挑戰:

• SARM1具顯著的動態構象變化，體外難以穩定并捕捉其激活態構象；
• 目前的候選藥物分子主要為競爭性抑制劑，缺乏明確的抑制機制。

目標:

• 通過晶泰科技AI驅動的Cryo-EM圖像處理模塊，幫助加速解析抑制劑與SARM1的高分辨率電鏡結構，為理性藥物設計提供新思路、新方向；
• 進一步揭示該靶點的自抑制態與激活態的轉換機制，加深對靶點作用機理的理解。

結構解析流程：

解析結果：

項目小結：

僅使用200kV電鏡，在8 周內成功解析SARM1與抑制劑的2.8 ?結構，這項開創性研究成果揭示了一種小分子抑制劑的競爭性抑制機制。高比例NMN/NAD+誘導激活SARM1，抑制劑在SARM1催化下與NAD+發生堿基交換反應，生成抑制劑-ADPR這一新產物，該產物與SARM1結合并鎖定其處于激活態構象，封鎖底物NAD+結合口袋，從而抑制SARM1的NAD+降解活性。該結果為證實SARM1的激活機制和堿基交換反應提供了最直接的結構證據。

數據計算過程中，通過使用 AI 驅動的圖像處理模塊實現了目標顆粒的精確挑選，與經典方法相比，在同等算力下，加速整體計算流程并實現更高的分辨率。

同時，首次從分子水平揭示了 SARM1 的激活機制，加深對其如何介導退行性疾病發生機理的理解。

The post 8周解析SARM1蛋白結構 appeared first on 晶泰科技 XtalPi.

Explore Two Case Studies on How XtalPi Leveraged MicroED to Address Different Polymorphism Challenges

Structural elucidation is a critical process in understanding and addressing polymorphism challenges in drug development. Here are two case studies highlighting how our technical team employed MicroED to determine the structures of crystalline samples in just 5 days, providing crucial structural information to tackle complex polymorphism issues.

Case Study 1: Determining Component Ratio in a Non-stoichiometric Co-former System Using MicroED?

XtalPi’s Approach & Findings

• Explained the varying stoichiometric ratios of a monobasic API and methanesulfonic acid in a non-stoichiometric co-former through MicroED-derived crystal structure.
• Revealed that methanesulfonic acids are situated in disordered lattice channels within a complex asymmetric unit that contains 8 APIs and 14 co-formers.

Case Study 2: MicroED Discovered an Unexpected Polymorphism from Samples with Highly Similar XRPD Patterns

XtalPi’s Approach & Findings

• Identified distinct polymorphic structures from two batches of samples with nearly identical XRPD spectra but different analytical profiles.
• Mitigated polymorphism risk by achieving definitive structural assignments using MicroED when single crystals could not be obtained for the samples. 

The post How XtalPi Utilized MicroED to Tackle Different Polymorphism Challenges appeared first on 晶泰科技 XtalPi.

Case Study: Harnessing CSP and MicroED to Determine Remdesivir’s Most Stable Polymorph in Just 33 Days

Accurately identifying the most stable polymorph is essential for successful drug development, especially when facing tight R&D timelines and limited material availability.?Faced with two similar Remdesivir anhydrates, our team utilized an integrated?experimental?and AI-driven computational approach to quickly pinpoint the most stable polymorph across varying temperatures, ensuring a reliable drug product development.?

Here’s how we delivered results 2x faster than standard polymorph research:

• Utilized?MicroED?to determine crystal structures of Form II and, for the first time, of Form IV to explain their similar XRPD patterns and properties.?
• Leveraged AI-powered?Crystal Structure Prediction (CSP)?platform to predict and recommend Form II as the most stable anhydrate at room and high temperatures, well ahead of traditional timelines.?

The post Accelerated Identification of the Most Stable Remdesivir Polymorph in 33 Days Using an Integrated Experimental and Computational Approach appeared first on 晶泰科技 XtalPi.

Case Study: How the XtalPi Team Discovered Novel, Non-Covalent, Potent Hits Against GPX4 in 28 Days

GPX4 is a critical enzyme for cellular antioxidant defense, but is a highly challenging target. In this case study, we share the story of how we discovered three novel non-covalent hit compounds with biochemical IC₅₀?< 10μM in just 28 days.

• Using predictive AI models and physics-based computations, we probed key pharmacophores with high accuracy and throughput, even without reference compounds.
• 159 library compounds were proposed and 124 were successfully synthesized using our automation platform.

The post Hit Identification – Novel Hits for GPX4 appeared first on 晶泰科技 XtalPi.

The post Application Note: DEL Screening appeared first on 晶泰科技 XtalPi.

Case Study: How the XtalPi Team Discovered a Selective, Novel Lead Series for Key Cancer Target SMARCA2

Loss-of-function mutations in SMARCA4 occur in various cancers, leading to an increased dependency on the structurally similar SMARCA2 for activity. This makes SMARCA2 an attractive therapeutic target due to its increased importance in cancer cell survival.

However, identifying novel small-molecule SMARCA2-selective inhibitors poses significant challenges. To address this, XtalPi leveraged its advanced AI-powered discovery platform and Cryo-EM technology, opening up new possibilities for targeted cancer treatments.

Our Key Results: 

• Discovered a novel lead series with high efficacy and 20-fold selectivity for SMARCA2 over SMARCA4 in biochemical assays, showcasing strong potential for further development.
• Identified a unique SMARCA2 binding pocket using Cryo-EM at 2.9 ? resolution, offering new insights for the design of selective inhibitors and advancing structure-based drug discovery.
• Achieved 100% library compound synthetic success rate by proprietary synthetic feasibility prediction models.

The post Lead Identification – Discovering Selective Inhibitors Against an Unreported Binding Pocket of SMARCA2 via a Rational Drug Design Approach appeared first on 晶泰科技 XtalPi.