BioMap's Three Groundbreaking Research Studies Presented at the 2023 AACR Annual Meeting

The 2023 AACR Annual Meeting featured there BioMap selectable breakthroughs, encompassing an innovative protein structure prediction model, target discovery, and high-throughput automation in protein expression/characterization. Notably, this marks BioMap's initial appearance at the prestigious AACR. 

BioMap's contributions at AACR include the following key studies.

Title: xTrimoABFold++: Fast De Novo Antibody Structure Prediction with Atomic Accuracy

Authors: Yining Wang, Shaochuan Li, Bing Yang, Yi Wu Sun, Yangang Wang, Hui Li*, Le Song*

Summary: A superior antibody structure prediction method, xTrimoABFold++, achieves atomic level precision, boosts prediction accuracy by 30% over AlphaFold2, and notably increases CDR-H3 prediction accuracy by 38%. Moreover, xTrimoABFold++'s speed surpasses AlphaFold2 by 540 times, enabling atomic precision predictions within 3 seconds.

Title: CRISPR/Cas9 Screening Identified Novel Membrane Targets Sensitizing Hepatocellular Carcinoma Cells to Natural Killer Cell-Mediated Cytotoxicity

Authors: Shuo Li, Lin Han, Siyao Zhang, Guangfeng Geng, Lei Huo, Dawei Huang, Zhaoren He, Shuoran Li, Zhaoshi Jiang, James X. Rong

Summary: Employing CRISPR/Cas9 screening technology, researchers unveiled novel genes regulating NK cell functionality within liver cancer cells. Suppressing inhibitory genes notably heightens the liver tumor's susceptibility to NK cell mediated cytotoxity. The lack of understanding regarding NK functioning mechanisms necessitates this exploration. Furthermore, the study vertically connects gene modifications with the associated phenotypical manifestations via high throughput assessment. Not only does it unearth novel immune regulatory targets but it can also furnish valuable data for computational biology, facilitating enhanced and precise target prediction.

Title: Accelerating the Drug Discovery Process with an Automated High-Throughput Protein Production and Characterization Platform for AI-Driven Antibody Development of Immunotherapy

Authors: Zhehao Xiong; Wei Jiang; Lijun Xia; Jianhua Huang; Wangyue Jia; Qirong Yang; Zhizhuo Zhang; Cheng-Chi Chao

Summary: An internationally advanced synthetic platform has been crafted for high-volume automated protein creation and evaluation, enabling accelerated AI-driven medicine protein design. The platform executes an entire cycle, spanning gene assembly and protein sample scrutiny within fourteen days at a monthly capacity of 2,000 proteins. Utilizing an in-house developed high-expressing mammalian cell transient transfection process, average antibody yields attain 300 milligrams per litre. Further propelling the looped method and upgrading of AI algorithm offerings, researchers introduced an intensively interconnected management systemfor experimental records, offering digital aids for sample tracking, tailored experiment protocols, instrument automation, data transmission, parameter and indicator monitoring, and information storage.