Toward better refinement of comparative models: Predicting loops in inexact environments

By Benjamin D. Sellers, Kai Zhu, Suwen Zhao, Richard A. Friesner, Matthew P. Jacobson. 2008

92 citations recorded by the source corpus.

Achieving atomic‐level accuracy in comparative protein models is limited by our ability to refine the initial, homolog‐derived model closer to the native state. Despite considerable effort, progress in developing a generalized refinement method has been limited. In contrast, methods have been described that can accurately reconstruct loop conformations in native protein structures. We hypothesize that loop refinement in homology models is much more difficult than loop reconstruction in crystal structures, in part, because side‐chain, backbone, and other structural inaccuracies surrounding the loop create a challenging sampling problem; the loop cannot be refined without simultaneously refining adjacent portions. In this work, we single out one sampling issue in an artificial but useful test set and examine how loop refinement accuracy is affected by errors in surrounding side‐chains. In 80 high‐resolution crystal structures, we first perturbed 6–12 residue loops away from the crystal conformation, and placed all protein side chains in non‐native but low energy conformations. Even these relatively small perturbations in the surroundings made the loop prediction problem much more challenging. Using a previously published loop prediction method, median backbone (N‐Cα‐C‐O) RMSD's for groups of 6, 8, 10, and 12 residue loops are 0.3/0.6/0.4/0.6 Å, respectively, on native structures and increase to 1.1/2.2/1.5/2.3 Å on the perturbed cases. We then augmented our previous loop prediction method to simultaneously optimize the rotamer states of side chains surrounding the loop. Our results show that this augmented loop prediction method can recover the native state in many perturbed structures where the previous method failed; the median RMSD's for the 6, 8, 10, and 12 residue perturbed loops improve to 0.4/0.8/1.1/1.2 Å. Finally, we highlight three comparative models from blind tests, in which our new method predicted loops closer to the native conformation than first modeled using the homolog template, a task generally understood to be difficult. Although many challenges remain in refining full comparative models to high accuracy, this work offers a methodical step toward that goal. Proteins 2008. © 2008 Wiley‐Liss, Inc.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Refinement of unreliable local regions in template‐based protein models (2012, unknown): “41 It still remains to be seen how well such methods perform in refining loops of template-based models.”
Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “The search algorithms include the minimum perturbation method (Fine et al., 1986), dihedral angle search through a rotamer library (Zhu et al., 2006; Sellers et al., 2008), molecular dynamics simulations (Bruccoleri and Karplus, 1990; van Vlijmen and Karplus, 1997), genetic algorithms (Ring et al.,…”
Fast Protein Loop Sampling and Structure Prediction Using Distance-Guided Sequential Chain-Growth Monte Carlo Method (2014, unknown): “They are also challenging to model computationally [5,6].”
Comparative Protein Structure Modeling Using Modeller (2006, methodology): “The search algorithms include the minimum perturbation method (Fine et al., 1986), dihedral angle search through a rotamer library (Zhu et al., 2006; Sellers et al., 2008), molecular dynamics simulations (Bruccoleri and Karplus, 1990; van Vlijmen and Karplus, 1997), genetic algorithms (Ring et al.,…”
The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling (2011, methodology): “The method has been previously described by Sellers et al. ,29 in which the surrounding side chains that have heavy atoms within 7.5 A˚ from any C-beta atoms in the target loop are optimized simultaneously with the loop.”
Comparative Protein Structure Modeling Using MODELLER (2016, methodology): “The search algorithms include the minimum perturbation method (Fine et al., 1986), dihedral angle search through a rotamer library (Zhu et al., 2006; Sellers et al., 2008), molecular dynamics simulations (Bruccoleri and Karplus, 1990; van Vlijmen and Karplus, 1997), genetic algorithms (Ring et al.,…”
Analysis and Modeling of the Variable Region of Camelid Single Domain Antibodies (2011, background): “ronment is challenging (52, 53), because multiple loop sampling can produce false-positive structures and requires significantly more computational time.”