Efficient flexible backbone protein–protein docking for challenging targets

Marze, Nicholas A.; Burman, Shourya S. Roy; Sheffler, William; Gray, Jeffrey J.

doi:10.1093/bioinformatics/bty355

Cited by 155 publications

(150 citation statements)

References 54 publications

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“…We modeled backbone flexibility by incorporating a pre-generated ensemble of backbone conformations during docking. With RosettaDock 4.0, 8 we sampled over fifty conformations for each partner to successfully model T122. Despite having an efficient backbone sampling algorithm, we failed to model T131 and T132 due to the absence of conformations where the interacting loops were in near-bound conformation.…”

Section: Discussionmentioning

confidence: 99%

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI Rounds 37–45

Burman

Nance

Jeliazkov

et al. 2019

Preprint

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CAPRI Rounds 37 through 45 introduced larger complexes, new macromolecules, and multi-stage assemblies. For these rounds, we used and expanded docking methods in Rosetta to model 23 target complexes. We successfully predicted 14 target complexes and recognized and refined nearnative models generated by other groups for two further targets. Notably, for targets T110 and T136, we achieved the closest prediction of any CAPRI participant. We created several innovative approaches during these rounds. Since Round 39 (target 122), we have used the new RosettaDock 4.0, which has a revamped coarse-grained energy function and the ability to perform conformer selection during docking with hundreds of pre-generated protein backbones. Ten of the complexes had some degree of symmetry in their interactions, so we tested Rosetta SymDock, realized its shortcomings, and developed the next-generation symmetric docking protocol, SymDock2, which includes docking of multiple backbones and induced-fit refinement. Since the last CAPRI assessment, we also developed methods for modeling and designing carbohydrates in Rosetta, and we used them to successfully model oligosaccharide-protein complexes in Round 41. While the results were broadly encouraging, they also highlighted the pressing need to invest in (1) flexible docking algorithms with the ability to model loop and linker motions and in (2) new sampling and scoring methods for oligosaccharide-protein interactions.

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Section: Discussionmentioning

confidence: 99%

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI Rounds 37–45

Burman

Nance

Jeliazkov

et al. 2019

Preprint

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“…Despite these efforts, a generalizable model selection strategy that captures high-level features such as topology, and secondary structure organization has yet to be developed. Previously reported methods have shown that recurring structural features (motifs) can be used to accelerate conformational searches and assist in structure evaluation (30,41,42). We have developed a novel approach that uses a CNN to capture and quantify such features.…”

Section: Discussionmentioning

confidence: 99%

Multi-Scale Structural Analysis of Proteins by Deep Semantic Segmentation

Eguchi

Huang

2018

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Recent advancements in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds, and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation -a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structural quality assessment. We represent protein structures as 2D α-carbon distance matrices ("contact maps"), and train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model performs exceptionally well, achieving a per-residue accuracy of 90.8% on the test set (95.0% average accuracy over all classes; 87.8% average within-structure accuracy). The unique aspect of our classifier is that it encodes sequence agnostic residue environments and can assess structural quality as quantitative probabilities. We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design.space, an enormous amount of sampling is still required to find the lengths, types (e.g. helix, betasheet) and order of secondary structure elements (here on, "topologies") that result in viable structures. The process of identifying successful models and topologies currently relies on a combination of scoring functions, hydrogen-bonding patterns, and other discernible regularities to screen for models that satisfy the desired criteria (13)(14)(15)(16)(17)(18)(19). However, such heuristics are often subjective and chosen ad hoc, and there are currently no unbiased, generalizable methods that can perform automated structure selection based on the overall organization of a protein.Meanwhile in the field of computer vision, convolutional neural networks (CNNs) have revolutionized pattern-recognition tasks ranging from facial recognition(20) to object detection (21). In applications to proteins, several groups have used 1D and 3D CNNs to process protein sequence and structure data, performing tasks such as domain prediction (22) and mutation stability evaluation(23). Nonetheless, several limitations have prevented the use of these models in protein design. In the case of 1D CNNs, low-dimensionality often results in loss of important features needed to describe realistic structures. In the case of 3D CNNs, model sizes (...

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“…This is especially notable because transmembrane membrane helices are notorious for kinks and curvature (50). Programs for incorporating backbone flexibility have emerged for soluble proteins (51,52) and these developments will soon translate to membrane proteins. Another challenge is incorporating the effect of bilayer deformations induced by the protein that influence the landscape of available protein-protein complex conformations (53).…”

Section: Challenges For Modeling Of Protein-protein Interactions In Tmentioning

confidence: 99%

Protein Docking and Steered Molecular Dynamics Reveal Alternative Regulatory Sites on the SERCA Calcium Transporter

Alford

Smolin

Young

et al. 2019

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The transport activity of the calcium ATPase SERCA is modulated by an inhibitory interaction with a 52-residue transmembrane peptide, phospholamban (PLB). Biochemical and structural studies have revealed the primary inhibitory site on SERCA, but PLB has been hypothesized to interact with alternative sites on SERCA that are distinct from the inhibitory site. The present study was undertaken to test these hypotheses and explore structural determinants of SERCA regulation by PLB. Steered molecular dynamics (SMD) and membrane protein-protein docking experiments were performed to investigate the apparent affinity of PLB interactions with candidate sites on SERCA. We modeled the relative binding of PLB to several different conformations of SERCA, representing different enzymatic states sampled during the calcium transport catalytic cycle. Overall, the SMD and docking experiments suggest that the canonical binding site is preferred, but also provide evidence for alternative sites that are favorable for certain conformational states of SERCA.

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Efficient flexible backbone protein–protein docking for challenging targets

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 155 publications

References 54 publications

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI Rounds 37–45

Novel sampling strategies and a coarse-grained score function for docking homomers, flexible heteromers, and oligosaccharides using Rosetta in CAPRI Rounds 37–45

Multi-Scale Structural Analysis of Proteins by Deep Semantic Segmentation

Protein Docking and Steered Molecular Dynamics Reveal Alternative Regulatory Sites on the SERCA Calcium Transporter

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