BCL

BCL assembles de novo protein structures from idealized α-helices and β-strands to predict complex protein topologies and generate models from sparse or low-resolution experimental data.


Key Features:

  • Topology Assembly: Constructs protein topologies by assembling idealized α-helices and β-strands to explore complex structural formations.
  • Monte Carlo Metropolis Simulated Annealing: Applies Monte Carlo Metropolis simulated annealing to optimize a knowledge-based potential.
  • Knowledge-based Potential Factors: The potential is informed by radius of gyration, β-strand pairing, secondary structure element (SSE) packing, amino acid pair distances, and environmental context.
  • Optimization Parameters: Considers contact order, agreement with secondary structure predictions, and loop closure to improve model accuracy.
  • Non-local Contact Sampling: Samples non-local contacts by discontinuing protein chains during simulations to facilitate complex topology formation.
  • Flexible Loop Exclusion: Excludes flexible loop regions from folding simulations to reduce the conformational search space and accelerate sampling.

Scientific Applications:

  • Modeling from sparse or low-resolution data: Generates structural models when experimental data are sparse or low resolution to support structural interpretation.
  • Restraint generation for secondary structure regions: Produces models that provide restraints for defined secondary structure regions to assist downstream modeling or experimental refinement.

Methodology:

Assembles protein topologies from idealized α-helices and β-strands and uses Monte Carlo Metropolis simulated annealing to optimize a knowledge-based potential informed by radius of gyration, β-strand pairing, SSE packing, amino acid pair distances, and environmental context; the method considers contact order, agreement with secondary structure predictions, and loop closure, samples non-local contacts by discontinuing protein chains, and excludes flexible loop regions to accelerate simulations; benchmarked on 66 proteins (83–293 amino acids) achieving RMSD100 < 8.0 Å for 61 proteins, recovering >20% native contacts, and assembling topologies up to 215 residues with relative contact order 0.46.

Topics

Details

Tool Type:
command-line tool
Operating Systems:
Linux, Windows, Mac
Programming Languages:
C++
Added:
8/3/2017
Last Updated:
11/25/2024

Operations

Data Inputs & Outputs

Publications

Karakaş M, Woetzel N, Staritzbichler R, Alexander N, Weiner BE, Meiler J. BCL::Fold - De Novo Prediction of Complex and Large Protein Topologies by Assembly of Secondary Structure Elements. PLoS ONE. 2012;7(11):e49240. doi:10.1371/journal.pone.0049240. PMID:23173050. PMCID:PMC3500284.

Documentation

Links