ZRANK: Reranking protein docking predictions with an optimized energy function

By Brian G. Pierce, Zhiping Weng. Proteins: Structure, Function, and Bioinformatics, 2007

461 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1c5fbc7fa6114220456939c62333b910e8a0670e

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Citations in the corpus

Contacts-based prediction of binding affinity in protein–protein complexes (2015, background): “…2004 ] and PMF[ Su et al., 2009 ]) and the composite scoring functions reported by the CCHarPPI webserver [ Moal et al., 2015a , b ], such as ZRANK [ Pierce and Weng, 2007 ], ZRANK2 [ Pierce and Weng, 2008 ], RosettaDock [ Chaudhury et al., 2010 ], PyDock [ Cheng et al., 2007 ], FireDock […”
Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes (2014, unknown): “As for the original ZRANK implementation [36], we employed a downhill simplex to select weights for the terms of the ZRANK scoring function, in this case maximizing the average area under the receiver operating characteristic curve (AUC) of 20 TCR-pMHC docking test cases [11], distinguishing hit…”
The scoring of poses in protein-protein docking: current capabilities and future directions (2013, methodology): “ZRANK2 [58] d The reoptimised ZRANK scoring function.”
CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK (2011, unknown): “A scoring function called ZRANK has been developed by the ZDOCK group, with the goal to re-rank ZDOCK solutions [28].”
Protein Docking Model Evaluation by Graph Neural Networks (2020, methodology): “GNN-DOVE was compared with DOVE and five other existing structure model scoring methods, GOAP ( 49 ), ITScore ( 29 ), ZRANK ( 50 ), ZRANK2 ( 51 ), and IRAD ( 52 ).”
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants (2021, background): “Developments such as explicit side chain flexibility during docking searches [8], use of normal mode analysis to represent protein flexibility [9, 10], clustering [11, 12] or re-scoring [13-16] docking models to improve ranking of near-native models, and use of experimental data as restraints for do”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, result): “Similar success rates were reported previously for popular protein-protein docking scoring functions ZRANK (92) and integration of residue-and atom-based potentials for docking (93).”
MODBASE: a database of annotated comparative protein structure models and associated resources (2005, methodology): “When using SOAP-PP for scoring protein–protein docking models, a near-native model is within the top 10 scoring models in 52% of the PatchDock decoys (30), compared with 23 and 27% for the state-of-the-art ZRANK (31) and FireDock (32) scoring functions, respectively.”
Finding correct protein–protein docking models using ProQDock (2016, unknown): “In addition, ProQDock was combined with the external energy terms, ZRANK (Pierce and Weng, 2007) and ZRANK2 (Pierce and Weng, 2008) (ProQDockZ), using a linear weighted sum optimized on the CnM set.”
Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes (2020, background): “ZRANK is a physics-based scoring function that utilizes a weighted sum of seven terms accounting for electrostatic potential, van der Waals interactions, and desolvation energy at the protein-protein interface.(14) Figure 2 shows the effect PAUL has on the ranking performance of SOAP-PP and ZRANK fo”
A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes (2013, methodology): “Protein–protein docking has advanced greatly over the past two decades, spurred by improvements in scoring functions, computational efficiency, as well as community interaction via the ongoing CAPRI blind docking experiment.13 Improved conformational searching has provided the ability to predict str”