中国物理B ›› 2020, Vol. 29 ›› Issue (10): 108707-.doi: 10.1088/1674-1056/abb659
Daiwen Sun(孙黛雯)1, Shijie Liu(刘世婕)1, Xinqi Gong(龚新奇)1,2,†()
收稿日期:
2020-06-29
修回日期:
2020-08-31
接受日期:
2020-09-09
出版日期:
2020-10-05
发布日期:
2020-10-05
通讯作者:
Xinqi Gong(龚新奇)
Daiwen Sun(孙黛雯)1, Shijie Liu(刘世婕)1, and Xinqi Gong(龚新奇)1,2,†
Received:
2020-06-29
Revised:
2020-08-31
Accepted:
2020-09-09
Online:
2020-10-05
Published:
2020-10-05
Contact:
†Corresponding author. E-mail: About author:
中图分类号: (Protein-protein interactions)
Daiwen Sun(孙黛雯), Shijie Liu(刘世婕), Xinqi Gong(龚新奇). [J]. 中国物理B, 2020, 29(10): 108707-.
Daiwen Sun(孙黛雯), Shijie Liu(刘世婕), and Xinqi Gong(龚新奇)†. Review of multimer protein–protein interaction complex topology and structure prediction[J]. Chin. Phys. B, 2020, 29(10): 108707-.
"
Optimization algorithms | Programs |
---|---|
Fast Fourier transformation | ZDOCK; GRAMM; DOT; SmoothDock; ClusPro; MolFit; FTDock; 3D-Dock; PIPER; pyDock; HDOCK; SDOCK; HEX; FRODOCK; InterEvDock; MDockPP; CoDockPP; HSYMDOCK; SAM |
Monte Carlo | RosettaDock; ICM-DOCK; HADDOCK; ATTRACT |
Genetic algorithm | DARWIN; Multi-LZerD; AutoDock |
"
Methods | Description | Advantages | Limits | ||
---|---|---|---|---|---|
Interface residue pair prediction | ComplexContact; RaptorX-Contact; RaptorX-Property; Gremlin; DNCON2; PSICOV; FreeContact; LSTM; LSTM with Graph Representation | Direct evolutionary coupling analysis (DCA), machine learning and deep learning methods | Interfacial residue pair prediction can help subsequent protein complex structure predictions, such as docking.Protein contact map prediction can help reconstruct the three-dimensional structure of protein complexes. | The accuracy of interface residues for prediction needs to be improved. | |
Protein structure prediction | Template-free | ZDOCK; GRAMM; DOT; SmoothDock; ClusPro; MolFit; FTDock; 3D-Dock; PIPER; pyDock; HDOCK; SDOCK; HEX; FRODOCK; InterEvDock; MDockPP; CoDockPP; HSYMDOCK; SAM; RosettaDock; ICM-DOCK; HADDOCK; ATTRACT; DARWIN; Multi-LZerD; AutoDock | The search strategies of these methods are mainly FFT, GA and MC. | Protein docking can give all possible complex structures, some of which can also dock Cn and Dn complexes. | Designing an effective scoring function to sort the docking structure remains to be further explored. |
Template-based | InterPreTS; Multimeric threading approach; M-Tasser; PISA; ProtCID | Using sequence or structure similarity to model protein complexes with known structures. | Template-based methods mainly reduce the possible structure by restricting the direction of protein binding. This method is more efficient than docking and can be applied to larger-scale protein complex prediction. | For proteins without a template, the structure of the complex cannot be predicted. | |
Protein complex prediction from PPI networks | Complex prediction based on PPI network clustering | MCODE; MCL; SPC; LCMA; SuperComplex; BN; CFinder; DPClus; IPCA; CMC; ClusterONE; HACO | The protein complex is part of a known PPI network, that is, the graph composed of protein complexes and their interactions is a subgraph of the PPI network. | Some of these methods only use the PPI network for clustering, and some use additional biological information, including structure, function, organization and co-evolution infornation, etc. | The proteins that may form complexes can only be picked out from the existing PPI network. |
Complex interaction link prediction from PPI network | GGA; HAC; ECT; RWR; MDS; Link-weighted PPI | Methods to predict actual links in the network include public neighbors-based methods and distance-based methods. | This type of method predicts possible protein--protein interactions based on existing network information. | Public neighbors-based methods have limited effect on sparse networks. |
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