Role of conformational sampling in computing mutation-induced changes in protein structure and stability

By Elizabeth H. Kellogg, Andrew Leaver-Fay, David Baker. 2010

640 citations recorded by the source corpus.

The prediction of changes in protein stability and structure resulting from single amino acid substitutions is both a fundamental test of macromolecular modeling methodology and an important current problem as high throughput sequencing reveals sequence polymorphisms at an increasing rate. In principle, given the structure of a wild‐type protein and a point mutation whose effects are to be predicted, an accurate method should recapitulate both the structural changes and the change in the folding‐free energy. Here, we explore the performance of protocols which sample an increasing diversity of conformations. We find that surprisingly similar performances in predicting changes in stability are achieved using protocols that involve very different amounts of conformational sampling, provided that the resolution of the force field is matched to the resolution of the sampling method. Methods involving backbone sampling can in some cases closely recapitulate the structural changes accompanying mutations but not surprisingly tend to do more harm than good in cases where structural changes are negligible. Analysis of the outliers in the stability change calculations suggests areas needing particular improvement; these include the balance between desolvation and the formation of favorable buried polar interactions, and unfolded state modeling. Proteins 2011. © 2010 Wiley‐Liss, Inc.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Scientific Benchmarks for Guiding Macromolecular Energy Function Improvement (2013, methodology): “This feature was used to scale the Score12 terms by a linear factor but otherwise keep them fixed to optimize the DDG of the mutation loss function (Kellogg et al., 2011).”
The Rosetta all-atom energy function for macromolecular modeling and design (2017, background): “…Rotamer Recovery Percentage of native rotamers recovered after full repacking [50] ∆∆ G Prediction Prediction of free energy changes upon mutation [97] Loop Modeling Prediction of loop conformations [98] High-resolution refinement Discrimination of native-like decoys upon refinement of ab initio…”
Design of Nanoparticulate Group 2 Influenza Virus Hemagglutinin Stem Antigens That Activate Unmutated Ancestor B Cell Receptors of Broadly Neutralizing Antibody Lineages (2018, positive): “The changes in free energy from point mutations were estimated using the program DDG_MONOMER (26) from the ROSETTA suite.”
Relationship between protein thermodynamic constraints and variation of evolutionary rates among sites (2014, methodology): “Shown are the correlation coefficients of measured rates with rates predicted using the stability model with FoldX ΔΔ G values (y axis) vs. Rosetta ΔΔ G values (x axis) for five proteins.”
SDM: a server for predicting effects of mutations on protein stability (2017, methodology): “Structure-based methods use either machine learning techniques (9– 16) or potential-energy-based approaches to predict the impacts of mutations (17–19).”
Adaptation in protein fitness landscapes is facilitated by indirect paths (2016, methodology): “The DDG prediction was performed by the ddg monomer application in Rosetta software (Das and Baker, 2008) using the parameters from row 16 of Table I in Kellogg et al.”
Pi-Pi contacts are an overlooked protein feature relevant to phase separation (2018, methodology): “F), Analysis of the relationship between the energetic effects of point mutations (DDG) and pi-contacts for experimental DDGs (blue bars) and DDGs predicted by simulation against the FOLDX force field (Schymkowitz et al., 2005) (C,E) and Rosetta (D,F).”
Improved Modeling of Side-Chain–Base Interactions and Plasticity in Protein–DNA Interface Design (2012, methodology): “There are a number of possible methods to potentially adapt to provide an alternative method of DNA movement that is less extreme than the fragment insertion protocol tested here.(24,44) Both the loss in recovery when using the DNA-Rebuild method and the comparisons to experimental data indicate tha”
mCSM: predicting the effects of mutations in proteins using graph-based signatures (2013, background): “Received on April 18, 2013; revised on November 12, 2013; accepted on November 21, 2013”