De novo backbone scaffolds for protein design

Abstract

In recent years, there have been significant advances in the field of computational protein design including the successful computational design of enzymes based on backbone scaffolds from experimentally solved structures. It is likely that large-scale sampling of protein backbone conformations will become necessary as further progress is made on more complicated systems. Removing the constraint of having to use scaffolds based on known protein backbones is a potential method of solving the problem. With this application in mind, we describe a method to systematically construct a large number of de novo backbone structures from idealized topological forms in a top–down hierarchical approach. The structural properties of these novel backbone scaffolds were analyzed and compared with a set of high-resolution experimental structures from the protein data bank (PDB). It was found that the Ramachandran plot distribution and relative γ- and β-turn frequencies were similar to those found in the PDB. The de novo scaffolds were sequence designed with RosettaDesign, and the energy distributions and amino acid compositions were comparable with the results for redesigned experimentally solved backbones. Proteins 2010. © 2009 Wiley-Liss, Inc.

High–quality protein backbone reconstruction from alpha carbons using Gaussian mixture models

“…The available methods that are able to reconstruct mainchain atoms while keeping the C a atom positions fixed were compared to the method described in this article. Some methods [5,18,27] are additionally able to refine C a positions but this aspect is not explored in this article.…”

“…The C a atoms were kept fixed in position during minimization with all other backbone atoms free to move. This was performed using a previously described simple backbone potential energy function [5] consisting of local structure molecular mechanics terms derived from the OPLS-UA force field (bond, torsion, improper torsion, 1-4 Lennard-Jones, 1-4 electrostatic, 1-5 LennardJones, and 1-5 electrostatic), a soft steric repulsive term for atom pairs separated by more than four bonds and statistical backbone hydrogen bonding potential terms. In fitting length six fragments to a C a trace, there is inevitably a problem at the beginning and end of each polypeptide chain, where there are insufficient C a atoms either side of the peptide bonds to anchor the fragment.…”

“…[2] C a coarse-grained models have also been successfully used for protein structure prediction applications [3,4] and have the potential to be used in computational protein design. [5,6] In order for the multiscale modeling approach to be viable, a fast and accurate method of interconversion between coarse-and fine-grained models is required. Recent evidence has shown that at low resolutions, low crystallographic R-values are still achievable if the model is accurate, such as if it is restrained to a similar structure refined at high resolution.…”

Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension

“…The incorporation of backbone flexibility in protein design has been recognized as a key challenge in computational protein design (30), with current methods typically reusing backbone fragments from other known protein structures (31,32). Recently, we have developed algorithms to sample loop conformations rapidly using a coarse-grained C α model (33) and to reconstruct proteins backbones accurately (34) as part of an approach that often gave subangstrom root-mean-square deviation (RMSD) loop predictions (35). In this paper, we have applied these techniques to de novo backbone design without using fragments from known protein structures while also explicitly considering alternative conformational states.…”

Designing New Proteins

“…The simplest way to find a backbone structure that is capable of being designed is to reuse experimentally solved backbone structures and/or local loop remodeling; however, there has been work on methods to design de novo backbone structures [17][18][19][20].…”