인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
ABSTRACT Protein aggregation drives proteinopathies ranging from ALS to systemic amyloidosis, yet the multiscale determinants bridging sequence, structure, and kinetics remain elusive. We present SKALE, an interpretable machine learning framework that integrates sequence motifs, AlphaFold‐derived structural descriptors, and experimental kinetics to decode aggregation mechanisms. SKALE identifies latent hotspots that evade conventional tools and matches high‐performing neural baselines while preserving computational efficiency. In ALS‐linked SOD1 G86R, the model isolates a risk region at residues 72–91 where preserved β‐sheet geometry coincides with weakened hydrogen bonding to drive nucleation. Similarly, analysis of TDP‐43 S332N reveals that a locally unwound helix increases surface exposure, a prediction validated by showing that targeted deletion of model‐identified regions significantly reduces cellular aggregation. The framework generalizes to Tau P301L and PRNP variants where it uncovers distal aggregation‐prone regions to discriminate pathogenic drivers from neutral mutations. Interpretability analysis further disentangles global from mutation‐local mechanisms to reveal that β‐sheet propensity acts as a shared determinant while hydrogen bond dynamics define specific routes to nucleation. These findings establish SKALE as a scalable, disease‐agnostic engine that combines high‐fidelity prediction with biophysical resolution to decode the molecular logic of misfolding and guide therapeutic design.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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