Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets

By Nicholas A. Marze, Shourya S. Roy Burman, W. Sheffler, Jeffrey J. Gray. 2017

158 citations recorded by the source corpus.

Computational prediction of protein-protein complex structures facilitates a fundamental understanding of biological mechanisms and enables therapeutics design. Binding-induced conformational changes challenge all current computational docking algorithms by exponentially increasing the conformational space to be explored. To restrict this search to relevant space, some computational docking algorithms exploit the inherent flexibility of the protein monomers to simulate conformational selection from pre-generated ensembles. As the ensemble size expands with increased protein flexibility, these methods struggle with efficiency and high false positive rates. Here, we develop and benchmark a method that efficiently samples large conformational ensembles of flexible proteins and docks them using a novel, six-dimensional, coarse-grained score function. A strong discriminative ability allows an eight-fold higher enrichment of nearnative candidate structures in the coarse-grained phase compared to a previous method. Further, the method adapts to the diversity of backbone conformations in the ensemble by modulating sampling rates. It samples 100 conformations each of the ligand and the receptor backbone while increasing computational time by only 20–80%. In a benchmark set of 88 proteins of varying degrees of flexibility, the expected success rate for blind predictions after resampling is 77% for rigid complexes, 49% for moderately flexible complexes, and 31% for highly flexible complexes. These success rates on flexible complexes are a substantial step forward from all existing methods. Additionally, for highly flexible proteins, we demonstrate that when a suitable conformer generation method exists, RosettaDock 4.0 can dock the complex successfully. Significance Predicting binding-induced conformational plasticity in protein backbones remains a principal challenge in computational protein–protein docking. To date, there are no methods that can reliably dock proteins that undergo more than 1 Å root-mean-squared-deviation of the backbones of the interface residues upon binding. Here, we present a method that samples backbone motions and scores conformations rapidly, obtaining–for the first time–successful docking of nearly 50% of flexible target complexes with backbone conformational change up to 2.2 Å RMSD. This method will be applicable to a broader range of protein docking problems, which in turn will help us understand biomolecular assembly and protein function.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

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

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

Flexible backbone assembly and refinement of symmetrical homomeric complexes (2018, methodology): “We previously developed MDS for docking heterodimeric complexes, and it greatly increased the number of conformations with near-native interfaces after a coarse-grained search (Marze et al., 2018).”
Three decades of nucleic acid aptamer technologies: Lessons learned, progress and opportunities on aptamer development. (2019, background): “In recent years, various Cell-SELEX derivatives such as fluorescence-activated cell sorter (FACS)-SELEX (Mayer et al., 2010), 3D Cell-SELEX (Souza et al.”
Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes (2020, methodology): “Despite the availability of many software packages designed for this purpose, 3-11 identifying accurate models out of a large set of candidate models has proven to be a major bottleneck for successful protein docking.”