MEGADOCK 4.0: an ultra–high-performance protein–protein docking software for heterogeneous supercomputers

Ohue, Masahito; Shimoda, Takehiro; Suzuki, Shuji; Matsuzaki, Yasushi; Ishida, Takashi; Akiyama, Yutaka

doi:10.1093/bioinformatics/btu532

Cited by 83 publications

(60 citation statements)

References 17 publications

(20 reference statements)

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“…Generation of the structural model was performed using MEGADOCK 4.0.2 [27,28]. The top three structural models of the docking score were output for each rotation angle, and the number of rotation angles was 3,600.…”

Section: Model Generationmentioning

confidence: 99%

Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces

2020

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Protein functions can be predicted based on their three-dimensional structures. However, many multidomain proteins have unstable structures, making it difficult to determine the whole structure in biological experiments. Additionally, multidomain proteins are often decomposed and identified based on their domains, with the structure of each domain often found in public databases. Recent studies have advanced structure prediction methods of multidomain proteins through computational analysis. In existing methods, proteins that serve as templates are used for three-dimensional structure prediction. However, when no protein template is available, the accuracy of the prediction is decreased. This study was conducted to predict the structures of multidomain proteins without the need for whole structure templates.We improved structure prediction methods by performing rigid-body docking from the structure of each domain and reranking a structure closer to the correct structure to have a higher value. In the proposed method, the score for the domain-domain interaction obtained without a structural template of the multidomain protein and score for the three-dimensional structure obtained during docking calculation were newly incorporated into the score function. We successfully predicted the structures of 50 of 55 multidomain proteins examined in the test dataset.Interaction residue pair information of the proteinprotein complex interface contributes to domain reorganizations even when a structural template for a multidomain protein cannot be obtained. This approach may be useful for predicting the structures of multidomain proteins with important biochemical functions.Key words: multidomain protein, protein tertiary structure prediction, interaction residue pair, rigid-body docking, conformations reranking More than half of prokaryote and eukaryote genes produce multidomain proteins with multiple partial structures known as protein domains [1,2]. Each protein domain is folded into a tight and stable structure that is conserved among different multidomain proteins [3].Because protein functions differ depending on theirWe have developed a novel multidomain protein structure prediction method named PINE without the need for whole multidomain structure templates. The score for the domain-domain interaction obtained without a structural template of the multidomain protein and score for the three-dimensional structure obtained during protein docking calculation were newly incorporated into the score function. We successfully predicted the structures of 50 of 55 multidomain proteins examined in the test dataset. Interaction residue pair information of the proteinprotein complex interface contributes to domain reorganizations even when a structural template for a multidomain protein cannot be obtained.

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Section: Model Generationmentioning

confidence: 99%

Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces

2020

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“…Step 1: Calculating the Decoy Profile I generated five peptide conformations from the input peptide sequence using the PEP-FOLD server [27]. The input protein structure and modeled peptide structures were then docked using MEGADOCK rigid-body docking software [20] to sample numerous peptide docking poses simultaneously. Specifically, I sampled 3,600 poses for each peptide model, and a total of 3,600 × 5 = 18,000 poses were generated using MEGADOCK.…”

Section: Protein-peptide Docking Re-rankingmentioning

confidence: 99%

“…By considering the structure of a protein as rigid, structural sampling can be achieved over the entire space with a short calculation time. The latest tools (MEGADOCK 4.0 [20] and Hex [21]) are capable of evaluating trillions of conformations in seconds on GPU accelerators, and protein-protein docking tools are used casually. Although the conformations predicted with these tools tend to include many false-positive complexes, the so-called decoy conformations that result from a rigid-body search can be used in reevaluating the predicted conformations [22,23,24] or provide information for the search direction in redocking [25].…”

Section: Introductionmentioning

confidence: 99%

Re-ranking of Computational Protein–Peptide Docking Solutions With Amino Acid Profiles of Rigid-Body Docking Results

Ohue

2020

Preprint

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Protein-peptide interactions, in which one partner is a globular protein and the other is a flexible linear peptide, are important for understanding cellular processes and regulatory pathways, and are therefore targets for drug discovery. In this study, I combined rigid-body protein-protein docking software (MEGADOCK) and global flexible protein-peptide docking software (CABS-dock) to establish a reranking method with amino acid contact profiles using rigid-body sampling decoys. I demonstrate that the correct complex structure cannot be predicted (< 10 Å peptide RMSD) using the current version of CABS-dock alone. However, my newly proposed re-ranking method based on the amino acid contact profile using rigid-body search results (designated the decoy profile) demonstrated the possibility of improvement of predictions. Adoption of my proposed method along with continuous efforts for effective computational modeling of protein-peptide interactions can provide useful information to understand complex biological processes in molecular detail and modulate protein-protein interactions in disease treatment.

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“…The protein shown in Figure 7 is fibronectin type domain of integrin β4, which makes a complex with plectin's actinbinding domain (Song et al, 2015). Thus, we applied a protein-protein docking to the modeled structures and a ligand structure from the complex structure (PDB ID: 4Q58, Chain: A), using MEGADOCK 4.0 (Ohue et al, 2014) with the default settings, to check the influence of model accuracy. Figure 8 shows the docking results using the model from the proposed method and that from HHsearch.…”

Section: Impact Of Model Accuracy Improvement For Protein Function Esmentioning

confidence: 99%

Sequence alignment using machine learning for accurate template-based protein structure prediction

Makigaki

Ishida

2019

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Motivation:Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments. Result: In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. It is difficult to directly predict sequence alignments using machine learning. Thus, when calculating sequence alignments, instead of a fixed substitution matrix , this method dynamically predicts a substitution score from the trained model. We evaluate our method by carefully splitting the training and test datasets and comparing the predicted structure's accuracy with that of stateof-the-art methods. Our method generates more accurate tertiary structure models than those produced from alignments obtained by other methods. Availability and Implementation: https://

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MEGADOCK 4.0: an ultra–high-performance protein–protein docking software for heterogeneous supercomputers

Cited by 83 publications

References 17 publications

Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces

Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces

Re-ranking of Computational Protein–Peptide Docking Solutions With Amino Acid Profiles of Rigid-Body Docking Results

Sequence alignment using machine learning for accurate template-based protein structure prediction

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