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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...
Noncovalent Attractions in Biomolecules02:35

Noncovalent Attractions in Biomolecules

Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

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Optimized atomic statistical potentials: assessment of protein interfaces and loops.

Guang Qiang Dong1, Hao Fan, Dina Schneidman-Duhovny

  • 1Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA.

Bioinformatics (Oxford, England)
|October 1, 2013
PubMed
Summary

We developed a new Bayesian framework for statistically optimized atomic potentials (SOAP) to improve protein modeling. This method enhances protein-protein docking and loop modeling accuracy, outperforming existing scoring functions.

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Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Statistical potentials are crucial for modeling protein structures and interactions.
  • Existing methods often rely on approximations and questionable statistical mechanical assumptions.
  • A robust statistical framework is needed to improve accuracy and avoid limitations.

Purpose of the Study:

  • To develop a general Bayesian framework for inferring statistically optimized atomic potentials (SOAP).
  • To replace the traditional reference state with data-driven 'recovery' functions.
  • To incorporate orientation-dependent interactions, such as hydrogen bonds, by restraining relative bond orientations.

Main Methods:

  • Derived a general Bayesian framework for inferring statistically optimized atomic potentials (SOAP).
  • Implemented data-driven 'recovery' functions instead of a reference state.
  • Restrained relative orientation between covalent bonds to capture orientation-dependent interactions.

Main Results:

  • Developed SOAP potentials for protein-protein docking (SOAP-PP) and loop modeling (SOAP-Loop).
  • SOAP-PP achieved near-native models in the top 10 for 40% of benchmark cases, surpassing ZDOCK and FireDock.
  • SOAP-Loop yielded an average RMSD of 1.5 Å for loop modeling, outperforming Rosetta, DFIRE, DOPE, and PLOP.

Conclusions:

  • The Bayesian framework provides a more accurate approach to statistical potentials.
  • SOAP potentials offer improved performance in protein-protein docking and loop modeling.
  • This framework has the potential for broader applications in leveraging experimentally determined protein structures.