LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

ESCASA: Analytical estimation of atomic coordinates from coarse‐grained geometry for nuclear‐magnetic‐resonance‐assisted protein structure modeling. I. Backbone and Hβ protons

Photo from wikipedia

A method for the estimation of coordinates of atoms in proteins from coarse‐grained geometry by simple analytical formulas (ESCASA), for use in nuclear‐magnetic‐resonance (NMR) data‐assisted coarse‐grained simulations of proteins is… Click to show full abstract

A method for the estimation of coordinates of atoms in proteins from coarse‐grained geometry by simple analytical formulas (ESCASA), for use in nuclear‐magnetic‐resonance (NMR) data‐assisted coarse‐grained simulations of proteins is proposed. In this paper, the formulas for the backbone Hα and amide (HN) protons, and the side‐chain Hβ protons, given the Cα‐trace, have been derived and parameterized, by using the interproton distances calculated from a set of 140 high‐resolution non‐homologous protein structures. The mean standard deviation over all types of proton pairs in the set was 0.44 Å after fitting. Validation against a set of 41 proteins with NMR‐determined structures, which were not considered in parameterization, resulted in average standard deviation from average proton–proton distances of the NMR‐determined structures of 0.25 Å, compared to 0.21 Å obtained with the PULCHRA all‐atom‐chain reconstruction algorithm and to the 0.12 Å standard deviation of the average‐structure proton–proton distance of NMR‐determined ensembles. The formulas provide analytical forces and can, therefore, be used in coarse‐grained molecular dynamics.

Keywords: estimation; nuclear magnetic; magnetic resonance; geometry; coarse grained; grained geometry

Journal Title: Journal of Computational Chemistry
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.