Modeling protein loops using a ϕ_i+1, Ψ_i dimer database

Abstract

We present an automated method for modeling backbones of protein loops. The method samples a database of 4,+, and $; angles constructed from a nonredundant version of the Protein Data Bank (PDB). The dihedral angles c$j+l and $, completely define the backbone conformation of a dimer when standard bond lengths, bond angles, and a trans planar peptide configuration are used. For the 400 possible dimers resulting from 20 natural amino acids, a list of allowed 4,+, , $; pairs for each dimer is created by pooling all such pairs from the loop segments of each protein in the nonredundant version of the PDB. Starting from the N-terminus of the loop sequence, conformations are generated by assigning randomly selected pairs of $, for each dimer from the respective pool using standard bond lengths, bond angles, and a frans peptide configuration. We use this database to simulate protein loops of lengths varying from 5 to 11 amino acids in five proteins of known three-dimensional structures. Typically, 10,000-50,000 models are simulated for each protein loop and are evaluated for stereochemical consistency. Depending on the length and sequence of a given loop, 50-80Vo of the models generated have no stereochemical strain in the backbone atoms. We demonstrate that, when simulated loops are extended to include flanking residues from homologous segments, only very few loops from an ensemble of sterically allowed conformations orient the flanking segments consistent with the protein topology. The presence of near-native backbone conformations for loops from five different proteins suggests the completeness of the dimeric database for use in modeling loops of homologous proteins. Here, we take advantage of this observation to design a method that filters near-native loop conformations from an ensemble of sterically allowed conformations. We demonstrate that our method eliminates the need for a loop-closure algorithm and hence allows for the use of topological constraints of the homologous proteins or disulfide constraints to filter near-native loop conformations.Keywords: de novo folding; dihedral angles; homology modeling; knowledge-based method; loop modelingIn the absence of models derived from X-ray or N M R techniques, homology modeling methods remain as the single most effective way to obtain three-dimensional models of structurally uncharacterized proteins. Homology modeling relies on the observation that proteins in a given family have a similar tertiary topology. The polypeptide segments that are structurally conserved within a family of proteins are generally known as structurally conserved regions (SCRs) and those polypeptide segments that are variable are known as structurally variable regions (SVRs, herein termed "loops"). Although several methods exist for modeling SCRs, modeling SVRs remains as one of the most significant challenges in homology modeling.In its most basic form, homology modeling transfers structural information from one member of a family, designated as the template protein, to one or more m...

The relationship between peptide plane rotation (PPR) and similar conformations

“…ed psi i and phi i q 1 values have been shown to incorporate conformational information and chain growth direction for two consecutive amino acids. 36 This is unlike the conventional torsion Ž . Ž .…”

CODA: A combined algorithm for predicting the structurally variable regions of protein models

“…This is not the only way to utilise the information from the PDB. Sudarsanam et al (1995) describe an SVR modeling procedure that uses a database of all (i +1) , (i) dimers from a nonredundant version of the PDB-approximately 50,000. These dimers were put into 400 pools representing all possible dimers that can result from the 20 naturally occurring amino acids.…”

MOLS sampling and its applications in structural biophysics

“…The computational prediction of the three-dimensional structure of the loops has received extensive specific attention, and several algorithms have been developed (Sudarsanam et al 1995;van Vlijmen and Karplus 1997;Wojcik et al 1999;Fiser et al 2000;DePristo et al 2003;Deane and Blundell 2000). There have also been several attempts to classify loop structures (Leszczynski and Rose 1986;Ring et al 1992;Efimov 1993).…”