An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes.

By Tanja Kortemme, Alexandre V. Morozov, David Baker. 2003

510 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. David Baker
2. Yang Zhang
3. Jeffrey J. Gray
4. Arlo Z. Randall
5. Tanja Kortemme
6. Robin Pearce
7. Brian Kuhlman
8. N. V. Dokholyan

In Materials Chemistry

1. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Yang Zhang
5. Jens Meiler
6. Hahnbeom Park
7. Brian Kuhlman
8. David E. Kim

In Molecular Biology

1. David Baker
2. Jeffrey J. Gray
3. Tanja Kortemme
4. Jens Meiler
5. Yang Zhang
6. Hahnbeom Park
7. I. Samish
8. Brian Kuhlman

Citations in the corpus

• Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “2004), the default fullatom energy function in Rosetta is composed of a Lennard-Jones term, an implicit solvation term (Lazaridis & Karplus, 1999), an orientation-dependent hydrogen bond term (Kortemme et al., 2003), sidechain and backbone torsion potentials derived from the PDB, a short-ranged know”
• Computational Alanine Scanning of Protein-Protein Interfaces (2004, background): “The function consists of a linear combination of a Lennard-Jones potential to describe atomic packing interactions, an implicit solvation model (6), an orientation-dependent hydrogen-bonding potential derived from high-resolution protein structures (7), statistical terms approximating the backbone-d”
• Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design (2018, background): “Hydrogen bonds are detected using Rosetta’s standard hydrogen bonding potential, which depends on the distance and relative orientation of the donor and acceptor groups 17,29 .”
• Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You (2010, background): “It combines the 6-12 Lennard-Jones potential for van der Waals forces, a solvation approximation ( 13 ), an orientation-dependent hydrogen bonding potential ( 14 ), a knowledge-based electrostatics term, and a knowledge-based conformation-dependent amino acid internal free energy term ( 9 ).”
• Protein structure modeling for CASP10 by multiple layers of global optimization (2014, methodology): “…from templates, where we combined physical energy terms including dynamic fragment assembly (DFA) energy4 together with DFIRE5 statistical potential energy, hydrogen bonding term,6 and GOAP terms.7 For side-chain re-modeling, we used an in-house energy function in the discrete rotamer space.”
• Extensive protein and DNA backbone sampling improves structure-based specificity prediction for C2H2 zinc fingers (2011, background): “Incorporation of the explicit electrostatics term in addition to Rosetta’s orientation-dependent hydrogen bonding potential (47) helps to prevent unfavorable short-range electrostatic interactions, modulates the interaction strength of charged and polar hydrogen bonds, and rewards electrostatic inte”