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Using multiple computer-predicted structures as molecular replacement models: application to the antiviral

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Summary

Advanced protein structure prediction tools successfully generated molecular replacement (MR) models for the LCB2 protein. Analysis revealed that structural variations represent a benign model bias, forming a multiconformer ensemble reflecting protein dynamics.

Keywords:
LCB2computer-predicted structuresmolecular replacementmulticonformer ensemblesprotein crystallographyrotamersside-chain conformations

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Area of Science:

  • Structural Biology
  • Computational Biology
  • Protein Engineering

Background:

  • Accurate protein structure determination is crucial for understanding biological function.
  • Computational methods for solving protein structures via molecular replacement (MR) are continuously evolving.
  • The engineered protein LCB2, a 58-residue three-helix bundle, serves as a test case for assessing novel structure prediction algorithms.

Purpose of the Study:

  • To evaluate the performance of various contemporary computational tools in generating molecular replacement (MR) models for protein structure determination.
  • To investigate the nature of structural differences observed between predicted models and experimentally determined structures.
  • To explore the concept of benign model bias and its implications for interpreting protein conformational heterogeneity.

Main Methods:

  • Utilized multiple computer-predicted molecular replacement (MR) models generated by AlphaFold3, AlphaFold2, MultiFOLD, Rosetta, RoseTTAFold, and trRosetta.
  • Employed Coot and Phenix for multi-start structure determination, assessing convergence and structural accuracy (all-atom RMSD).
  • Assigned B factors using predictor-generated confidence scores or accessible surface area (ASA) values.
  • Analyzed side-chain conformations and electron density maps to identify sources of structural variation.
  • Performed molecular dynamics (MD) simulations to support structural interpretations.

Main Results:

  • All tested advanced predictors (AlphaFold3, AlphaFold2, MultiFOLD, Rosetta, RoseTTAFold, trRosetta) successfully generated MR models for the LCB2 protein.
  • Six highly similar structures (within 0.25 Å all-atom RMSD) were determined, with differences largely attributed to a specific crystal contact.
  • Observed variations in surface side-chain conformations across the six solutions, though individual electron densities suggested single rotameric states.
  • Interpreted these variations as a benign model bias, where models matching actual rotamers enhance electron density.
  • Grouping the six structures into a multiconformer ensemble significantly improved crystallographic refinement statistics (Rwork and Rfree).

Conclusions:

  • Advanced computational tools are effective in generating starting models for protein structure determination via MR.
  • Structural variations in the LCB2 dataset, initially appearing as model bias, represent a multiconformer ensemble reflecting protein dynamics.
  • This multiconformer interpretation enhances the accuracy of structural models and provides insights into protein flexibility.
  • The study highlights the utility of combining computational predictions with experimental data for a comprehensive understanding of protein structure and dynamics.