A Simple Model of Backbone Flexibility Improves Modeling of Side-chain Conformational Variability

Abstract

The considerable flexibility of side-chains in folded proteins is important for protein stability and function, and may play a role in mediating pathways of energetic connectivity between allosteric sites. While sampling side-chain degrees of freedom has been an integral part of several successful computational protein design methods, the predictions of these approaches have not been directly compared to experimental measurements of side-chain motional amplitudes. In addition, protein design methods generally keep the backbone fixed, an approximation that may substantially limit the ability to accurately model side-chain flexibility. Here we describe a Monte Carlo approach to modeling side-chain conformational variability and validate our method against a large dataset of methyl relaxation order parameters derived from Nuclear Magnetic Resonance experiments (17 proteins and a total of 530 data points). We also evaluate a model of backbone flexibility based on Backrub motions, a type of conformational change frequently observed in ultra-high resolution Xray structures that accounts for correlated side-chain backbone movements. The fixed-backbone model performs reasonably well with an overall rmsd between computed and predicted side-chain order parameters of 0.26. Notably, including backbone flexibility leads to significant © 2008 Elsevier Ltd. All rights reserved. * Corresponding author: Kortemme@cgl.ucsf.edu. AUTHOR CONTRIBUTIONSGDF and TK conceived and designed the experiments. GDF and AJL performed the experiments. GDF, TK, and AJL analyzed the data and wrote the paper. CAS contributed reagents/materials/analysis tools. SUPPORTING INFORMATION AVAILABLEOne figure with population distributions of ubiquitin I13 and L15 chi 1 and chi 2. One table with detailed results from Model 1; one table with results from Models 2 and 3 for nonpolar methyl groups only; and one table with detailed parameter sensitivity results from Model 2.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access

Computational Optimization of Electric Fields for Improving Catalysis of a Designed Kemp Eliminase

“…For each sequence variant, we computed 10,000 trial moves to create each 25 uncorrelated and low energy backbone structures using backrub simulations provided within the Rosetta modeling software package [38][39][40] . To generate additional structural ensemble diversity, we used our recently developed Monte Carlo method (MC-SCE) 41 , for which we have performed extensive validation across ~60 proteins through comparison to high quality X-ray crystallography data and NMR experiments, to characterize the side chain structural ensemble on the 25 backbone structures for KE15.…”

Protein Structure Refinement Algorithms

“…Recently, a flexible backbone modeling algorithm was applied [18][19][20] . All these methods have attained some degree of success; however, none has emerged as clearly superior 13,14,16 .…”

“…All these methods have attained some degree of success; however, none has emerged as clearly superior 13,14,16 . Current methods require improvements in terms of computational efficiency and/or accuracy 18,[20][21][22][23] . For those approaches that generate an ensemble containing a large number of low-resolution structures, refinement algorithms must be very effi-!…”

“…Unlike many refinement methods that rely on stochastic techniques, GRID has been tuned to use a single pass search strategy and therefore requires less computational effort. We compared GRID to Backrub 27 , a flexible backbone modeling method that is able to predict most of the small conformational changes associated with point mutations 18,20 . GRID and Backrub were applied to a set of 10 high-resolution crystal structures from the PDB, and the algorithms were assessed in terms of energy improvement and computational efficiency.…”

“…Several groups have worked on developing force fields [6][7][8][9][10][11][12][13][14][15] , and creating better sampling methods [16][17][18][19][20][21][22] .…”