Programmable One-pot Synthesis of Oligosaccharides | Biochemistry

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Perspective

Programmable One-pot Synthesis of Oligosaccharides Cheng-Wei Cheng, Chung-Yi Wu, Wen-Lian Hsu, and Chi-Huey Wong Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.9b00613 • Publication Date (Web): 27 Aug 2019 Downloaded from pubs.acs.org on August 28, 2019

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Biochemistry

Programmable One-pot Synthesis of Oligosaccharides Cheng-Wei Cheng1, Chung-Yi Wu1, Wen-Lian Hsu2, and Chi-Huey Wong1,3* 1

Genomics Research Center, Academia Sinica, 11529 Taipei, Taiwan

2

Institute of Information Science, Academia Sinica, 11529 Taipei, Taiwan

3

Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA

*

Corresponding Author

KEYWORDS Programmable one-pot synthesis, carbohydrate and oligosaccharide, data science, algorithm and machine learning, RRV prediction, Auto-CHO

ABSTRACT

Carbohydrates are one of the four major classes of biomolecules, often conjugated with proteins as glycoproteins or with lipids as glycolipids and participate in many important biochemical functions in living species. However, glycoproteins or glycolipids often exist as mixtures, and as a consequence, it is difficult to isolate individual glycoproteins or glycolipids as pure form to understand the role carbohydrates play in the glycoconjugate. Currently the only feasible way to obtain pure glycoconjugates is through synthesis, and of the many methods developed for the synthesis of oligosaccharides, the ones with automatic and programmable potential are considered to be more effective to address the issues of carbohydrate diversity and related functions. In this Perspective, we describe how data science, including algorithm and machine learning, can be used

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to assist the chemical synthesis of oligosaccharide in a programmable and one-pot manner, and how the programmable method can be used to speed up the construction of diverse oligosaccharides to facilitate our understanding of glycosylation in biology.

TEXT Introduction Glycans are one of the most important bio-molecules in life, and are involved in many essential biochemical reactions and recognition events, such as cell differentiation, intercellular interaction, cancer proliferation, inflammation, and immune responses.1–3 Compared to nucleic acids and proteins which are linear bio-molecules, carbohydrate structures are more complicated and often branched leading to a greater diversity. It is estimated that the possible number of pentasaccharide structures generated from the 8 major monosaccharide building blocks commonly found in humans is more than 15 million, and neither chemical nor biological methods available to date are able to create such a diverse number of structures. In humans, proteins and lipids are often glycosylated to form glycoproteins or glycolipids. However, the process of biological glycosylation and its functional role have not been well understood, and it has been very difficult to isolate specific glycoconjugates as pure form with sufficient amounts to study the role of glycosylation, particularly in glycoproteins.4 Therefore, chemical, enzymatic or chemoenzymatic synthesis of glycans or glycoproteins has been used to obtain homogeneous glycans or glycoforms for structural and functional study, as illustrated in the synthesis of carbohydrate-based vaccines against cancer and infectious diseases,5–13 heparin-based anticoagulants,14–17 homogeneous Nglycans and glycoproteins including antibodies.18–29 Of the high-speed methods developed to date for oligosaccharide synthesis, the automated solid phase synthesis method developed by the Seeberger group in 2001, was performed successfully in


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Biochemistry

a modified peptide synthesizer with features adapted for carbohydrate chemistry.30–32 Recently, the automated method has been further improved and used to provide many synthetic products for biological studies.32,33 Another notable approach designed for large-scale synthesis is the enzymatic one-pot synthesis of oligosaccharides coupled with sugar-nucleotide regeneration originally developed by Wong and Whitesides in 198234 and since then the method has been further advanced and extended to the synthesis of many other oligosaccharides and glycoproteins.35–38 Inspired by this enzymatic one-pot reaction and the one-pot chemical synthesis of oligosaccharides39–42, the first programmable one-pot chemical synthesis of oligosaccharides was developed in 199943 , which was designed to enable speedy synthesis of large numbers of oligosaccharides, using the designed software “Optimer” to search Building BLocks (BBLs) with defined relative reactivity values (RRVs) to be used sequentially in the one-pot chemical reaction. This software was further upgraded in 2018 to a new version called Auto-CHO44 to expand the scope and capability of programmable synthesis. It contains a library of 154 BBLs with experimentally defined RRV together with a virtual library of approximately 50,000 BBLs with predicted RRVs created by machine learning; the program is also able to guide the selection of fragments for the one-pot synthesis of larger oligosaccharides through the hierarchical programmable one-pot approach implemented in the software. In this Perspective, we focus on the evolutionary process of programmable one-pot oligosaccharide synthesis and its interplay with data science to further refine and improve the programmable capability, and the application of this method to the synthesis of representative oligosaccharides with biological significance. Development of Software to Guide the Selection of Designed Building Blocks for Programmable One-pot Synthesis.


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As mentioned, there are about 8 monosaccharides commonly used in human as building blocks for glycan synthesis, including glucose (Glc), galactose (Gal), mannose (Man), fucose (Fuc), Nacetylglucosamine

(GlcNAc),

N-acetylgalactosamine

(GalNAc),

N-acetylmannosamine

(ManNAc), and sialic acid (Neu5Ac). The possible structural diversity that can be generated from these building blocks is enormous, estimated to exceed 15 million for pentasaccharides, and over 20,000 for the N-linked glycans found on human glycoproteins. It has been impossible to create such a diversity using currently available chemical or biological methods, and as such, development of new methods for use to address the issue of glycan diversity remains a long standing problem. This challenge has stimulated us to develop a programmable one-pot method using designed building blocks; and to achieve this goal, we have introduced appropriate protecting groups to each monosaccharide BBL to tune and measure their reactivity quantitatively. Previously, the concept of one-pot oligosaccharide synthesis has been reported by Fraser-Reid,39 Kahne,40 and Hung41 et al. using orthogonal leaving groups, and the work of Ley42 has shown that the reactivity of sugars can be tuned additively with protecting groups. Based on these advances, the programmable one-pot method based on Optimer was initially developed using 50 BBLs, each containing the STol leaving group (p-methylphenyl thioglycoside) with well-defined RRV for the synthesis of oligosaccharides. In the programmable one-pot reaction, BBLs are added sequentially to a flask according to the RRV of each BBL by descending order from the non-reducing end to the reducing end of the glycan to be synthesized. The RRV of a BBL was determined by comparing the rate of reaction with methanol with that of peracetyl tolylthiomannoside (RRV = 1.0) by highperformance liquid chromatography (HPLC).44 We selected the STol leaving group because the leaving group is a good chromophore for detection, the preparation of the thioglycoside is convenient, has no effect on the operation of carbohydrate protecting group, and is easy for


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Biochemistry

observing the reactivity of BBL by HPLC. Methanol is used as acceptor to obtain the RRV to avoid any steric interference in glycosylation. There are approximately 20 protecting groups commonly used in glycosylation reaction, so the combinatorial choice of these protecting groups for each monosaccharide, disaccharide, or trisaccharide BBL is enormous and could generate numerous BBLs with a wide range of RRV. Currently, the smallest RRV in the experiment BBL library is 0.69, and the largest one is 330,000. Since the selection of appropriate BBLs for the highyield synthesis of a desired oligosaccharide is a crucial issue to the success of one-pot synthesis and the availability to BBLs may be different in different laboratories, the Optimer program has implemented options of synthetic strategies with different BBL sets and over-all yield for each synthetic strategy. So depending on the availability of BBLs to the lab, scientists can select the most convenient way to carry out the synthesis. It is noted that the RRV of a BBL is measured with methanol as acceptor to exclude possible steric effect in glycosylation; however, steric effect is often encountered when protected sugars are used as acceptors in oligosaccharide synthesis. In addition, the programmable one-pot synthesis was designed to start with the most reactive BBL followed by the addition of other BBLs with sequential reduction in reactivity, and the difference of RRV between BBLs is better to be larger than 1,000 to ensure good reaction rate, and the glycosylation reaction is limited to 3-4 steps in order to reduce byproducts and to achieve the best result. With these limitations in place, sialic acid BBL can not be used as the first BBL because it is always the least reactive BBL, no matter how the protecting groups are introduced (due to the electron-withdrawing carboxyl group at the anomeric center) and the stereoselectivity of sialylation is often relatively low due to the presence of quaternary anomeric center. To overcome this limitation, we have linked sialic acid to the next sugar which is often the galactose residue as the 2,3-, 2,6-, 2,8- or 2,9-linked sialyl disaccharide BBLs.44,45 In this way, the reactivity of the


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sialyl disaccharide is determined by the galactose residue which can be more easily tuned by its protecting groups, and the sialic acid residue is no longer involved in the one-pot reaction as it is just one of the protecting groups for the galactose residue. This strategy has also been applied to the synthesis of sialyl disaccharide building blocks containing a fluorine group at the 3-position of the sialic acid residue to make the sialoside more stable and resistant to sialidase23. We have recently introduced this strategy to the synthesis of a homogeneous antibody containing a fluorosialylated biantennary N-glycan in the Fc region to prolong its interaction with Fc receptors, thereby enhancing the antibody dependent cellular cytoxicity (ADCC) and the vaccinal effect. Scheme 1 shows the programmable one-pot synthesis of 3-F sialylated hexasaccharide and the synthesis of complex-type biantennary N-glycan with terminal 3Fax-Neu5Ac in the α-2,6-linkage (α2,6-F-SCT)23.


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Biochemistry

BnO O HO BnO TrocHN BnO BnO BnO 2 RRV = 537

O O STol Ph

OAc OAc

AcO

CO2Me O

AcO

OBn

F OBn O

BzO 1 RRV = 2053

STol

BzO

OAc OAc

AcO

BnO O O BnO TrocHN

3 RRV = 0

(a)

O

AcHN

OAc O

O O HO

26%

R 1O R O O

CO2Me

2

O

AcHN

O

AcO

F OBn BnO O BnO

O

BzO BzO

OAc OAc

AcO

O O

O BnO

O

OBn

TrocHN

4 R1, R2 = PhCH 5 R1 = R2 = H

(b) O

NHTroc

CO2Me O

AcHN (c)

BnO BnO BnO

OBn

OAc O

AcO

O F OBn O

BzO BzO

6

BnO O O BnO TrocHN BnO BnO BnO

O O F

R 1O

OR1 OR1

CO2R3

AcHN R 1O

F OR4

7 R1 = Ac, R2 = Bz, R3 = Me, R4 = Bn, R5 = Troc

(d)-(g)

O

8 R1 = R2 = R3 = R4 = H, R5 = Ac

O O

R 2O 2

R O

R 4O O R 4O

O

O

R5HN 4

R O R 4O R 4O R 1O

OR1 OR1

CO2R3

HO

O

AcHN R 1O

F OR4

R 2O

4

O O

R 2O

O

R 4O O R 4O

R O R 4O R 4O

O O

OR1 O

OR4 O R 4O

O

OR4

R5HN

O O

O

5

R HN

(a) TfOH, NIS, 4Å MS, CH2Cl2, -40 to -10 °C, 3 h; (b) pTSA•H2O, CH3CN, 6 h, 75%; (c) 6, AgOTf, Cp2HfCl2, toluene, 4 Å MS, -15 °C, 3 h, 70 % (80% brsm, based on recovered starting materials); (d) LiOH, dioxane/H2O (4:1), 90 °C, 16 h; (e) Ac2O, Py, 16 h; (f) NaOMe, MeOH, 16 h; (g) Pd(OH)2, MeOH/H2O/HCOOH (6:3:1), H2, 16 h, 40% (4 steps).


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Scheme 1. The Programmable one-pot synthesis of oligosaccharides using 3-F sialylated disaccharide 1 as building block and its application to the synthesis of a homogeneous antibody containing a fluorinated biantennary glycan to prolong the ADCC and vaccinal effects. Machine Learning Machine learning, especially deep learning is a branch of artificial intelligence, and has a lot of successful applications in many areas, such as board game,46,47 self-driving car,48–50 speech recognition,51–53 computer vision,54–56 natural language processing,57,58 recommender systems,59– 61

and etc. It has also been applied to the biomedical research area, such as diagnosis and referral

in retinal disease,62 variant calling from next-generation DNA sequencing data,63 drug discovery and development,64,65 and computer-aided retrosynthesis.66 However, it is difficult or expensive to obtain labeled data in some research areas, in which case, it is not easy for deep neural network67 to perform well. Although other methods such as one-shot learning68 have been proposed to deal with small data set problem, traditional machine learning approaches such as support vector machine69 or random forest70 still play an important role for small data set. In our previous research,44 we have successfully applied support vector regression trained by a small labeled data set for predicting the RRV of virtual BBLs. Application of Machine Learning to the Creation of Virtual BBLs with Predicted RRV. In the original study of programmable one-pot oligosaccharide synthesis, there were only around 50 BBLs in the library.43 Though the library was later expanded to more than 150 BBLs71, with well-defined RRV, few of them were actually involoved in oligosaccharide synthesis. Since it is labor-intensive and time-consuming to measure the RRV experimentally, the library size is not easy to grow quickly. BBLs are like cooking materials. Without enough suitable materials, it is hard to cook a good dish. To tackle this problem, we have generated more than 50,000 virtual


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Biochemistry

BBLs from 5-type monosaccharide structures with possible combination of protecting groups and hydroxyl group as corresponding features. Furthermore, we have developed a machine learning model to predict RRVs of BBLs (Figure 1).44 In this model, three types of features, namely, basic properties, calculated NMR chemical shift values,72 and molecular descriptors,73 are used for model development, and more than 1,500 features are tested.

Figure 1. The virtual building block library construction and RRV prediction by machine learning. To achieve the best feature combination, we applied recursive feature elimination approach as a feature selection algorithm, and support vector regression was chosen as the machine learning model. We then used leave-one-out cross-validation (LOOCV) and independent test for performance evaluation. After parameter optimization, the optimized RRV predictor achieved the performance in LOOCV with 0.97 Pearson correlation coefficient (PCC). It also achieved an outstanding performance of RRV prediction during the independent test, which means BBLs in


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the independent test set do not appear in the training set. The PCC between observed and predicted RRVs in the independent test set is 0.86, indicating that the RRV prediction is quite successful. For example, the protecting group 3 of Dx7 building block44 is Fmoc, which is novel and does not appear in the training set. The predicted RRV is 971 and the observed RRV is 1,313, which is very close. For another example, the observed RRV of Dx5 building block44 is 13,127, which is very close to its predicted RRV 13,217. Table 1 shows some independent test examples with their observed and predicted RRVs used in the previous research44. It is noted that although the observed and predicted RRVs of the last case in Table 1 are quite different, they still belong to the same RRV class (medium RRV class) and the predicted RRV is valuable for one-pot synthesis. Using the optimized predicted RRVs of these virtual BBLs, we successfully expanded the BBL library by machine learning.


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Biochemistry

Table 1. Comparison of the predicted RRV of some representative virtual BBLs with the RRV measured experimentally. BBL Chemical Structure

Predicted RRV

Experimental RRV

9.43

3.00

Gal

1,416.13

1,730.80

Gal

438.57

148.20

GalNAc

469.81

479.00

GalNAc

11,427.68

3,652.00

GlcNAc

OAc O

BzO BzO

Sugar Type

STol

NPhth

Ph O

O O

BnO

STol

OBz OBz

BzO

O

HO

STol

OBn

N3

BnO

O

BnO

STol

NPhth

Ph O

O PMBO

O

STol

OBz


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Auto-CHO Software

Figure 2. Illustration of Auto-CHO software operation and the concept of hierarchical one-pot synthesis of oligosaccharide, which involves the use of fragments prepared in advance by one-pot synthesis and then used as BBLs for the subsequent one-pot synthesis in order to reduce the steps in the one-pot reactions and increase the size of the oligosaccharide. The order of RRVs from nonreducing end to reducing end BBL or fragments that participate in a one-pot reaction should be high, medium, and small, the RRV of the reducing end acceptor is zero, and the leaving group used in one-pot reactions is STol group. After getting enough BBLs (materials), the next question is how to use suitable BBLs to synthesize a desired glycan by the one-pot approach. Just like cooking, one needs a recipe to know how to make a dish. Auto-CHO is like a recipe program telling us about how to synthesize the desired glycan with appropriate steps and suitable BBLs. Figure 2 shows the Auto-CHO software


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Biochemistry

operation and the concept of hierarchical one-pot synthesis of oligosaccharide using fragments prepared by Auto-CHO and used as BBLs in the subsequent one-pot reaction. The input of AutoCHO is a desired glycan structure that can be edited by GlycanBuilder,74,75 and it outputs with onepot synthetic solutions. Auto-CHO searches suitable BBLs from experimental and virtual libraries, including 154 BBLs in the experimental library and more than 50,000 BBLs in the virtual library. Auto-CHO also allows users to give feedback for virtual BBLs through online questionnaires. Feedback from the research community can help keep useful virtual BBLs and eliminate useless ones. The output of Auto-CHO is a one-pot synthesis blueprint, which can be done by one or multiple one-pot reactions (hierarchical solutions). In Figure 2, the [2 + 1 + 1] strategy shows the synthetic solution without further fragmentation. On the other hand, the [1 + 2 + 1] example gives a hierarchical one-pot synthesis option. The precursor of the internal fragment can be synthesized by two BBLs, and the corresponding protecting group of the internal fragment can be deprotected to form a new BBL which can be used as a BBL in another one-pot reaction. Auto-CHO software can be downloaded from the website (https://sites.google.com/view/auto-cho/home), and the source code can be accessed from the GitHub (https://github.com/CW-Wayne/Auto-CHO). Examples of Oligosaccharides Prepared by Auto-CHO Guided One-pot Synthesis To illustrate the use of Auto-CHO in oligosaccharide synthesis, four representative oligosaccharides with important biological functions have been prepared successfully, including stage-specific embryonic antigen-4 (SSEA-4),44 a heparin pentasaccharide,76,77 an oligomer of Nacetyllactosamine (oligoLacNAc),78 and Globo-H.79,80 SSEA-4 is a human embryonic stem cell marker that is a potential therapeutic target in glioblastoma multiforme and many other cancers.81 Auto-CHO software suggests that SSEA-4 can


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be synthesized by a [2 + 1 + 3] one-pot strategy using the three BBLs selected by the software (9, 10, and 11) with RRVs of 1462, 32, and 0, respectively (Scheme 2) to give the final product 12 in 43% yield, compared to 30% based on the orthogonal method.82 NO2

O

O HO

NO2

O

O O

STol

NHTroc

Ph O O

10 RRV = 32 i)

AcO

OAc OAc AcN

COOMe O

O O

ii)

OBz O

STol

(h)

O O

O 9

HO BnO

O

O OBn O O BnO BnO OBn

OBn O

O(CH2)5N3 OBn

11 RRV = 0

Ph

RRV = 1462

NO2

NO2

Ph O O O O O O OAc AcO OAc COOMe OBz O O O O O O AcN O BnO O OBn TrocHN O O O O O O BnO BnO 12 Ph OBn

OBn O O(CH ) N 2 5 3 OBn

(h): i) TfOH, NIS, CH2Cl2, -40 oC, 3 h; ii) TfOH, NIS, CH2Cl2, -20 oC

Scheme 2. The programmable one-pot synthesis of SSEA-4 using a sialyl disaccharide as BBL. Various heparin pentasaccharides have been useful as anti-coagulants; however, side effects of breeding often occur which were thought to be caused by the undesired sulfate groups. In order to develop an effective method for the synthesis of heparin pentasaccharides with different regiodefined sulfation pattern, we have developed a programmable one-pot synthesis method which allows differential deprotection of the final pentasaccharide product and introduction of the


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Biochemistry

sulfate groups selectively for functional evaluation.83 To fulfill this purpose, the protected pentasaccharide can be synthesized with a [1 + 2 + 2] one-pot strategy suggested by Auto-CHO as shown in Scheme 3., using the three BBLs 13 (RRV = 132), disaccharide 14 (RRV = 18.2), and disaccharide 15a or 15b (RRV = 0) to give the final products (16a and 16b in 48% and 54% yields, with Lev and Ac groups on the reducing end disaccharide, respectively.77 MeO2C HO BnO

O OBz

OAc O

O BnO

STol

N3

14

RRV = 18.2 O

BnO MeO2C OTBDPS BnO BnO

O

(i)

OH

STol

BnO

O

OR O N3

OMe

OBz 15a: R = Ac 15b: R = Lev RRV = 0

N3 13 RRV = 132

BnO BnO

OTBDPS O N3 MeO2C OBnO

OAc O CO Me 2 OBn N3 O O O BnO BzO 16a: R = Ac, 54 % 16b: R = Lev, 48 % O

O BnO OBz

OR O N3 OMe

(i): NIS/TfOH, CH2Cl2, -45 oC to -25 oC

Scheme 3. The programmable one-pot synthesis of heparin pentasaccharide containing differential protecting groups for access to different regiodefined sulfate patterns. Recently, another analog of heparin pentasaccharide have been synthesized in a one-pot manner using newly designed building blocks and protecting groups (Scheme 4).14


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MeO2C HO MeO

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OAc O OMe

O BnO 18

O

STol

OBn

i)

OBn ii)

(j)

OTBDPS MeO MeO

MeO2C OH

O

O

O BnO

MeO O

OMe

BnO OMe 19

OPO(OBu)2 OMe 17

MeO MeO

70% OTBDPS O CO2Me OAc MeO O O O CO2Me O MeO OMe BnO MeO BnO O OBn O O 20 BnO O MeO BnO MeO

(j) i) TMSOTf (1.0 equiv.), CH2Cl2, 4Å MS,-45 oC; ii) NIS, -45 oC to -25 oC, 80 min.

Scheme 4. One-pot synthesis of protected Idraparinux. N-acetyl lactosamine is often found in N-linked glycans as repeated units and is associated with cancer and infectious diseases. To understand the role of LacNAc repeats in diseases progression requires access to the N-glycans or N-glycoproteins. Though the enzymes responsible for the synthesis of LacNAc repeats have been identified, the resulting non-protected LacNAc repeats may be difficult to be incorporated into a desired multiantennary N-glycan for biological evaluation. We have thus developed a programmable one-pot synthesis of protected LacNAc repeats which could be useful for the modular assembly of N-glycans and N-glycoproteins.20,24,84 Scheme 5 demonstrates the synthesis of an oligoLacNAc by a [2 + 2 + 2] one-pot strategy as suggested by Auto-CHO. Three BBLs 21, 22, and 23 with RRV 263, 51, and 0, respectively were used for the one-pot synthesis to give the product in 60% overall yield.44


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Biochemistry

OBn HO BnO

O NPhth 22 RRV = 51

OBz

BzO

O

O

STol

OBz i)

OBn HO BnO

OBn BnO AcO BnO

PhthN

OBn

O

O

O

O

ii) Ph

(k)

STol O OBz NPhth 21 RRV = 263

O O BnO

O

23 OPMP RRV = 0

60%

OBn BnO AcO BnO

O

OBn O

O NPhth

OBz

OBn O

O BnO

NPhth

BzO O

OBz

OBn

O OBz

O BnO

O PhthN O

24 Ph

O O BnO

O OPMP

(k): i) TfOH, NIS, CH2Cl2, -50 oC; ii) TfOH, NIS, CH2Cl2, -20 oC

Scheme 5. The programmable one-pot synthesis of an oligoLacNAc. Globo-H, a hexasaccharide, and SSEA4 are found on the cell surface of many epithelial tumors, including colon, endometrial, gastric, lung, ovarian, pancreatic, prostate, and breast cancers as globo-series glycolipids, and they are not found on normal tissues.6,85–87 Globo-H has been used as hepten for the development of a carbohydrate-based vaccine used for the treatment of metastatic breast cancer and prostate cancer88 and currently the Globo-H vaccine is in the phase III global trial for the metastatic triple-negative breast cancer. The hierarchical programmable one-pot synthesis of Globo-H has been reported in the previous publication79 as shown in Scheme 6. The internal trisaccharide fragment 28 used in the [1 + 3 + 2] one-pot reaction strategy was prepared in advance by one-pot synthesis using three monosaccharide BBLs (25, 26, and 27) with RRV 4000, 850, and 13, respectively. The Lev group of the synthetic fragment was deprotected to form fragment 28 as a new building block for the final one-pot reaction, in which 29, 28, and 30 were


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used sequentially in the reaction with RRV 72,000, 6, and 0, respectively. To further improve the yield and efficiency, another one-pot strategy was suggested by Auto-CHO using the [1 + 2 + 3] approach without additional one-pot reactions (Scheme 7), where the monosaccharide 32, disaccharide 33, and reducing end trisaccharide 34 with RRVs of 72,000, 644, and 0, respectively were used sequentially to give the product 35 in 80% overall yield.80 BzO

OBz O

HO

STol

26 NHTroc RRV = 850 i)

(BzN)O ii)

(l)

OBn

BnO

O

BnO

HO

O(NBz) O

STol 27 O(ClBn) RRV = 13

STol

25 OLev

67%

RRV = 4000 Deprotection (OLev to OH) OBn

BnO

BzO

O

BnO

OBz

(BzN)O

O

O

O

28 NHTroc RRV = 6.0

OH

O(NBz) O

STol O(ClBn)

iii)

HO OBn

OBn

O

O

O BnO OBn 30 RRV = 0

BnO STol OBn

O

(m)

iv)

BnO OBn 29 RRV = 72000

OPMP

OBn

62%

BnO

OBn O

BnO

BzO O

O O BnO OBn

OBn

O(NBz) (BzN)O O O O NHTroc BnO O OBn

OBn

O

O

OBz

31

BnO

O BnO OBn

OPMP

OBn

(l): i) NIS, TfOH, -20 oC; ii) NIS, TfOH, -20 oC, 67% overall yield. (m): iii) NIS, TfOH, -40 oC; iv) NIS, TfOH, -40 oC to RT, 62% overall yield.


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Biochemistry

Scheme 6. The [1 + 3 + 2] one-pot synthesis of Globo-H. OBz

OBn BzO

BnO

O

BnO

O

O

OH

STol

NHTroc

33

RRV = 644

Ph O O

i)

HO

STol OBn

O BnO

BnO O OBn O O BnO OBn BnO 34

ii)

(n)

O

OBn 32

OBn O O(CH ) NHCbz 2 5 OBn

RRV = 0 83%

RRV = 72000

Ph BnO

OBn BzO O

BnO

O

BnO OBn

O

OBn

35

O

O

NHTroc

O O

O O

OBz

BnO

O OBn

BnO

O OBn

OBn O BnO

O

O(CH2)5NHCbz

OBn

(n): i) NIS, TfOH, -40 oC; ii) NIS, TfOH, -30 oC 83% overall yield.

Scheme 7. The [1 + 2 + 3] one-pot synthesis of Globo-H. In addition to the above cases, other oligosaccharides have also been prepared by the programmable one-pot approach, including Lewis Y (Ley)89, fucosyl GM1,90,91 dimeric Lewis X,92 KH-1 epitope,92 tumor-associated antigen N3 minor octasaccharide,93 α-Gal oligomers,94,95 vancomycin,96 oligomannoses,97 lactotetraose (Lc4) and 2‴-O-fucosyl-Lc4 (IV2Fuc-Lc4),98 and oligosaccharide libraries.99 With more examples of BBLs developed for the one-pot synthesis and their RRVs deposited in the Auto-CHO program, the programmable one-pot synthesis method could be extended to the synthesis of many other oligosaccharides which could be used for different applications.


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Conclusion and Future Prospects As the progression of science advanced, artificial intelligence (AI) comes into play to help organize and manipulate data gathered by research, and to learn from this data processing to come up with an ability to predict the result from an experiment which then is further validated experimentally. This iterative process has been precisely adopted by the development of the AutoCHO program for the programmable one-pot synthesis of oligosaccharides. This process is an illustration of interdisciplinary collaboration, bringing together carbohydrate chemistry and computer science to solve a long-standing problem in oligosaccharide synthesis. The programmable one-pot method can be used alone or integrated with enzymatic method100,101 to develop important tools such as glycan arrays and carbohydrate-based vaccines. We believe the strategy and principle of programmable one-pot synthesis can be applied to other reactions for the assembly of complex structures, including, for example, enzymatic synthesis of complex molecules102 such as glycoproteins. We believe that development of programmable method as an efficient synthetic methodology to tackle the problem of carbohydrate diversity and complexity will have a major contribution to facilitate our understanding of the role carbohydrates play in biology, and this contribution is expected to have a significant impact on the advances of glycoscience. With the information of various homogeneous glycoproteins and their 3-D structures as well as functions available, we eventually may be able to reach the stage to predict the effect of glycosylation on protein structure and function.

AUTHOR INFORMATION Corresponding Author *Email: [email protected]


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Biochemistry

Funding Sources ACKNOWLEDGMENT This research was supported by the Summit Program of Academia Sinica and by the NSF (CHE1664283) and NIH (AI072155). We would like to thank those who contribute their work to the development of programmable one-pot synthesis of oligosaccharides to facilitate the advancement of glycoscience as well as human health. ABBREVIATIONS Ac, acetyl; AI, artificial intelligence; BBL, building block; Bn, benzyl; Bu, n-Butyl; Bz, benzoyl; Cbz, benzyloxycarbonyl; ClBn, ortho-chlorobenzyl; HPLC, high performance liquid chromatography; Lev, levulinoyl; NBz, para-nitrobenzoyl; Ph, phenyl; Phth, phthalimido; PMB, para-methoxybenzyl; PMP, para-methoxyphenyl; RRV, relative reactivity value; STol, pmethylphenyl

thioglycoside;

TBDPS,

tert-butyldiphenylsilyl;

Troc,

2,2,2-

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Biochemistry

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Programmable One-pot Synthesis of Oligosaccharides | Biochemistry

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