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A surface measure for probabilistic structural computations

J P Schmidt1, C C Chen, J L Cooper

  • 1Section on Medical Informatics, Stanford University, USA. jschmidt@incyte.com

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|October 23, 1998
PubMed
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Incorporating surface proximity data significantly enhances molecular structure computation. This method improves accuracy by up to 80% when combined with distance constraints, aiding in determining atomic positions.

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Biophysics

Background:

  • Determining 3D molecular structures from sparse data is challenging.
  • Integrating diverse experimental constraints (distances, angles, volume, shape, surface) is complex.
  • Utilizing surface/buried atom information as a constraint has been inefficient.

Purpose of the Study:

  • To investigate the efficacy of using surface measures in molecular structure computation.
  • To introduce a novel, efficient surface proximity measure for structural optimization.
  • To demonstrate the improvement in computed structure quality by incorporating surface data.

Main Methods:

  • Employed probabilistic least squares computations for integrating heterogeneous data sources.

Related Experiment Videos

  • Introduced Maximal Conic View (MCV), a novel, computable, and differentiable surface proximity measure.
  • Validated MCV against established surface area measures.

    • Main Results:

    • MCV showed good correlation with total exposed surface area.
    • Adding surface proximity information to distance constraints significantly improved computed structure quality (up to 80% improvement in RMS fit).
    • The improvement was most notable when 30-50% of short-range distances were provided.

    Conclusions:

    • Surface proximity information is a powerful constraint for molecular structure estimation.
    • The novel MCV measure facilitates the integration of surface data into structural computations.
    • This approach offers a more efficient and accurate method for determining 3D molecular structures.