안녕하십니까. 서울무신사 토토사이트교 바이오인공지능연구단에서 AI와 바이오 및 과학 연구의 융합을 주제로 하는 11월 월례회에 여러분을 초대합니다.
일시: 2025년 11월 17일 월요일 오후 16시 30분
장소: 서울무신사 토토사이트교 유전공학연구소(105동) 서관 1층 강당(132호)
연사 1: 정진주 교수 (Yale School of Medicine, Dept. of Cellular & Molecular Physiology)
제목: From Blueprint to Atlas: AI in Predicting Sperm Signaling and Reconstructing Fertilization in 3D
초록: Understanding the molecular choreography of mammalian fertilization, from initial signaling to the complete cellular merger, presents immense challenge due to its complexity and transient nature. This talk will present a multi-scale AI-strategy to dissect this fundamental process, moving from a molecular blueprint to a cellular atlas. We first employ predictive AI to engineer sperm signaling by targeting the critical sperm CatSper ion channel. We then use machine learning segmentation to reconstruct that first complete, high-resolution 3D atlas of the fertilizing sperm-egg complex in situ.
** 본 강연은 한국어로 진행됩니다. This lecture will be held in Korean.
연사 2: 윤태영 교수 (서울무신사 토토사이트교 자연과학무신사 토토사이트 생명과학부)
제목: Biological Insights Provided by Artificial Intelligence
초록: Antibodies play a central role in adaptive immunity by recognizing antigens through highly specific interactions mediated by their Complementarity Determining Regions (CDRs). These typically unstructured loops undergo substantial conformational rearrangements upon antigen binding—induced fit-like mechanisms that remain poorly understood, even by the most advanced AI models for protein structure prediction and design. Here, we present a novel approach leveraging the Single-Protein Interaction Detection (SPID) platform, repurposed to systematically map the local interaction landscapes of antibody-antigen pairs with unprecedented resolution and throughput. SPID enables precise quantification of dissociation constants and antibody material properties across thousands of CDR variants per week—achieving accuracy on par with conventional gold standards such as Surface Plasmon Resonance (SPR) and Bio-Layer Interferometry (BLI), but at a significantly enhanced scale. Using targeted CDR editing and high-throughput screening, we delineate optimization routes that improve both antigen-binding affinity and biophysical developability. We further demonstrate how SPID-generated datasets can power deep learning approaches, significantly enhancing predictive modeling of antibody-antigen interactions.
** 본 강연은 영어로 진행됩니다. This lecture will be held in English.
※ 월례회에 참석하시는 분들에게 간단한 다과를 제공합니다. (소진 시 조기 종료)