Data driven flexible backbone protein design

By Mark G. F. Sun, Philip M. Kim. 2017

14 citations recorded by the source corpus.

Protein design remains an important problem in computational structural biology. Current computational protein design methods largely use physics-based methods, which make use of information from a single protein structure. This is despite the fact that multiple structures of many protein folds are now readily available in the PDB. While ensemble protein design methods can use multiple protein structures, they treat each structure independently. Here, we introduce a flexible backbone strategy, FlexiBaL-GP, which learns global protein backbone movements directly from multiple protein structures. FlexiBaL-GP uses the machine learning method of Gaussian Process Latent Variable Models to learn a lower dimensional representation of the protein coordinates that best represent backbone movements. These learned backbone movements are used to explore alternative protein backbones, while engineering a protein within a parallel tempered MCMC framework. Using the human ubiquitin–USP21 complex as a model we demonstrate that our design strategy outperforms current strategies for the interface design task of identifying tight binding ubiquitin variants for USP21.

Subfields: Molecular Biology, Spectroscopy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Alexey Strokach
2. Carles Corbi-Verge
3. David Becerra
4. Albert Perez-Riba
5. Philip M. Kim

In Spectroscopy

1. Carles Corbi-Verge
2. Alexey Strokach
3. David Becerra
4. Albert Perez-Riba
5. Philip M. Kim

Citations in the corpus

• Fast and flexible design of novel proteins using graph neural networks (2019, methodology): “Conventionally, a sampling technique such as Markov-chain Monte Carlo is used to generate sequences optimized with respect to a force-field or statistical potential ( 3 – 5 ).”