16,19-29,32-34H2,1-2H3,(H,54,62)(H,55,65)(H,56,67)(H,57,63)(H2,58,59,66)(H,70,71,72)/t35?,40-,45-,46?,50-/m0/s1

SMILES

CC(C1=CC(=C(C=C1[N+](=O)[O-])OCCCC(=O)NCC(C(=O)NCCOCCOCCOCCNC(=O)CCCCC2C3C(CS2)NC(=O)N3)S(=O)(=O)O)OC)OC(=O)NCCC(=O)N4CC5=CC=CC=C5C#CC6=CC=CC=C64

1. Predicting the pathogenicity of bacterial genomes using widely spread protein families

Shaked Naor-Hoffmann, Dina Svetlitsky, Neta Sal-Man, Yaron Orenstein, Michal Ziv-Ukelson BMC Bioinformatics. 2022 Jun 24;23(1):253.doi: 10.1186/s12859-022-04777-w.

Background:The human body is inhabited by a diverse community of commensal non-pathogenic bacteria, many of which are essential for our health. By contrast, pathogenic bacteria have the ability to invade their hosts and cause a disease. Characterizing the differences between pathogenic and commensal non-pathogenic bacteria is important for the detection of emerging pathogens and for the development of new treatments. Previous methods for classification of bacteria as pathogenic or non-pathogenic used either raw genomic reads or protein families as features. Using protein families instead of reads provided a better interpretability of the resulting model. However, the accuracy of protein-families-based classifiers can still be improved. Results:We developed a wide scope pathogenicity classifier (WSPC), a new protein-content-based machine-learning classification model. We trained WSPC on a newly curated dataset of 641 bacterial genomes, where each genome belongs to a different species. A comparative analysis we conducted shows that WSPC outperforms existing models on two benchmark test sets. We observed that the most discriminative protein-family features in WSPC are widely spread among bacterial species. These features correspond to proteins that are involved in the ability of bacteria to survive and replicate during an infection, rather than proteins that are directly involved in damaging or invading the host.

2. Study on the Ingredient Proportions and After-Treatment of Laser Sintering Walnut Shell Composites

Yueqiang Yu, Yanling Guo, Ting Jiang, Jian Li, Kaiyi Jiang, Hui Zhang Materials (Basel). 2017 Dec 2;10(12):1381.doi: 10.3390/ma10121381.

To alleviate resource shortage, reduce the cost of materials consumption and the pollution of agricultural and forestry waste, walnut shell composites (WSPC) consisting of walnut shell as additive and copolyester hot melt adhesive (Co-PES) as binder was developed as the feedstock of selective laser sintering (SLS). WSPC parts with different ingredient proportions were fabricated by SLS and processed through after-treatment technology. The density, mechanical properties and surface quality of WSPC parts before and after post processing were analyzed via formula method, mechanical test and scanning electron microscopy (SEM), respectively. Results show that, when the volume fraction of the walnut shell powder in the WSPC reaches the maximum (40%), sintered WSPC parts have the smallest warping deformation and the highest dimension precision, although the surface quality, density, and mechanical properties are low. However, performing permeating resin as the after-treatment technology could considerably increase the tensile, bending and impact strength by 496%, 464%, and 516%, respectively.

3. Urban sprawl in Canada: Values in all 33 Census Metropolitan Areas and corresponding 469 Census Subdivisions between 1991 and 2011

Mehrdokht Pourali, Craig Townsend, Angela Kross, Alex Guindon, Jochen A G Jaeger Data Brief. 2022 Feb 10;41:107941.doi: 10.1016/j.dib.2022.107941.eCollection 2022 Apr.

The dataset presented here provides the degree of urban sprawl across 33 Census Metropolitan Areas (CMAs) in Canada of 2011 together with the 469 Census Subdivisions (CSDs) located within the 2011 boundaries of the CMAs, for the years 1991, 2001, and 2011. The dataset contains the values of weighted urban proliferation (WUP) and weighted sprawl per capita (WSPC) and their components. The landscape-oriented value of WUP indicates how strongly the landscape within the boundaries of a reporting unit is sprawled per square meter, while WSPC is inhabitant-oriented and reveals how much on average an inhabitant or workplace is contributing to urban sprawl in the reporting unit. The values of the components of the WUP and WSPC metrics are provided as well: percentage of built-up area (PBA), urban dispersion (DIS), land uptake per person (LUP), and urban permeation (UP). The values of full-time equivalents for the numbers of jobs, which were considered in the calculation of LUP values (pertaining to the number of inhabitants and jobs) are also included in order to facilitate future research.

WSPC Biotin-PEG3-DBCO is a bifunctional PEG-based linker designed for modular PROTAC and related targeted degradation workflows, combining a biotin handle for affinity capture with a DBCO moiety for efficient bioorthogonal conjugation. Its flexible ether-rich chain supports favorable linker conformations and reduced steric constraints at the conjugation sites. The DBCO group enables robust coupling to azide-bearing partners under mild conditions, facilitating assembly of degraders and conjugates. Detailed structural and reactivity guidance is provided below.

Structure: The linker contains a polyethylene glycol segment that provides conformational flexibility and hydrophilicity, flanked by a biotin-derived recognition element and a cyclooctyne (DBCO) reactive handle. Ether linkages dominate the backbone, with amide and carbonyl-containing features contributing to chemical stability and defined connectivity.

Reactivity: DBCO reacts selectively with azides via a strain-promoted azide–alkyne cycloaddition, typically proceeding without metal catalysts. Suitable conditions include aqueous or mixed aqueous buffers compatible with protein and ligand conjugation, with mild temperature control to preserve biomolecule integrity. Solvents such as water, buffered alcohols, or other bioconjugation-compatible media are commonly used, and reaction progress can be monitored by orthogonal analytical methods to confirm completion.

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* Our calculator is based on the following equation:
Concentration (start) x Volume (start) = Concentration (final) x Volume (final)
It is commonly abbreviated as: C₁V₁ = C₂V₂