AI Empowered Glyco-synthesis: Hierarchical and Programmable One-pot Synthesis of Oligosaccharides

Abstract

The programmable one-pot oligosaccharide synthesis method was designed to enable the rapid synthesis of a large number of oligosaccharides, using the software Optimer to search Building BLocks (BBLs) with defined relative reactivity values (RRVs) to be used sequentially in the one-pot reaction. However, there were only about 50 BBLs with measured RRVs in the original library and the method could only synthesize small oligosaccharides due to the RRV ordering requirement. Here, we increase the library to include 154 validated BBLs and more than 50,000 virtual BBLs with predicted RRVs by machine learning. We also develop the software Auto-CHO to accommodate more data handling and support hierarchical one-pot synthesis using fragments as BBLs generated by the one-pot synthesis. This advanced programmable one-pot method provides potential synthetic solutions for complex glycans with four successful examples demonstrated in this work. ? 摘要 ??? 可規劃一鍋化寡醣合成法被設計用於快速大量合成寡醣，透過Optimer軟體的幫助，搜尋醣元件資料庫(Building Block Library)，提供由大到小相對反應速率(RRV)的醣元件合成藍圖。然而原本的資料庫大約只有50個被實驗驗證RRV的醣元件；此外基於醣元件RRV順序由大到小的要求，此方法僅適用於小寡醣的合成。為了解決上述兩項問題，我們擴增醣元件資料庫，將實驗驗證RRV的醣元件擴增至154個，並建立超過50,000虛擬醣元件，以機器學習精準預測每個虛擬醣元件的RRV。此外我們設計階層化拆解醣分子演算法，開發了新軟體Auto-CHO。Auto-CHO可將一個以一鍋化方法合成出來的醣片段可以作為新的醣元件，再次用於另一個一鍋化方法。因此Auto-CHO對於複雜醣類的合成提供了可行方案。在此研究中，我們以四種重要的醣類合成作為範例。