Potential energy functions for protein design.

By F. Boas, Pehr Harbury, Richard Lavery, Kim A. Sharp, L. Jones. 2007

147 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

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In Materials Chemistry

In Molecular Biology

Citations in the corpus

Computational Protein Design with Deep Learning Neural Networks (2018, background): “25,26 In many cases, the sequences from computational design are subject to further filtering by considering various factors such as shape complementarity 9 and in silico folding free energy landscape, 27,28 where human experience and familiarity with the designed protein play an important role,…”
Energy-based models for atomic-resolution protein conformations (2020, methodology): “…use of complex energy functions that approximate the physical forces that determine protein conformations (Cornell et al., 1995; Jorgensen et al., 1996; MacKerell Jr et al., 1998), incorporating knowledge about statistical patterns in databases of protein crystal structures (Boas & Harbury, 2007).”
Improving computational protein design by using structure-derived sequence profile (2010, background): “Some scoring functions are made of various physical energy terms, 50 including van der Waals interaction, solvation, 51 , 52 electrostatics, 53 and hydrogen bonding.”
Solvent accessible surface area approximations for rapid and accurate protein structure prediction (2009, background): “KBPs relate the probability of a conformation to the energy associated with that conformation using an inverse Boltzmann relation [13]: Δ G ¼ (cid:1) RT ln P which provides a means for the derivation of a free energy from a propensity.”
Energy functions in de novo protein design: current challenges and future prospects. (2013, background): “Energy functions for protein design are typically modified from the energy functions for protein folding or dynamics studies (for a discussion, see 6, 13, 72, 85, 88, 97, 109, 114).”
Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design (2018, methodology): “The energy functions that are typically used for computational protein design 22,23 , including the Rosetta Energy Function 24 , are expressed as the sum of pairwise energies, which is important for computational efficiency and algorithmic compatibility 25 .”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, background): “Thus, they are successfully applied to many problems, such as discrimination of the native structure from decoys (9,10), fold recognition (11), structure prediction (12), protein docking (13–15) and design (16,17), and prediction of protein stability and afﬁnity (18–20).”