Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
September 6th, 2019
This protocol demonstrates how to use the Auto-CHO software for hierarchical and programmable one-pot synthesis of oligosaccharides. It also describes the general procedure for RRV determination experiments and one-pot glycosylation of SSEA-4.
We demonstrate how to use our newly-developed software, Auto-CHO, for hierarchical and programmable one-pot oligosaccharide synthesis. Auto-CHO includes algorithms on expanded building blocks, with relative reactivity values predicted through machine learning. The Auto-CHO software provides a valuable guideline for building block selection, and a hierarchical blueprint for the multiple one-pot synthesis of more complex glycans through fragment composition.
The implications of this technique extend toward therapy of cancers or infectious diseases by carbohydrate-based therapeuticals. Before using Auto-CHO, please make sure the Java Runtime Environment has been installed in a PC or Mac. The visual demonstration of Auto-CHO software manipulation and the related reactivity value determination experiment will help chemists follow the protocol, and proceed with the experiment quickly.
To perform software initialization of the the Java Runtime Environment, go to the Auto-CHO website and download the software according to the operating system. Currently, Auto-CHO supports Windows, macOS, and Ubuntu. The latest PDF user guide is provided on the Auto-CHO website.
For Windows users, unzip the Auto-CHO Windows. zip, and double click on Auto-CHO. jar in the Auto-CHO Windows folder to start the program.
Input the desired glycan structure. Choose to draw a glycan structure or read an existing structure file. To input by drawing, click on edit glycan by GlycanBuilder, or the area of click here to edit the synthetic target to draw and edit the query structure by GlycanBuilder.
Linkage and chirality information should not be ignored. Click on the Globo-H, SSEA-4, or OligoLacNAc buttons to display the examples. Close the GlycanBuilder dialogue to complete editing.
Define the search parameters in the parameter settings tab to get reasonable search results. Click on the okay button to enable new settings. The default setting is to search the experimental library only.
If it is desired to search both the experimental and virtual libraries, select the virtual building block library tab. Select use experimental and virtual libraries, and apply filtering to display virtual building blocks with certain criteria. Experimental and virtual building blocks can work together to enhance the searching ability of Auto-CHO.
Currently, Auto-CHO provides more than 50, 000 virtual building blocks, with predicted RRVs in the library. Check one or multiple desired virtual building blocks that the user would like to use for searching. Click on the show selected virtual building blocks button to show only the selected virtual building blocks.
Click on the show filtered virtual building blocks button to show only virtual building blocks with certain criteria defined by the user. Click on the show all virtual building blocks button to show all available virtual building blocks, and reset the filter. Select the query structure tab and click on the search building block library button to find the one-pot synthetic solutions for the query structure.
Then confirm the parameter settings. Search the result viewer. The search result is shown in the result visualization tab.
The reducing end acceptors of different residue numbers are displayed in the reducing end acceptor column. Next, select a reducing end acceptor. Solutions are displayed on the synthetic solution list.
Fragments are shown in the fragment list to suggest how many fragments should be used in the synthesis. The system provides detailed information of each fragment, including the RRV of the fragment, computational yield, as well as which protecting group should be deprotected for use of the fragment in the one-pot reaction. The building blocks used to assemble the selected fragment and the fragment connection information are also displayed.
For experimental building blocks, view and check chemical structures of the selected building blocks in the chemical structure of building block region, and see the detailed information building block browser. In a 10-milliliter round bottom flask, combine the two thioglycoside donors, absolute methanol, and drierite in DCM. Then stir at room temperature for one hour.
Take a 30-microliter aliquot of this mixture and inject the mixture into high-performance liquid chromatography in three separate injections. Measure the coefficient between the absorption and concentration of the donor molecule under the baseline separation conditions. Add a solution of 0.5 molar N-Iodosuccinimide in acetonitrile into the reaction mixture, followed by addition of a 0.1 molar trifluoromethanesulfonic acid solution.
Stir the mixture at room temperature for two hours. Next, dilute the reaction mixture with four milliliters of DCM. Filter and wash the reaction with saturated aqueous sodium thiosulfate, containing 10%sodium hydrogen carbonate.
Now extract the aqueous layer three times with five milliliters of DCM. Combine all organic layers, and wash with five milliliters of brine. Then dry the combined layers with approximately 200 milligrams of anhydrous magnesium sulfate.
Shake the mixture mildly for 30 seconds, and filter it through a funnel with a fluted filter paper in order to remove the magnesium sulfate. Then collect the filtrate in a 25-milliliter round bottom flask. Remove the solvent, using a rotary evaporator.
Dissolve the residue in one milliliter of DCM. Take a 30-microliter aliquot of this mixture, and inject it into high-performance liquid chromatography in three separate injections. Measure the concentrations of the remaining donors by HPLC, under the same separation conditions.
Measure the relative reactivity. Based on the relative reactivity value of DR4, the relative reactivity value of DX1 is three. The Auto-CHO search result based on default parameter settings indicates SSEA-4 can be synthesized by a two plus one plus three one-pot reaction.
When a trisaccharide-reducing end acceptor is selected, the program shows four potential solutions for the query. The first solution has one fragment, and its calculated yield is about 94%The fragment can be synthesized by two building blocks. The RRV of the first disaccharide building block is 1462, and the RRV of the second monosaccharide is 32.0.
The chemical structure of the first suggested building block used in the one-pot reaction is also shown. The one-pot experiment shows that SSEA-4 can be successfully synthesized in 43%yield by this suggestion. SSEA-4 can be synthesized by three units suggested by Auto-CHO.
These units include silele disaccharide building block one, monosaccharide building block two, and reducing end acceptor three. For parameter settings, we suggest to set parameters with stricter criteria at the beginning. For the selection of the building block library, we suggest to search the experimental library only at first.
Through this demonstration, we hope more important glycans, such as tumor-associated carbohydrate antigens, can be synthesized by the one-pot approach for further studies. Following this procedure, synthesis of all such antigens can be performed in order to design carbohydrate-based cancer vaccines. We also hope that artificial intelligence and computer algorithms can facilitate automated glycan synthesis to benefit disease treatment and prevention.
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Overview
This protocol demonstrates the use of Auto-CHO software for hierarchical and programmable one-pot synthesis of oligosaccharides. It includes a visual demonstration of software manipulation and RRV determination experiments.
Key Study Components
Area of Science
- Oligosaccharide synthesis
- Software development
- Machine learning applications
Background
- Auto-CHO software aids in oligosaccharide synthesis.
- Algorithms predict relative reactivity values using machine learning.
- Guidelines for building block selection are provided.
- Implications for therapeutic applications in cancer and infectious diseases.
Purpose of Study
- To demonstrate the functionality of Auto-CHO software.
- To provide a protocol for one-pot glycosylation.
- To facilitate complex glycan synthesis through fragment composition.
Methods Used
- Hierarchical synthesis of oligosaccharides.
- Machine learning algorithms for predicting reactivity values.
- Visual demonstrations of software usage.
- RRV determination experiments.
Main Results
- Successful demonstration of one-pot oligosaccharide synthesis.
- Effective guidelines for building block selection.
- Clear visual instructions for chemists.
- Potential therapeutic applications highlighted.
Conclusions
- Auto-CHO software is a valuable tool for oligosaccharide synthesis.
- Machine learning enhances the synthesis process.
- The protocol supports rapid experimental execution.
Frequently Asked Questions
Executive Industry Relevance
Programmable one-pot oligosaccharide synthesis enables rapid assembly of complex glycans for therapeutic target validation, reducing development timelines for carbohydrate-based drug candidates. Auto-CHO software provides predictive building block selection and hierarchical synthesis planning, supporting mechanistic de-risking in early discovery. This approach enhances portfolio triage by improving confidence in glycan structure-activity relationships prior to preclinical investment.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses through rapid synthesis of defined glycan structures like SSEA-4 for stem cell marker validation.
- Operational Value: Supports biological de-risking by generating homogeneous oligosaccharide batches for functional target validation assays.
- Predictive Value: Facilitates structure-activity relationship mapping to prioritize glycan epitopes with therapeutic potential in cancer and infectious disease targets.
Screening & Assay Development
- Assay Readiness: Produces standardized glycan preparations suitable for carbohydrate microarray and binding assay development.
- Quantitative Outputs: Delivers measurable yields and purity profiles enabling reliable compound screening against glycan-binding proteins.
- Platform Reuse: Allows iterative refinement of building block libraries via user feedback on virtual blocks, enhancing long-term screening scalability.
Translational & Preclinical Research
- Disease Relevance: Demonstrated synthesis of SSEA-4, a pluripotent stem cell marker, supports biomarker alignment in regenerative medicine and cancer stem cell targeting.
- Translational Continuity: Provides a pathway from discovery-scale synthesis to preclinical evaluation of tumor-associated carbohydrate antigens for vaccine development.
- Risk-Adjusted Advancement: Enables structure-defined glycan production to reduce ambiguity in immunological efficacy and safety profiling.
Pipeline & Workflow Integration
The method integrates into early discovery workflows by enabling hypothesis-driven glycan synthesis, feeding into lead identification through defined antigenic structures, and supporting preclinical validation via reproducible glycan production.
- Discovery Biology: Supports mechanistic de-risking by clarifying biosynthetic pathways and validating glycan targets through defined chemical synthesis.
- Screening: Generates assay-ready glycan panels with quantified yields for high-throughput screening of glycan-protein interactions.
- Analytics: Provides relative reactivity values and computational yields as quantitative readouts to compare synthetic route efficiency and predict success.
- Translational Research: Connects to preclinical work by enabling synthesis of tumor-associated carbohydrate antigens for vaccine candidate evaluation.
- Enterprise Reuse: Functions as a reusable capability through Auto-CHO’s feedback-driven virtual library expansion, reducing redundant synthesis planning across projects.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in target validation by reducing mechanistic ambiguity in glycan function.
- Operational Value: Enhances reproducibility and scalability of oligosaccharide production through standardized one-pot protocols.
- Strategic Value: Improves go/no-go decisions by enabling rapid structure-activity exploration, reducing late-stage biological failure risk.
- Portfolio Impact: Supports risk-adjusted prioritization of glycan-based candidates through defined synthetic accessibility and yield predictions.
Implementation Considerations
- Requires expertise in carbohydrate chemistry and glycan structure interpretation for effective building block selection.
- Needs access to HPLC and standard organic synthesis infrastructure for RRV determination and reaction monitoring.
- Demands cross-team standardization of glycan nomenclature and structural input formats for consistent Auto-CHO querying.
- Involves adaptation considerations when extending to non-thioglycoside building blocks or alternative protecting group strategies.
- Limited by the accuracy of machine learning-predicted RRVs for novel building blocks, requiring experimental validation for high-confidence predictions.
