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Implicitly and Differentiably Representing Protein Surfaces and Interfaces.

Cory B Scott1, Charlie Rothschild2, Benjamin E Nye2

  • 1Department of Mathematics and Computer Science, Colorado College, Colorado Springs, CO 80909, USA, cbs@coloradocollege.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new method to represent proteins using signed distance functions (SDFs). This approach shows promise for machine learning applications in structural biology and protein analysis.

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Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Machine learning

Background:

  • Proteins are fundamental biological molecules with complex 3D structures.
  • Representing protein geometry is crucial for understanding function and interactions.
  • Existing methods for protein representation have limitations in machine learning contexts.

Purpose of the Study:

  • To introduce a novel pipeline for implicitly representing proteins and protein complexes.
  • To explore the utility of signed distance functions (SDFs) for protein representation in machine learning.
  • To demonstrate the potential of SDF-based protein models in biologically relevant applications.

Main Methods:

  • Representing each atom as a sphere with its van der Waals radius.
  • Constructing the protein's molecular surface as a union of these atomic spheres.
  • Utilizing signed distance functions (SDFs) to implicitly define the protein geometry.
  • Applying this SDF representation in a machine learning framework.

Main Results:

  • A proof-of-concept pipeline for SDF-based protein representation was successfully developed.
  • The SDF representation was shown to be a viable alternative to traditional methods for machine learning.
  • Potential applications in areas such as protein structure prediction and drug discovery were highlighted.

Conclusions:

  • The union of SDFs offers a powerful implicit representation for proteins and protein complexes.
  • This approach has not been widely adopted in machine learning settings but shows significant potential.
  • Further experimental validation is required to fully establish the efficacy of SDF-based protein models.