Dockground Tool for Development and Benchmarking of Protein Docking Procedures

Kundrotas, Petras J.; Kotthoff, Ian; Choi, Sherman W; Copeland, Matthew M.; Vakser, Ilya A.

doi:10.1007/978-1-0716-0708-4_17

Cited by 8 publications

(10 citation statements)

References 42 publications

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“…While AF2 was trained to predict the structure of individual chains, it is possible that it can also be used to predict the structure of protein complexes, just as trRosetta, Raptor-X and RoseTTAFold. To examine this, we tested the ability of AF2 to fold and "dock" two benchmark sets -a set of proteins known to form oligomers (Ponstingl et al, 2000) and the Dockground 4.3 heterodimeric benchmark (Kundrotas et al, 2020).…”

Section: Af2 Based Prediction Of Protein Complex Structuresmentioning

confidence: 99%

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A structural biology community assessment of AlphaFold 2 applications

Akdel

Pires

et al. 2021

Preprint

120

166

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Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

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Section: Af2 Based Prediction Of Protein Complex Structuresmentioning

confidence: 99%

“…We next examined the Dockground 4.3 heterodimeric benchmark set (Kundrotas et al, 2020). We predicted complex structures using the DeepMind default dataset and the small BFD database.…”

Section: Af2 Based Prediction Of Protein Complex Structuresmentioning

confidence: 99%

A structural biology community assessment of AlphaFold 2 applications

Akdel

Pires

et al. 2021

Preprint

120

166

View full text Add to dashboard Cite

show abstract

“…The template-based docking was performed on PDB structures (1,792 chains) and modeled structures (3,598 chains) of individual proteins by the full structure alignment protocol, 25 using our most recent template library of 11,756 co-crystallized binary complexes from Dockground . 29 The target proteins were structurally aligned to the template monomers by TM-align. 30 Only alignments with target/template TM-scores 31 > 0.4 were used to build the docking models further scored by the combined scoring function.…”

Section: Resultsmentioning

confidence: 99%

GWYRE: A Resource for Mapping Variants onto Experimental and Modeled Structures of Human Protein Complexes

Malladi

Powell

David

et al. 2022

Journal of Molecular Biology

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“…The dataset of 3-6-mers was taken from the DOCKGROUND resource (dockground.compbio.ku.edu) [40] to analyse the relationship between accuracy and number of chains in complex using AlphaFold-multimer. This dataset consists of non-redundant PDB structures (available before October 2020) which have 3-6 protein chains (all longer than 30 amino acids) in both biological PDB units and do not have RNA or DNA.…”

Section: Non-redundant Complexes From the Pdb With 3-6 Chainsmentioning

confidence: 99%

Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

Bryant

Pozzati

Zhu

et al. 2022

Preprint

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AlphaFold and AlphaFold-multimer can predict the structure of single- and multiple chain proteins with very high accuracy. However, predicting protein complexes with more than a handful of chains is still unfeasible, as the accuracy rapidly decreases with the number of chains and the protein size is limited by the memory on a GPU. Nevertheless, it might be possible to predict the structure of large complexes starting from predictions of subcomponents. Here, we take a graph traversal approach to assemble 175 protein complexes with 10-30 chains using predictions of subcomponents. We compute paths through a complex graph constructed of subcomponents using Monte Carlo Tree Search and assemble these in a stepwise fashion. Using subcomponents predicted from all possible trimeric interactions, 88 complexes (50%) are assembled to completion. We create a scoring function, mpDockQ, that can distinguish if assemblies are complete and predict their accuracy. Selecting complete complexes with TM-score ≥0.9 at FPR 10% using mpDockQ results in 23 complexes with a median TM-score of 0.92. The complete assembly protocol, starting from the sequences, is freely available at: https://gitlab.com/patrickbryant1/molpc

show abstract

Dockground Tool for Development and Benchmarking of Protein Docking Procedures

Cited by 8 publications

References 42 publications

A structural biology community assessment of AlphaFold 2 applications

A structural biology community assessment of AlphaFold 2 applications

GWYRE: A Resource for Mapping Variants onto Experimental and Modeled Structures of Human Protein Complexes

Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search

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