Abstract

The dipole interaction model is a classical electromagnetic theory for calculating circular dichroism (CD) resulting from the π-π* transitions of amides. The theoretical model, pioneered by J. Applequist, is assembled into a package, DInaMo, written in Fortran allowing for treatment of proteins. DInaMo reads Protein Data Bank formatted files of structures generated by molecular mechanics or reconstructed secondary structures. Crystal structures cannot be used directly with DInaMo; they either need to be rebuilt with idealized bond angles and lengths, or they need to be energy minimized to adjust bond lengths and bond angles because it is common for crystal structure geometries to have slightly short bond lengths, and DInaMo is sensitive to this. DInaMo reduces all the amide chromophores to points with anisotropic polarizability and all nonchromophoric aliphatic atoms including hydrogens to points with isotropic polarizability; all other atoms are ignored. By determining the interactions among the chromophoric and nonchromophoric parts of the molecule using empirically derived polarizabilities, the rotational and dipole strengths are determined leading to the calculation of CD. Furthermore, ignoring hydrogens bound to methyl groups is initially explored and proves to be a good approximation. Theoretical calculations on 24 proteins agree with experiment showing bands with similar morphology and maxima.

Highlights

  • 1182-Pos Board B74 Bridging the Gap between Sequence and Function Alexander Johs

  • We modeled the N-domain of Troponin C (NTnC) at both 4Ò"f and 30Ò"f, for which NMR structures are known

  • We present a survey of N-glycan structures of 35 different glycan sequences in the PDB, showing that N-glycan structures found on homologous glycoproteins are significantly conserved compared to the random background

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Summary

Introduction

1182-Pos Board B74 Bridging the Gap between Sequence and Function Alexander Johs. Oak Ridge National Laboratory, Oak Ridge, TN, USA. Rosetta is a de novo/comparative protein structure modeling algorithm. Our approach should be generally applicable to modeling temperature-dependent protein conformational rearrangements.

Results
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