N-MIX: an in silico framework for predicting ADAM10-mediated substrate cleavage sites through structural and spatial

Dae Sun Chung1,2, Jongkeun Park1, Won Jong Choi2

  • 1Department of Medical Sciences, Graduate School of the Catholic University of Korea, Seoul, 06591, Republic of Korea.

Scientific Reports
|July 3, 2026
PubMed

Insights

ADAM10 protease specificity is determined by 3D structure, not just sequence. A new AI tool, N-MIX, uses structural insights to predict ADAM10 cleavage sites, aiding cancer drug discovery.

Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • ADAM10 is a crucial metalloprotease involved in cancer, inflammation, and neurodegeneration.
  • Understanding ADAM10 substrate specificity is vital but challenging due to limitations of sequence-based analysis.

Purpose of the Study:

  • To elucidate the determinants of ADAM10 substrate specificity using structural insights.
  • To develop a computational framework for predicting ADAM10 cleavage sites.

Main Methods:

  • Three-dimensional structural analysis of ADAM10-substrate complexes predicted by AlphaFold2.
  • Identification of key structural determinants governing proteolytic cleavage.
  • Development of the Novel-Metalloproteinase Interaction eXcision (N-MIX) computational framework.

Main Results:

  • Four key determinants of ADAM10 cleavage specificity were identified, emphasizing 3D conformational context over primary sequence.
  • N-MIX successfully classified 50 ADAM10 substrates and prioritized candidate cleavage regions with high accuracy (62.9% Top-1 concordance, 92.6% Top-5 coverage).

Conclusions:

  • ADAM10 substrate specificity is governed by structural and conformational factors.
  • N-MIX offers a scalable, AI-driven platform for identifying ADAM10 cleavage sites, accelerating protease research and anticancer target discovery.