AutoRNC: an automated modeling program for building atomic models of ribosome-nascent chain complexes

Area of Science:

• Computational biology and structural bioinformatics.
• Protein folding and biophysics.

Background:

• Interpreting co-translational protein folding experiments often requires computational modeling of ribosome-nascent chain (RNC) complexes.
• Building accurate 3D models of RNCs is challenging due to variations in size and structural complexity, often necessitating expert knowledge.

Approach:

• Introduced AutoRNC, an automated modeling program for rapid construction of plausible RNC atomic models.
• AutoRNC accepts user-defined secondary/tertiary structure specifications for the nascent chain.
• It builds conformations by sampling and assembling dipeptide structures from the RCSB, respecting ribosomal constraints.

Key Points:

• AutoRNC-generated models of unfolded proteins show good agreement with experimental radii of gyration.
• The program successfully models diverse RNC constructs with existing experimental data.
• AutoRNC requires minimal computational resources.

Conclusions:

• AutoRNC serves as a valuable hypothesis-generation tool for experimental studies on co-translational folding.
• It can predict the folding potential of designed constructs and provide starting points for simulations.