Deep graph learning of inter-protein contacts

Xie, Ziwei; Xu, Jinbo

doi:10.1101/2021.08.14.456342

Cited by 5 publications

(8 citation statements)

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“…In the suboptimal scenarios, the predicted interchain contacts together with the true tertiary structure of monomers in the bound state are used as input. We used two interchain contact predictors: DRCon (6Å version) to predict interchain contacts for the CASP_CAPRI homodimer dataset and Glinter (Xie and Xu 2021) for the Std_32 heterodimer dataset. In the most realistic scenario both the predicted interchain contacts and the unbound tertiary structures of monomers predicted by AlphaFold2 (Jumper et al 2021) are used as input.…”

Section: Resultsmentioning

confidence: 99%

“…Despite the notable progress in tertiary structure prediction, the prediction of quaternary structure of protein complexes is still in the early stage of development (Zeng et al 2018; J. Hou et al 2020; Quadir, Roy, Soltanikazemi, et al 2021; Quadir, Roy, Halfmann, et al 2021, 2; Yan and Huang 2021; Roy et al 2021; Xie and Xu 2021). The methods for quaternary structure prediction can be subdivided into two categories: ab-initio methods(Lyskov and Gray 2008; Pierce et al 2014; Quadir, Roy, Soltanikazemi, et al 2021; Evans et al 2021; Park et al 2021) and template-based methods(Tuncbag et al 2012; Guerler, Govindarajoo, and Zhang 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a gradient descent structure optimization method - GD (Soltanikazemi et al 2022) and a simulated annealing method - ConComplex (Quadir, Roy, Soltanikazemi, et al 2021) have been developed to build quaternary structures from predicted interchain residue-residue contacts and tertiary structures of protein chains. Due to the recent development of more accurate deep learning methods for interchain contact prediction such as DRCon(Roy et al 2021) and GLINTER(Xie and Xu 2021), these approaches can generate high-quality quaternary structures for some proteins. The latest significant advance in the field is the extension of the end-to-end deep learning method of predicting tertiary structures to quaternary structure prediction(Baek et al 2021; Jumper et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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A deep reinforcement learning approach to reconstructing quaternary structures of protein dimers through self-learning

Soltanikazemi

Roy

Quadir

et al. 2022

Preprint

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Predicted interchain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protein quaternary structures using predicted interchain contacts. Here, we present an agent-based self-learning method based on deep reinforcement learning (DRLComplex) to build protein complex structures using interchain contacts as distance constraints. We rigorously tested the DRLComplex on two standard datasets of homodimeric and heterodimeric dimers (the CASP-CAPRI homodimer dataset and Std_32 heterodimer dataset) using both true and predicted contacts. Utilizing true contacts as input, the DRLComplex achieved a high average TM-score of 0.9895 and 0.9881 and a low average interface RMSD (I_RMSD) of 0.2197 and 0.92 on the two datasets, respectively. When predicted contacts are used, the method achieves the TM-score of 0.73 and 0.76 for homodimers and heterodimers respectively. The accuracy of reconstructed quaternary structures depends on the accuracy of contact predictions. Compared with other optimization methods of reconstructing quaternary structures from interchain contacts, DRLComplex performs similarly to an advanced gradient descent method and better than a Markov Chain Monte Carlo simulation method and a simulated annealing-based method. The source code of DRLComplex is available at: https://github.com/jianlin-cheng/DRLComplex

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A deep reinforcement learning approach to reconstructing quaternary structures of protein dimers through self-learning

Soltanikazemi

Roy

Quadir

et al. 2022

Preprint

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“… Su et al , 2021 ; Tunyasuvunakool et al , 2021 ), which opens up new types of features to be included in DL methods for interface prediction (e.g. Dai and Bailey-Kellogg, 2021 ; Xie and Xu, 2021 ). These are really exciting developments, and e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the usefulness of predicted structures for interface prediction may be limited (e.g. Xie and Xu, 2021 ). Therefore, we aim to predict protein bindings of the mentioned interaction types at residue level, using only information related to protein sequence.…”

Section: Introductionmentioning

confidence: 99%

PIPENN: protein interface prediction from sequence with an ensemble of neural nets

et al. 2022

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Motivation The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly, and challenging task, while protein sequence data is ubiquitous. Consequently, many computational and machine learning approaches have been developed over the years to predict such interface residues from sequence. However, the effectiveness of different deep learning architectures and learning strategies for protein–protein, protein–nucleotide, and protein–small molecule interface prediction, has not yet been investigated in great detail. Therefore, we here explore the prediction of protein interface residues using six deep learning architectures and various learning strategies with sequence-derived input features. Results We constructed a large data set dubbed BioDL, comprising protein–protein interactions from the PDB, and DNA/RNA and small molecule interactions from the BioLip database. We also constructed six DL architectures, and evaluated them on the BioDL benchmarks. This shows that no single architecture performs best on all instances. An ensemble architecture, which combines all six architectures, does consistently achieve peak prediction accuracy. We confirmed these results on the published benchmark set by Zhang & Kurgan (ZK448), and on our own existing curated homo- and heteromeric protein interaction data set. Our PIPENN sequence-based ensemble predictor outperforms current state-of-the-art sequence-based protein interface predictors on ZK448 on all interaction types, achieving an AUC-ROC of 0.718 for protein–protein, 0.823 for protein–nucleotide and 0.842 for protein–small molecule. Availability Source code and data sets at https://github.com/ibivu/pipenn/

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Protein language model embedded geometric graphs power inter-protein contact prediction

Yan

2023

Preprint

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Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions (PPIs). Although significant improvement has been made in inter-protein contact prediction recently, there is still large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets shows that PLMGraph-Inter significantly outperforms three top inter-protein contact prediction methods, including DRN-1D2D_Inter, DeepHomo and GLINTER. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

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Deep graph learning of inter-protein contacts

Cited by 5 publications

References 100 publications

A deep reinforcement learning approach to reconstructing quaternary structures of protein dimers through self-learning

A deep reinforcement learning approach to reconstructing quaternary structures of protein dimers through self-learning

PIPENN: protein interface prediction from sequence with an ensemble of neural nets

Protein language model embedded geometric graphs power inter-protein contact prediction

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