Methods in Enzymology: “Flexible backbone sampling methods to model and design protein alternative conformations”

By Noah Ollikainen, Colin A. Smith, James S. Fraser, Tanja Kortemme. 2013

43 citations recorded by the source corpus.

Sampling alternative conformations is key to understanding how proteins work and engineering them for new functions. However, accurately characterizing and modeling protein conformational ensembles remains experimentally and computationally challenging. These challenges must be met before protein conformational heterogeneity can be exploited in protein engineering and design. Here, as a stepping stone, we describe methods to detect alternative conformations in proteins and strategies to model these near-native conformational changes based on backrub-type Monte Carlo moves in Rosetta. We illustrate how Rosetta simulations that apply backrub moves improve modeling of point mutant side chain conformations, native side chain conformational heterogeneity, functional conformational changes, tolerated sequence space, protein interaction specificity, and amino acid co-variation across protein-protein interfaces. We include relevant Rosetta command lines and RosettaScripts to encourage the application of these types of simulations to other systems. Our work highlights that critical scoring and sampling improvements will be necessary to approximate conformational landscapes. Challenges for the future development of these methods include modeling conformational changes that propagate away from designed mutation sites and modulating backbone flexibility to predictively design functionally important conformational heterogeneity.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Sampling alternative conformational states of transporters and receptors with AlphaFold2 (2022, methodology): “Moreover, this approach showed limited success when applied to transporters whose structures were used to train AF2, hinting at the possibility that traditional methods may still be required to capture alternative conformers ( Crawley et al., 2011 ; Ollikainen et al., 2013 ).”
Expanding the space of protein geometries by computational design of de novo fold families (2020, methodology): “This scale of variation exceeds the backbone changes generated by existing flexible backbone design methods 23,24 that are typically smaller than 2Å RMSD.”
A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design (2015, background): “Until the amount of available data of this nature greatly increases, other datasets, in particular the diversity of sequences present in naturally evolved protein families or selected in large-scale experimental screens, can provide informative benchmarks that have been used in the past to assess an”
Recent advances in de novo protein design: Principles, methods, and applications (2021, methodology): “Typically, methods that exploit backbone ﬂ exibility or use backbone ensembles outperform the ﬁ xed backbone design (96, 97).”