scholarly journals Protein models docking benchmark 2

2015 ◽  
Vol 83 (5) ◽  
pp. 891-897 ◽  
Author(s):  
Ivan Anishchenko ◽  
Petras J. Kundrotas ◽  
Alexander V. Tuzikov ◽  
Ilya A. Vakser
Keyword(s):  
Protein Models ◽  
Docking Benchmark

scholarly journals Improved Docking of Protein Models by a Combination of Alphafold2 and ClusPro

2021 ◽  
Author(s):  
Usman Ghani ◽  
Israel Desta ◽  
Akhil Jindal ◽  
Omeir Khan ◽  
George Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Success Rate ◽  
Protein Docking ◽  
Additional Step ◽  
The Neural Network ◽  
Docking Program ◽  
Protein Models ◽  
Docking Benchmark ◽  
Benchmark Sets

AbstractIt has been demonstrated earlier that the neural network based program AlphaFold2 can be used to dock proteins given the two sequences separated by a gap as the input. The protocol presented here combines AlphaFold2 with the physics based docking program ClusPro. The monomers of the model generated by AlphaFold2 are separated, re-docked using ClusPro, and the resulting 10 models are refined by AlphaFold2. Finally, the five original AlphaFold2 models are added to the 10 AlphaFold2 refined ClusPro models, and the 15 models are ranked by their predicted aligned error (PAE) values obtained by AlphaFold2. The protocol is applied to two benchmark sets of complexes, the first based on the established protein-protein docking benchmark, and the second consisting of only structures released after May 2018, the cut-off date for training AlphaFold2. It is shown that the quality of the initial AlphaFold2 models improves with each additional step of the protocol. In particular, adding the AlphaFold2 refined ClusPro models to the AlphaFold2 models increases the success rate by 23% in the top 5 predictions, whereas considering the 10 models obtained by the combined protocol increases the success rate to close to 40%. The improvement is similar for the second benchmark that includes only complexes distinct from the proteins used for training the neural network.

scholarly journals Docking Benchmark Set of Protein Models

2014 ◽  
Vol 106 (2) ◽  
pp. 656a
Author(s):  
Ivan Anishchanka ◽  
Petras J. Kundrotas ◽  
Alexander V. Tuzikov ◽  
Ilya A. Vakser
Keyword(s):  
Protein Models ◽  
Docking Benchmark

scholarly journals Dockground scoring benchmarks for protein docking

2021 ◽  
Author(s):  
Ian Kotthoff ◽  
Petras J. Kundrotas ◽  
Ilya A. Vakser
Keyword(s):  
Protein Complexes ◽  
Protein Docking ◽  
Structural Quality ◽  
Scoring Functions ◽  
Model Benchmark ◽  
User Friendly ◽  
Development And Validation ◽  
Protein Models ◽  
Docking Benchmark

AbstractProtein docking protocols typically involve global docking scan, followed by re-ranking of the scan predictions by more accurate scoring functions that are either computationally too expensive or algorithmically impossible to include in the global scan. Development and validation of scoring methodologies are often performed on scoring benchmark sets (docking decoys) which offer concise and nonredundant representation of the global docking scan output for a large and diverse set of protein-protein complexes. Two such protein-protein scoring benchmarks were built for the Dockground resource, which contains various datasets for the development and testing of protein docking methodologies. One set was generated based on the Dockground unbound docking benchmark 4, and the other based on protein models from the Dockground model-model benchmark 2. The docking decoys were designed to reflect the reality of the real-case docking applications (e.g., correct docking predictions defined as near-native rather than native structures), and to minimize applicability of approaches not directly related to the development of scoring functions (reducing clustering of predictions in the binding funnel and disparity in structural quality of the near-native and non-native matches). The sets were further characterized by the source organism and the function of the protein-protein complexes. The sets, freely available to the research community on the Dockground webpage, present a unique, user-friendly resource for the developing and testing of protein-protein scoring approaches.

scholarly journals Docking Benchmark Set of Protein Models

2011 ◽  
Vol 100 (3) ◽  
pp. 320a
Author(s):  
Petras J. Kundrotas ◽  
Ivan Anishchenko ◽  
Alexander V. Tuzikov ◽  
Ilya A. Vakser
Keyword(s):  
Protein Models ◽  
Docking Benchmark

Synthetic heme protein models that function in aqueous solution

2021 ◽  
Vol 57 (2) ◽  
pp. 148-173
Author(s):  
Hiroaki Kitagishi ◽  
Koji Kano
Keyword(s):  
Aqueous Solution ◽  
Heme Protein ◽  
Cyclodextrin Complexes ◽  
Protein Models

Supramolecular porphyrin–cyclodextrin complexes act as biomimetic heme protein models in aqueous solution.

scholarly journals A method for evaluating the structural quality of protein models by using higher-order  -  pairs scoring

2006 ◽  
Vol 103 (12) ◽  
pp. 4428-4432 ◽  
Author(s):  
G. E. Sims ◽  
S.-H. Kim
Keyword(s):  
Higher Order ◽  
Structural Quality ◽  
Protein Models

scholarly journals Automated method to differentiate between native and mirror protein models obtained from contact maps

PLoS ONE ◽  
2018 ◽  
Vol 13 (5) ◽  
pp. e0196993
Author(s):  
Monika Kurczynska ◽  
Malgorzata Kotulska
Keyword(s):  
Contact Maps ◽  
Automated Method ◽  
Protein Models

scholarly journals BCL::Score—Knowledge Based Energy Potentials for Ranking Protein Models Represented by Idealized Secondary Structure Elements

PLoS ONE ◽  
2012 ◽  
Vol 7 (11) ◽  
pp. e49242 ◽  
Author(s):  
Nils Woetzel ◽  
Mert Karakaş ◽  
Rene Staritzbichler ◽  
Ralf Müller ◽  
Brian E. Weiner ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Knowledge Based ◽  
Protein Models

Protein models are literally on the tips of students’ tongues

2021 ◽  
pp. 7-7
Author(s):  
Sam Lemonick
Keyword(s):  
Protein Models
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