| 학회 | 한국공업화학회 |
| 학술대회 | 2022년 봄 (05/11 ~ 05/13, 제주국제컨벤션센터(ICC JEJU)) |
| 권호 | 26권 1호 |
| 발표분야 | [생물공학] 생물공정에 있어서 효소공학 최신 연구 및 전망 |
| 제목 | Developing AI-based prediction model of substrate specificity of amine transaminase |
| 초록 | Creation of enzyme variants displaying desirable catalytic performance usually necessitates tedious and time-consuming procedures for library generation and selection, which may be circumvented by a computational method based on a precise understanding on reaction mechanism. To address this problem, we aimed at developing an automated molecular modeling method based on artificial intelligence algorithms to afford computational prediction of substrate specificity of amine transaminase. To this end, we examined structural features of substrate docking poses and reaction intermediates to extract important descriptor parameters. Multi-variable linear regressions using the descriptors were performed to generate a reliable scoring function which was then used to assess the structure-activity relationships guided by automated modeling procedures. The computational method afforded profiling of substrate preference, which agreed well with experimental results. |
| 저자 | 신종식 |
| 소속 | 연세대 |
| 키워드 | amine transaminase; molecular modeling; substrate specificity; biocatalysis |
