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Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations.

Luis J WalterPatrick K QuoikaMartin Zacharias
Published in: Journal of chemical information and modeling (2024)
Many biological functions are mediated by large complexes formed by multiple proteins and other cellular macromolecules. Recent progress in experimental structure determination, as well as in integrative modeling and protein structure prediction using deep learning approaches, has resulted in a rapid increase in the number of solved multiprotein assemblies. However, the assembly process of large complexes from their components is much less well-studied. We introduce a rapid computational structure-based (SB) model, GoCa, that allows to follow the assembly process of large multiprotein complexes based on a known native structure. Beyond existing SB Go̅-type models, it distinguishes between intra- and intersubunit interactions, allowing us to include coupled folding and binding. It accounts automatically for the permutation of identical subunits in a complex and allows the definition of multiple minima (native) structures in the case of proteins that undergo global transitions during assembly. The model is successfully tested on several multiprotein complexes. The source code of the GoCa program including a tutorial is publicly available on Github: https://github.com/ZachariasLab/GoCa. We also provide a web source that allows users to quickly generate the necessary input files for a GoCa simulation: https://goca.t38webservices.nat.tum.de.
Keyphrases
  • deep learning
  • protein protein
  • molecular dynamics
  • amino acid
  • high resolution
  • small molecule
  • machine learning
  • quality improvement
  • single molecule
  • artificial intelligence
  • transcription factor
  • mass spectrometry