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Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure

Jimin Pei1,2,3, Jing Zhang1,2,3, Qian Cong1,2,3

  • 1Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

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Summary
This summary is machine-generated.

Deep learning accurately predicted human mitochondrial protein interactions, identifying novel protein pairs and their functions. This advances understanding of mitochondrial biology and disease.

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Area of Science:

  • Computational biology
  • Molecular biology
  • Genomics

Background:

  • Deep learning significantly improved 3D protein structure prediction accuracy.
  • Extending these methods to protein pairs enables large-scale protein-protein interaction (PPI) detection and complex modeling.
  • Mitochondrial proteins are crucial for cellular processes and linked to numerous diseases.

Purpose of the Study:

  • To analyze coevolution of human mitochondrial proteins using deep learning.
  • To predict and model protein-protein interactions (PPIs) within the human mitochondria.
  • To identify novel PPIs and elucidate their functional roles in cellular processes.

Main Methods:

  • Applied RoseTTAFold and AlphaFold, advanced deep learning tools, for structure and interaction prediction.
  • Predicted coevolution for approximately 95% of human mitochondrial protein pairs using RoseTTAFold.
  • Modeled complex structures and contact probabilities for top-ranked pairs using AlphaFold.

Main Results:

  • Identified and structurally modeled numerous high-confidence mitochondrial protein-protein interactions.
  • Validated predictions against known PPIs and experimental structural complexes.
  • Discovered novel PPIs, including PYURF-NDUFAF5, LYRM1-MTRF1L, and COA8-COX10, and predicted their functions.

Conclusions:

  • Deep learning methods effectively predict human mitochondrial protein-protein interactions.
  • The study provides insights into mitochondrial complex organization and function.
  • Identified novel interactions advance understanding of mitochondrial biology and disease mechanisms.