Integrating statistical pair potentials into protein complex prediction

By J. Mintseris, Brian G. Pierce, K. Wiehe, Robert Anderson, Rong Chen, Zhiping Weng. Proteins: Structure, Function, and Bioinformatics, 2007

333 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2f388df34889e00fd8f58e43eb7d349a7ff6fbc1

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Citations in the corpus

Updates to the integrated protein-protein interaction benchmarks: Docking benchmark version 5 and affinity benchmark version 2 (2015, methodology): “2 that uses the IFACE [53] scoring function and the advanced 3D convolution library [54].”
Templates are available to model nearly all complexes of structurally characterized proteins (2012, methodology): “Tem-plate-free docking methods rely on the geometric and chemical-physical complementarity of the protein surfaces (1), now often supplemented by statistical potentials (2, 3), and subject to a variety of constraints (4).”
Protein–protein docking by fast generalized Fourier transforms on 5D rotational manifolds (2016, unknown): “In later FFT based methods the scoring function has been expanded to include electrostatic and solvation terms [6, 7], and more recently structure-based interaction potentials [8, 9], substantially improving the accuracy of docked structures.”
The scoring of poses in protein-protein docking: current capabilities and future directions (2013, unknown): “AP_WENG [173] r An atomic contact potential.”
Protein–protein interaction and quaternary structure (2008, background): “…interactions (Keskin et al. 1998), but the tendency in recent years has been to develop potentials that are specifically designed for that purpose (Mintseris et al. 2007), and to complement the statistical approach with machine-learning procedures that can handle parameters not easily converted…”
CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK (2011, unknown): “Unlike HADDOCK, ZDOCK is optimized for such cases, containing features such as shape complementarity [30,31] and statistical contact potentials [32] that are absent in HADDOCK.”
Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes (2020, background): “are based on a probability distribution of defined spatial features (eg, interatomic distances and bond orientations) computed for a set of known protein complex structures.(15-18) Recently, several scoring functions have been developed using machine learning methods.”
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants (2021, background): “41,42 Remarkably, AlphaFold was able to generate models with acceptable or higher accuracy for approximately half (51”
Protein-protein docking: from interaction to interactome. (2014, unknown): “(10,11), and statistics-based propensities (12,13).”